diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
index 02bfc0a4..c7e01de4 100644
--- a/.github/workflows/docs.yml
+++ b/.github/workflows/docs.yml
@@ -1,11 +1,23 @@
-name: Build Docs
+name: Build Documentation
-# We don't want pushes (or PRs) to gh-pages to kick anything off
on:
+ workflow_dispatch:
push:
branches: [ main ]
+ paths:
+ - tests/*
+ - hydromt_fiat/*
+ - docs/*
+ - examples/*
+ - pyproject.toml
pull_request:
branches: [ main ]
+ paths:
+ - tests/*
+ - hydromt_fiat/*
+ - docs/*
+ - examples/*
+ - pyproject.toml
jobs:
# Build docs on Linux
@@ -18,45 +30,38 @@ jobs:
run:
shell: bash -l {0}
steps:
- - name: Checkout source
- uses: actions/checkout@v2
-
- # - name: Get tags
- # run: git fetch --depth=1 origin +refs/tags/*:refs/tags/*
+ - name: Checkout code
+ uses: actions/checkout@v3
- - name: Setup environment
+ - name: Setup Mambaforge
uses: conda-incubator/setup-miniconda@v2
with:
- python-version: "3.9"
- mamba-version: "*"
- channels: conda-forge,defaults
- channel-priority: true
- environment-file: envs/hydromt-fiat-dev.yml
- activate-environment: hydromt-fiat-dev
+ python-version: "3.10"
+ miniforge-variant: Mambaforge
+ miniforge-version: latest
+ channels: conda-forge
+ use-mamba: true
- - name: Install hydromt-fiat
- run: flit install
+ - name: Install and activate environment
+ run: |
+ mamba env create -f envs/hydromt-fiat-docs.yml
+ conda activate hydromt-fiat-docs
+ pip install .
- # Move examples folder before make html for nbsphinx
- - name: Build docs
+ - name: Generate docs
+ if: ${{ github.event_name != 'pull_request' && !github.event.act }}
run: |
- mv examples docs/_examples
- pushd docs
- make html
- popd
- # This overrides the version "dev" with the proper version if we're building off a
- # branch that's not main (which is confined to n.nn.x above) or on a tag.
- - name: Set doc version
- if: ${{ github.event_name != 'push' || !contains(github.ref, 'main') }}
- run: echo "DOC_VERSION=$(python -c 'from hydromt_fiat import __version__ as v; print("dev" if "dev" in v else "v"+v.replace(".dev",""))')" >> $GITHUB_ENV
+ export PATH=/usr/share/miniconda3/bin:$PATH
+ PYTHONPYCACHEPREFIX=~/pycache mamba run -n hydromt-fiat-docs sphinx-build -M html ./docs ./docs/_build
+ echo "DOC_VERSION=$(mamba run -n hydromt-fiat-docs python -c 'from hydromt_fiat import __version__ as v; print("dev" if "dev" in v else "v"+v.replace(".dev",""))')" >> $GITHUB_ENV
- name: Upload to GitHub Pages
- if: ${{ github.event_name != 'pull_request'}}
- uses: peaceiris/actions-gh-pages@v3.8.0
+ if: ${{ github.event_name != 'pull_request' && !github.event.act }}
+ uses: peaceiris/actions-gh-pages@v3.9.3
with:
github_token: ${{ secrets.GITHUB_TOKEN }}
publish_dir: ./docs/_build/html
- exclude_assets: '.buildinfo,_sources/*'
+ exclude_assets: .buildinfo,_sources/*,_examples/*.ipynb
destination_dir: ./${{ env.DOC_VERSION }}
keep_files: false
- full_commit_message: Deploy ${{ env.DOC_VERSION }} to GitHub Pages
\ No newline at end of file
+ full_commit_message: Deploy ${{ env.DOC_VERSION }} to GitHub Pages
diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
deleted file mode 100644
index 2935b3aa..00000000
--- a/.github/workflows/tests.yml
+++ /dev/null
@@ -1,47 +0,0 @@
-name: Tests
-
-on:
- push:
- branches: [ main ]
- pull_request:
- branches: [ main ]
-
-jobs:
- Test:
- name: ${{ matrix.os }} - py${{ matrix.python-version }}
- runs-on: ${{ matrix.os }}
- defaults:
- run:
- shell: bash -l {0}
- strategy:
- fail-fast: false
- matrix:
- os: ["ubuntu-latest"] #, "macos-latest", "windows-latest"]
- python-version: ["3.10"]
-
- steps:
- - uses: actions/checkout@v2
- - uses: conda-incubator/setup-miniconda@v2
- with:
- python-version: ${{ matrix.python-version }}
- mamba-version: "*"
- channels: conda-forge,defaults
- channel-priority: true
- environment-file: envs/hydromt-fiat-dev.yml
- activate-environment: hydromt-fiat-dev
-
- - name: Conda info
- run: |
- conda info
- conda list
-
- - name: Install hydromt_fiat
- run: flit install
-
- - name: Test
- run: python -m pytest --verbose --cov=hydromt_fiat --cov-report xml
-
- - name: Check style
- run: black --check .
-
- - uses: codecov/codecov-action@v1
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
index 581052b0..faaa1226 100644
--- a/.gitignore
+++ b/.gitignore
@@ -119,4 +119,26 @@ venv.bak/
/examples/data/miami_dade_landuse.gpkg
cache
-/examples/FIAT_database
\ No newline at end of file
+/examples/FIAT_database
+hydromt_fiat/data/damage_functions/flooding/AllDDF_HAZUS_fractions.xlsx
+hydromt_fiat/data/damage_functions/flooding/Hazus_IWR_curves.csv
+hydromt_fiat/data/damage_functions/flooding/Hazus_IWR_curves.xlsx
+
+# notebooks
+/examples/aggregation_zones_example_files/
+/examples/data/aggregation_zones/aggregation_zones_test1/
+/examples/data/aggregation_zones/aggregation_zones_test2/
+examples/aggregation_zones_example.html
+examples/aggregation_zones_example_files/
+examples/data/road_network/FIAT model/
+examples/data/global_OSM_JRC/fiat_model/
+examples/data/update_ground_floor_height/test_update_ground_floor_height_points/
+examples/data/update_ground_floor_height/test_update_ground_floor_height_polygons/
+examples/data/building_footprints/fiat_model_bfs/
+examples/data/aggregation_zones/output/
+examples/data/setup_exposure_buildings_with_dem_data/test_vulnerability_and_exposure_NSI_with_dem_data/
+examples/data/setup_new_composite_area/test_setup_new_composite_area_datum/
+examples/data/setup_new_composite_area/test_setup_new_composite_area_elevation/
+examples/data/setup_new_composite_area/test_setup_new_composite_area_geom/
+examples/data/update_max_potential_damage/test_update_max_potential_damage_points/
+examples/data/update_max_potential_damage/test_update_max_potential_damage_polygons/
\ No newline at end of file
diff --git a/LICENSE b/LICENSE
index e72bfdda..f288702d 100644
--- a/LICENSE
+++ b/LICENSE
@@ -671,4 +671,4 @@ into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
-.
\ No newline at end of file
+.
diff --git a/README.rst b/README.rst
index 0f390f08..5693233a 100644
--- a/README.rst
+++ b/README.rst
@@ -1,5 +1,5 @@
-HydroMT-FIAT: Delft-FIAT plugin for HydroMT
-#########################################
+HydroMT-FIAT: HydroMT plugin for Delft-FIAT
+############################################
.. image:: https://codecov.io/gh/Deltares/hydromt_fiat/branch/main/graph/badge.svg
:target: https://codecov.io/gh/Deltares/hydromt_fiat
@@ -20,7 +20,7 @@ HydroMT-FIAT: Delft-FIAT plugin for HydroMT
:target: https://mybinder.org/v2/gh/Deltares/hydromt_fiat/main?urlpath=lab/tree/examples
-hydroMT_ is a python package, developed by Deltares, to build and analyze hydro models.
+HydroMT_ is a python package, developed by Deltares, to build and analyze hydro models.
It provides a generic model api with attributes to access the model schematization,
(dynamic) forcing data, results and states. This plugin provides an implementation
for the Delft-FIAT_ model.
@@ -52,25 +52,9 @@ For developing on HydroMT-FIAT, do:
Documentation
-------------
-Learn more about the hydroMT_fiat plugin in its `online documentation `_
+Learn more about the HydroMT-FIAT plugin in its `online documentation `_
Contributing
------------
-You can find information about contributing to hydroMT at our `Contributing page `_.
-
-License
--------
-
-Copyright (c) 2021, Deltares
-
-This program is free software: you can redistribute it and/or modify it under the terms of the GNU General
-Public License as published by the Free Software Foundation, either version 3 of the License, or (at your
-option) any later version.
-
-This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the
-implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
-for more details.
-
-You should have received a copy of the GNU General Public License along with this program. If not,
-see .
+You can find information about contributing to HydroMT at our `Contributing page `_.
diff --git a/docs/_static/Region_download.PNG b/docs/_static/Region_download.PNG
new file mode 100644
index 00000000..b3b17536
Binary files /dev/null and b/docs/_static/Region_download.PNG differ
diff --git a/docs/_static/charleston.svg b/docs/_static/charleston.svg
new file mode 100644
index 00000000..6d2a318e
--- /dev/null
+++ b/docs/_static/charleston.svg
@@ -0,0 +1,39 @@
+
+
+
+
+
+
+
+
+
+
diff --git a/docs/_static/deltares-blue.svg b/docs/_static/deltares-blue.svg
new file mode 100644
index 00000000..a5a6f8d5
--- /dev/null
+++ b/docs/_static/deltares-blue.svg
@@ -0,0 +1 @@
+Artboard 1
diff --git a/docs/_static/fiat_logo.svg b/docs/_static/fiat_logo.svg
new file mode 100644
index 00000000..63570dfa
--- /dev/null
+++ b/docs/_static/fiat_logo.svg
@@ -0,0 +1,20 @@
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/docs/_static/format_vulnerability_curves.PNG b/docs/_static/format_vulnerability_curves.PNG
new file mode 100644
index 00000000..60f3d9cd
Binary files /dev/null and b/docs/_static/format_vulnerability_curves.PNG differ
diff --git a/docs/_static/hydromt-icon.svg b/docs/_static/hydromt-icon.svg
new file mode 100644
index 00000000..869424b0
--- /dev/null
+++ b/docs/_static/hydromt-icon.svg
@@ -0,0 +1,400 @@
+
+
+
+
+
+ image/svg+xml
+
+ il_icon hydromt_ I1000642-002_rs_v3
+
+
+
+
+
+
+
+ il_icon hydromt_ I1000642-002_rs_v3
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/docs/api/api_index.rst b/docs/api/api_index.rst
index 98b875d6..4885d1cb 100644
--- a/docs/api/api_index.rst
+++ b/docs/api/api_index.rst
@@ -1,14 +1,11 @@
.. currentmodule:: hydromt_fiat
+.. _api_model:
+
=============
API reference
=============
-.. _api_model:
-
-FIAT model class
-================
-
Initialize
----------
@@ -23,13 +20,23 @@ Build components
.. autosummary::
:toctree: ../_generated/
- fiat.FiatModel.setup_config
fiat.FiatModel.setup_basemaps
fiat.FiatModel.setup_vulnerability
- fiat.FiatModel.setup_exposure_vector
+ fiat.FiatModel.setup_vulnerability_from_csv
+ fiat.FiatModel.setup_exposure_buildings
fiat.FiatModel.setup_hazard
fiat.FiatModel.setup_social_vulnerability_index
+Delft-FIAT settings
+-------------------
+
+.. autosummary::
+ :toctree: ../_generated/
+
+ fiat.FiatModel.setup_config
+ fiat.FiatModel.setup_global_settings
+ fiat.FiatModel.setup_output
+
Attributes
----------
diff --git a/docs/conf.py b/docs/conf.py
index aa0737e8..5453e1eb 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -22,10 +22,8 @@
import numpy as np
import sphinx_autosummary_accessors
-from click.testing import CliRunner
import hydromt_fiat
-from hydromt.cli.main import main as hydromt_cli
# here = os.path.dirname(__file__)
# sys.path.insert(0, os.path.abspath(os.path.join(here, "..")))
@@ -103,17 +101,6 @@ def write_nested_dropdown(name, data_cat, note="", categories=[]):
if not os.path.isdir("_generated"):
os.makedirs("_generated")
-# -- Generate cli help docs ----------------------------------------------
-
-cli_build = CliRunner().invoke(hydromt_cli, ["build", "--help"])
-cli2rst(cli_build.output, r"_generated/cli_build.rst")
-
-cli_update = CliRunner().invoke(hydromt_cli, ["update", "--help"])
-cli2rst(cli_update.output, r"_generated/cli_update.rst")
-
-cli_clip = CliRunner().invoke(hydromt_cli, ["clip", "--help"])
-cli2rst(cli_clip.output, r"_generated/cli_clip.rst")
-
# -- General configuration ------------------------------------------------
# If your documentation needs a minimal Sphinx version, state it here.
@@ -174,7 +161,7 @@ def write_nested_dropdown(name, data_cat, note="", categories=[]):
# a list of builtin themes.
#
html_theme = "pydata_sphinx_theme"
-html_logo = "_static/hydromt-icon.svg"
+html_logo = "_static/fiat_logo.svg"
autodoc_member_order = "bysource" # overwrite default alphabetical sort
autoclass_content = "both"
@@ -216,7 +203,7 @@ def write_nested_dropdown(name, data_cat, note="", categories=[]):
"github_url": "https://github.com", # or your GitHub Enterprise interprise
"github_user": "Deltares",
"github_repo": "hydromt_fiat",
- "github_version": "main", # FIXME
+ "github_version": "main",
"doc_path": "docs",
"default_mode": "light",
}
@@ -305,17 +292,6 @@ def write_nested_dropdown(name, data_cat, note="", categories=[]):
),
]
-# FIXME exception while evaluating only directive expression: chunk after expression
-# nbsphinx_prolog = r"""
-# {% set docname = env.doc2path(env.docname, base=None) %}
-# .. only:: html
-# .. role:: raw-html(raw)
-# :format: html
-# .. note::
-# | This page was generated from `{{ docname }}`__.
-# | Interactive online version: :raw-html:` `
-# __ https://github.com/Deltares/hydromt/blob/main/examples/{{ docname }}
-# """
# -- INTERSPHINX -----------------------------------------------------------
@@ -346,3 +322,6 @@ def write_nested_dropdown(name, data_cat, note="", categories=[]):
"""
+
+# Allow errors in notebooks
+# nbsphinx_allow_errors = True
diff --git a/docs/contributing.rst b/docs/contributing.rst
deleted file mode 100644
index 872ddebf..00000000
--- a/docs/contributing.rst
+++ /dev/null
@@ -1,7 +0,0 @@
-Contributing
-============
-
-.. _contributing:
-
-To contribute to HydroMT and the plugin plugin, please follow the
-`HydroMT contribution guidelines `_.
\ No newline at end of file
diff --git a/docs/data/data_sources.rst b/docs/data/data_sources.rst
new file mode 100644
index 00000000..ee2040cd
--- /dev/null
+++ b/docs/data/data_sources.rst
@@ -0,0 +1,24 @@
+.. _data_sources:
+.. currentmodule:: hydromt_fiat.fiat
+
+======================
+Data Sources
+======================
+With the HydroMT-FIAT package you can easily access exposure and vulnerability data from different sources.
+The following data sources are currently supported:
+
+
+* :doc:`national_structure_inventory`
+* :doc:`openstreetmap`
+* :doc:`jrc`
+* :doc:`hazus`
+
+.. toctree::
+ :maxdepth: 1
+ :hidden:
+ :caption: Available data sources
+
+ national_structure_inventory
+ openstreetmap
+ jrc
+ hazus
\ No newline at end of file
diff --git a/docs/data/hazus.rst b/docs/data/hazus.rst
new file mode 100644
index 00000000..824f3c03
--- /dev/null
+++ b/docs/data/hazus.rst
@@ -0,0 +1,26 @@
+.. _hazus:
+
+===============================================
+HAZUS flood depth-damage functions
+===============================================
+
+Users of **HydroMT-FIAT** can directly and easily make use of the depth-damage
+functions and repair values from `HAZUS
+`_. Hazus is a US-based
+program and the depth-damage functions are based on US data. The functions and
+values can be used only together with the `hydromt_fiat_catalog_USA.yml` and
+by providing the following values in the configuration file::
+
+ [setup_vulnerability]
+ vulnerability_fn = "default_vulnerability_curves"
+ vulnerability_identifiers_and_linking_fn = "default_hazus_iwr_linking"
+ unit = "ft"
+
+ [setup_exposure_buildings]
+ max_potential_damage = "hazus_max_potential_damages"
+ unit = "m"
+
+
+The HAZUS flood depth-damage functions and replacement values are processed into an easy-to-use format
+for HydroMT-FIAT and stored in the ``hydromt_fiat/data`` folder.
+
diff --git a/docs/data/jrc.rst b/docs/data/jrc.rst
new file mode 100644
index 00000000..c784b821
--- /dev/null
+++ b/docs/data/jrc.rst
@@ -0,0 +1,28 @@
+.. _jrc:
+
+===============================================
+JRC Global flood depth-damage functions
+===============================================
+
+Users of **HydroMT-FIAT** can directly and easily make use of the `JRC Global flood depth-damage functions and
+replacement values `_ [1]. The functions and
+values can be used only together with the `hydromt_fiat_catalog_global.yml` and
+by providing the following values in the configuration file::
+
+ [setup_vulnerability]
+ vulnerability_fn = "jrc_vulnerability_curves"
+ vulnerability_identifiers_and_linking_fn = "jrc_vulnerability_curves_linking"
+ unit = "m"
+
+ [setup_exposure_buildings]
+ max_potential_damage = "jrc_damage_values"
+ unit = "m"
+
+
+The JRC Global flood depth-damage functions and replacement values are processed into an easy-to-use format
+for HydroMT-FIAT and stored in the ``hydromt_fiat/data`` folder.
+
+
+[1] Huizinga, J., De Moel, H. and Szewczyk, W., Global flood depth-damage functions: Methodology
+and the database with guidelines, EUR 28552 EN, Publications Office of the European Union, Luxembourg,
+2017, ISBN 978-92-79-67781-6, doi:10.2760/16510, JRC105688.
\ No newline at end of file
diff --git a/docs/data/national_structure_inventory.rst b/docs/data/national_structure_inventory.rst
index 31b2649f..dd4806e1 100644
--- a/docs/data/national_structure_inventory.rst
+++ b/docs/data/national_structure_inventory.rst
@@ -1,17 +1,20 @@
+.. _nsi:
+
+==================================
National Structure Inventory (USA)
==================================
For projects in the United States, users of **HydroMT-FIAT** can directly and easily make use
-of the `National Structure Inventory ` (NSI). The
+of the `National Structure Inventory `_ (NSI). The
user can access the data through providing 'NSI' in the configuration file as such::
- [setup_exposure_vector]
- asset_locations = NSI
- occupancy_type = NSI
- max_potential_damage = NSI
+ [setup_exposure_buildings]
+ asset_locations = "NSI"
+ occupancy_type = "NSI"
+ max_potential_damage = "NSI"
The following attributes for Delft-FIAT (left-hand side) will be filled with data from the
-corresponding NSI fields (right-hand side).
+corresponding NSI fields (right-hand side)::
"Object ID": "fd_id",
"Object Name": "fd_id",
@@ -25,7 +28,7 @@ corresponding NSI fields (right-hand side).
"Aggregation Label: Census Block": "cbfips"
HydroMT-FIAT obtains the NSI data of the area of interest through the `NSI API
-`.
+`_.
For more information about this data we refer to their online `Technical Documentation
-`.
+`_.
diff --git a/docs/data/openstreetmap.rst b/docs/data/openstreetmap.rst
new file mode 100644
index 00000000..c1cc1d67
--- /dev/null
+++ b/docs/data/openstreetmap.rst
@@ -0,0 +1,19 @@
+.. _osm:
+
+===============================================
+OpenStreetMap Building Footprints and Land Use
+===============================================
+
+Users of **HydroMT-FIAT** can directly and easily make use of the `OpenStreetMap `_
+(OSM) initiative. The user can access the data through providing 'OSM' in the configuration file as such::
+
+ [setup_exposure_buildings]
+ asset_locations = "OSM"
+ occupancy_type = "OSM"
+
+HydroMT-FIAT obtains the OSM data of the area of interest through the OSMnx package [1].
+
+
+[1] Boeing, G. 2017. "`OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex
+Street Networks. `_" Computers, Environment
+and Urban Systems 65, 126-139.
\ No newline at end of file
diff --git a/docs/examples/index.rst b/docs/examples/index.rst
index 9ea80bfa..9f9182d5 100644
--- a/docs/examples/index.rst
+++ b/docs/examples/index.rst
@@ -5,4 +5,13 @@ Examples
.. include:: ../_examples/README.rst
-# TODO add notebook examples
+
+----------------------
+Offline example access
+----------------------
+Below you can access some of the main examples and go through them in an off-line manner. More examples will be added soon.
+
+1. `Creating a Delft-FIAT model anywhere in the world from OSM and JRC data <../_examples/global_OSM_JRC.ipynb>`_
+2. `Adding aggregation zones to your Delft-FIAT model <../_examples/aggregation_zones_example.ipynb>`_
+3. `Updating your Delft-FIAT model with local data <../_examples/update_exposure_data.ipynb>`_
+4. `Creating a Delft-FIAT model to assess road damage with OSM roads <../_examples/add_roadnetwork.ipynb>`_
diff --git a/docs/index.rst b/docs/index.rst
index 1e382d7f..77a13f2a 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -1,65 +1,51 @@
-======================
-HydroMT plugin: FIAT
-======================
+=====================================================================
+HydroMT-FIAT: Automated and reproducible Delft-FIAT model building
+=====================================================================
-`HydroMT `_ is a python package, developed by Deltares, to build
-and analyse hydro models. It provides a generic model api with attributes to access the model schematization,
-(dynamic) forcing data, results and states.
+With the `HydroMT-FIAT plugin `_, users can easily benefit
+from the rich set of tools of the `HydroMT package `_ to build and update
+`Delft-FIAT `_ models from available global and local data.
-This plugin provides an implementation for the
-`Delft-FIAT `_ flood impact assessment tool.
-It details the different steps and explains how to use HydroMT to easily get started and work on your own FIAT model.
+This plugin assists the FIAT modeller in:
+
+- Quickly setting up a Delft-FIAT model based on existing hazard maps, global and user-input exposure layers, and global, US national, or user-input vulnerability curves.
+- Creating a Social Vulnerability Index (SVI) based on US Census or user-input data.
+- Adjusting and updating components of a Delft-FIAT model and their associated parameters in a consistent way.
+
+The HydroMT-FIAT plugin aims to make the FIAT model building process **fast**, **modular**, and **reproducible**
+by configuring the model building process from single *yml* file.
For detailed information on HydroMT itself, you can visit the `core documentation `_.
-Documentation
+::
+
+ Note: This repository is under development, the documentation is not yet complete...
+
+
+Overview
=============
**Getting Started**
-* :doc:`intro`
* :doc:`installation`
* :doc:`examples/index`
-.. toctree::
- :maxdepth: 1
- :hidden:
- :caption: Getting Started
-
- intro
- installation
- examples/index
-
**User Guide**
-* :doc:`user_guide/fiat`
-* :doc:`user_guide/fiat_flood`
-
-
-.. toctree::
- :maxdepth: 1
- :hidden:
- :caption: User Guide
+* :doc:`user_guide/user_guide_overview`
+* :doc:`data/data_sources`
- user_guide/fiat
- user_guide/fiat_flood
-
-
-**References & Help**
+**Technical documentation**
* :doc:`api/api_index`
-* :doc:`contributing`
* :doc:`changelog`
-.. toctree::
- :maxdepth: 1
- :hidden:
- :caption: References & Help
- api/api_index
- contributing
- changelog
+Contributing
+------------
+To contribute to HydroMT and the plugin plugin, please follow the
+`HydroMT contribution guidelines `_.
License
-------
@@ -72,4 +58,16 @@ License as published by the Free Software Foundation, either version 3 of the Li
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty
of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
-You can find the full terms of the GNU General Public License at .
\ No newline at end of file
+You can find the full terms of the GNU General Public License at .
+
+.. toctree::
+ :titlesonly:
+ :maxdepth: 1
+ :hidden:
+
+ installation
+ examples/index
+ user_guide/user_guide_overview
+ data/data_sources
+ api/api_index
+ changelog
diff --git a/docs/installation.rst b/docs/installation.rst
index 92bdb95e..f45dc575 100644
--- a/docs/installation.rst
+++ b/docs/installation.rst
@@ -1,75 +1,30 @@
+.. _installation:
+
+============
Installation
============
User install
------------
-#TODO update if hydromt_fiat on conda-forge
-
-The hydromt_fiat plugin is currently only available from PyPi.
-We are working on a release from conda-forge.
-
-If you haven't installed the `hydroMT core package `_
-we recommend installing it from conda-forge to get all dependencies and then install the plugin.
-
-To install hydromt using conda-forge do:
-
-.. code-block:: console
-
- conda install hydromt -c conda-forge
-
-To install hydromt_fiat using pip do:
-Note: make sure this is installed in the same environment as hydromt.
-
-.. code-block:: console
-
- pip install hydromt_fiat
+HydroMT-FIAT is available from pypi and will be added to conda-forge (in progress).
-The hydroMT core and fiat plugin can be easily installed together in a single hydromt-fiat environment
-using the environment.yml file in the repository binder folder. This environment includes some packages that are
-required to run the example notebooks.
+To install HydroMT-FIAT using pip, do:
.. code-block:: console
- conda env create -f binder/environment.yml
- conda activate hydromt-fiat
pip install hydromt_fiat
-Developper install
+Developer install
------------------
-If you want to download the fiat plugin directly from git to easily have access to the latest developments or
-make changes to the code you can use the following steps.
-
-First, clone hydromt's fiat plugin ``git`` repo from
-`github `_, then navigate into the
-the code folder (where the envs folder and pyproject.toml are located):
+For developing on HydroMT-FIAT, do:
.. code-block:: console
- $ git clone https://github.com/Deltares/hydromt_fiat.git
- $ cd hydromt_fiat
-
-Then, make and activate a new hydromt-fiat conda environment based on the envs/hydromt-fiat.yml
-file contained in the repository:
-
-.. code-block:: console
-
- $ conda env create -f envs/hydromt-fiat.yml
- $ conda activate hydromt-fiat
-
-Finally, build and install an editable version of hydromt_fiat using `flit `_.
-
-For Windows:
-
-.. code-block:: console
-
- $ flit install --pth-file
-
-For Linux:
-
-.. code-block:: console
+ conda env create -f envs/hydromt-fiat-dev.yml
+ conda activate hydromt-fiat-dev
+ pip install -e .
- $ flit install -s
-For more information about how to contribute, see `HydroMT contributing guidelines `_.
+For more information about how to contribute, see `HydroMT contributing guidelines `_.
diff --git a/docs/intro.rst b/docs/intro.rst
deleted file mode 100644
index 6d059bcb..00000000
--- a/docs/intro.rst
+++ /dev/null
@@ -1,12 +0,0 @@
-Introduction
-============
-
-With the **hydromt_fiat plugin**, users can easily benefit from the rich set of tools of the
-`HydroMT package `_ to build and update
-`Delft-FIAT `_ models from available global and local data.
-
-This plugin assists the fiat modeller in:
-
-- Quickly setting up a Delft-FIAT model based on existing hazard maps, global exposure layers and a database of vulnerability curves.
-- Making maximum use of the best available global or local data.
-- Adjusting and updating components of a FIAT model and their associated parameters in a consistent way.
\ No newline at end of file
diff --git a/docs/user_guide/exposure_vector.rst b/docs/user_guide/exposure_vector.rst
new file mode 100644
index 00000000..759f4287
--- /dev/null
+++ b/docs/user_guide/exposure_vector.rst
@@ -0,0 +1,8 @@
+.. _exposure_vector:
+.. currentmodule:: hydromt_fiat.workflows.exposure_vector
+
+===============================
+Exposure Module for Vector data
+===============================
+
+Coming soon..
diff --git a/docs/user_guide/fiat.rst b/docs/user_guide/fiat.rst
deleted file mode 100644
index be51a1f7..00000000
--- a/docs/user_guide/fiat.rst
+++ /dev/null
@@ -1,43 +0,0 @@
-.. _model_components:
-.. currentmodule:: hydromt_fiat.fiat
-
-=======================
-FIAT model components
-=======================
-
-With the hydromt_fiat plugin, you can easily work with Delft-FIAT model schematizations.
-This plugin helps you preparing or updating several model components of a Delft-FIAT model
-such as hazard, exposure maps and vulnerability curves.
-
-When building a model from command line a model region_
-(a bounding box `bbox` or hazard map `grid`); a model setup
-configuration (.ini file) with model components_ and options and, optionally,
-a data_ sources (.yml) file should be prepared.
-
-
-The Delft-FIAT model components are available from the HydroMT Command Line and Python Interfaces and
-allow you to configure HydroMT in order to build or update Delft-FIAT model schematizations.
-See :ref:`flood damge Delft-FIAT model schematization ` for suggested components
-and options to use for coastal or riverine applications.
-
-For python users all FIAT attributes and methods are available, see :ref:`api_model`
-
-.. _components:
-
-Model components
-================
-
-The following components are available to build or update Delft-FIAT model schematizations:
-
-.. autosummary::
- :toctree: ../_generated/
- :nosignatures:
-
- FiatModel.setup_config
- FiatModel.setup_basemaps
- FiatModel.setup_hazard
- FiatModel.setup_exposure_buildings
- FiatModel.scale_exposure
-
-.. _data: https://deltares.github.io/hydromt/latest/user_guide/data.html
-.. _region: https://deltares.github.io/hydromt/latest/user_guide/cli.html#region-options
diff --git a/docs/user_guide/fiat_flood.rst b/docs/user_guide/fiat_flood.rst
deleted file mode 100644
index 3a9642dc..00000000
--- a/docs/user_guide/fiat_flood.rst
+++ /dev/null
@@ -1,44 +0,0 @@
-.. _model_components:
-.. currentmodule:: hydromt_fiat.fiat
-
-=======================
-FIAT model components
-=======================
-
-With the hydromt_fiat plugin, you can easily work with Delft-FIAT model schematizations.
-This plugin helps you preparing or updating several model components of a Delft-FIAT model
-such as hazard, exposure maps and vulnerability curves.
-
-When building a model from command line a model region_
-(a bounding box `bbox` or hazard map `grid`); a model setup
-configuration (.ini file) with model components_ and options and, optionally,
-a data_ sources (.yml) file should be prepared.
-
-
-The Delft-FIAT model components are available from the HydroMT Command Line and Python Interfaces and
-allow you to configure HydroMT in order to build or update Delft-FIAT model schematizations.
-See :ref:`flood damge Delft-FIAT model schematization ` for suggested components
-and options to use for coastal or riverine applications.
-
-For python users all FIAT attributes and methods are available, see :ref:`api_model`
-
-.. _components:
-
-Model components
-================
-
-The following components are available to build or update Delft-FIAT model schematizations:
-
-.. autosummary::
- :toctree: ../_generated/
- :nosignatures:
-
- FiatModel.setup_config
- FiatModel.setup_basemaps
- FiatModel.setup_vulnerability
- FiatModel.setup_exposure_vector
- FiatModel.setup_hazard
- FiatModel.setup_vulnerability
-
-.. _data: https://deltares.github.io/hydromt/latest/user_guide/data.html
-.. _region: https://deltares.github.io/hydromt/latest/user_guide/cli.html#region-options
\ No newline at end of file
diff --git a/docs/user_guide/hazard.rst b/docs/user_guide/hazard.rst
new file mode 100644
index 00000000..f3b4e544
--- /dev/null
+++ b/docs/user_guide/hazard.rst
@@ -0,0 +1,9 @@
+.. _hazard:
+.. currentmodule:: hydromt_fiat.workflows.hazard
+
+=======================
+Hazard Module
+=======================
+
+Coming soon..
+
diff --git a/docs/user_guide/social_vulnerability_index.rst b/docs/user_guide/social_vulnerability_index.rst
new file mode 100644
index 00000000..6960a16d
--- /dev/null
+++ b/docs/user_guide/social_vulnerability_index.rst
@@ -0,0 +1,9 @@
+.. _svi:
+.. currentmodule:: hydromt_fiat.workflows.social_vulnerability_index
+
+=================================
+Social Vulnerability Index Module
+=================================
+
+Coming soon..
+
diff --git a/docs/user_guide/user_guide_overview.rst b/docs/user_guide/user_guide_overview.rst
new file mode 100644
index 00000000..a9bdbb7c
--- /dev/null
+++ b/docs/user_guide/user_guide_overview.rst
@@ -0,0 +1,171 @@
+.. _user_guide:
+.. currentmodule:: hydromt_fiat.fiat
+
+=======================
+User Guide
+=======================
+
+With the HydroMT-FIAT plugin, you can easily work with Delft-FIAT model schematizations.
+This plugin helps you preparing or updating several model components of a Delft-FIAT model
+such as the hazard, exposure and vulnerability data.
+
+When building a model from command line, a model setup configuration file (*.yml* ) and,
+optionally, a model region_ (e.g., a bounding box `bbox` or hazard map `grid`) and a
+data_catalog_ (*.yml*) file should be prepared.
+
+The Delft-FIAT model methods are available from the HydroMT Command Line and Python Interfaces and
+allow you to build or update Delft-FIAT model schematizations.
+
+Built-in data sources are available for the exposure and vulnerability components, see the available
+:ref:`data_sources`.
+
+For python users, all FIAT attributes and methods are available, see the :ref:`api_model`.
+
+Exposure
+=======================
+
+Vector data
+-----------------------
+
+Exposure data can be build from a data_catalog_, a data API key, or by supplying an absolute path
+to local data on the user's machine. The `asset_locations`, `occupancy_type`, and `max_potential_damage`
+data should be provided as a vector file (e.g. *.shp* or *.gpkg*). The `ground_floor_height` can currently
+only be set to a single value (this will be updated soon!). The `damage_types` should be provided as a
+list of strings (e.g. ["structure", "content"]). The `unit` should be provided as a string (e.g. "m").
+See below how the `setup_exposure_buildings` method can be used to build or update the exposure data::
+
+ [setup_exposure_buildings]
+ asset_locations =
+ occupancy_type =
+ damage_types =
+ max_potential_damage =
+ ground_floor_height =
+ unit =
+
+The following method is used to build or update the **exposure** data:
+
+.. autosummary::
+ FiatModel.setup_exposure_buildings
+
+For more information, see the :ref:`exposure_vector`.
+
+Raster data
+-----------------------
+This option will be implemented at a later stage.
+
+
+Aggregation Zones
+-----------------------
+In spatial analysis and urban planning the division of objects into spatial zones, such as land-use or accommodation type, is a pivotal tool to facilitate analysis and/or visualization. The `FIAT toolbox `_ offers simple tools to assign aggregation labels to the exposure.csv file, after calculating damages with `FIAT toolbox `_. Subsequental, the `FIAT toolbox `_ can be used to automatically calculate metrics over the aggregation areas. The user can add multiple aggregation labels at once by providing vector files for each zone (e.g., *.shp* or *.gpkg*).
+
+To associate the original exposure data with the aggregation zones, the **"join_exposure_aggregation_areas"** function can be utilized. This function seamlessly links each geometry in the original exposure data to its corresponding spatial aggregation zone.
+To prepare the data, an aggregation configuration file (*.yml*) must be created with the following information (case-sensitive):
+
+Input yaml file:
+ - **new_root**: Path to the output folder
+ - **aggregation_area_fn**: Path to the aggregation file
+ - **attribute_names**: Name of the zone attribute in your file
+ - **label_names**: Desired aggregation label for newly created aggregation zone
+
+In case the user wants to add several aggregation zones at once, multiple aggregation files can be provided in a list. Each variable (file path, attribute name, label name) must follow the same order to assure that attribute and label names are assigned to the correct aggregation file::
+
+ [Example configuration yaml file for two aggregation zone files.]
+
+ Title: "Base_zones and Land_use aggregation zones"
+ new_root: "./fiat_model/output/aggregation_zones"
+ configuration:
+ setup_aggregation_areas:
+ aggregation_area_fn:
+ - "./agg_zones/base_zone_aggregation.shp"
+ - "./agg_zones/land_use_aggregation.shp"
+ attribute_names:
+ - "ZONE_BASE"
+ - "LAND_USE"
+ label_names:
+ - "Base Zone"
+ - "Land Use"
+
+After loading the configuration file (*.yml*) and executing the **FIAT Hydro MT** model builder, the user receives a file (*.csv*) with the original exposure data and an additional column with the aggregation label(s) as output. The `FIAT toolbox `_ will automatically prepend "*Aggregation Label*" to the prior specified aggregation label, therefore the aggregation labels can be identified as such.
+
+ Aggregation Label: {label_name}
+
+*Note: It may occur that polygons overlap in the aggregation vector files. In this case the information for the affected Object ID will be merged and both aggregation zones will be assigned to the object.* ::
+
+ Object ID Zone
+
+ 1 Base Zone 1
+ 2 Land Use 1, Land Use 3 > two zones (polygons) in the land-use aggregation file overlap and object
+ falls into both zones
+ 3 Land Use 2
+
+
+
+Vulnerability
+=======================
+Vulnerability data can be build from a data_catalog_ or by supplying an absolute path
+to local data on the user's machine. The `vulnerability_fn` and `vulnerability_identifiers_and_linking_fn`
+data should be provided as a csv file (*.csv*). The `functions_mean` and `functions_max` parameters
+can be used when an aereal calculation is done (i.e., taking the hazard value over the whole area of the
+building footprint or over the whole length of the linestring). The user can use one of the parameters as
+a default by setting it to "default". If required, the user can provide the names of the damage functions
+as a list to `functions_mean` or `functions_max` to respectively use the mean or max hazard value for all
+assets that use that damage function. The `damage_types` should be provided as a
+list of strings (e.g. ["structure", "content"]). The `unit` should be provided as a string (e.g. "m").
+The `step_size` should be provided as a float (e.g. 0.01).
+See below how the `setup_vulnerability` method can be used to build or update the exposure data::
+
+ [setup_vulnerability]
+ vulnerability_fn =
+ vulnerability_identifiers_and_linking_fn =
+ functions_mean =
+ functions_max =
+ unit =
+ step_size =
+
+The following methods can be used to build or update the **vulnerability** data:
+
+.. autosummary::
+ FiatModel.setup_vulnerability
+ FiatModel.setup_vulnerability_from_csv
+
+For more information, see the :ref:`vulnerability`.
+
+Hazard
+=======================
+The following methods can be used to build or update the **hazard** data:
+
+.. autosummary::
+ FiatModel.setup_hazard
+
+For more information, see the :ref:`hazard`.
+
+Social Vulnerability Index
+===========================
+The following methods can be used to build or update the **Social Vulnerability Index** data:
+
+.. autosummary::
+ FiatModel.setup_social_vulnerability_index
+
+For more information, see the :ref:`svi`.
+
+Output and run settings
+========================
+The following methods are available to set up the **output and run settings** for Delft-FIAT:
+
+.. autosummary::
+ FiatModel.setup_global_settings
+ FiatModel.setup_output
+
+
+.. _data_catalog: https://deltares.github.io/hydromt/latest/user_guide/data_prepare_cat.html
+.. _region: https://deltares.github.io/hydromt/latest/user_guide/cli.html#region-options
+
+.. toctree::
+ :maxdepth: 1
+ :hidden:
+ :caption: Modules
+
+ exposure_vector
+ vulnerability
+ hazard
+ social_vulnerability_index
diff --git a/docs/user_guide/vulnerability.rst b/docs/user_guide/vulnerability.rst
new file mode 100644
index 00000000..bacab60e
--- /dev/null
+++ b/docs/user_guide/vulnerability.rst
@@ -0,0 +1,38 @@
+.. _vulnerability:
+.. currentmodule:: hydromt_fiat.workflows.vulnerability
+
+=======================
+Vulnerability Module
+=======================
+
+The vulnerability module contains functions to select vulnerability curves from
+existing (pre-defined) sources and transform them to a common
+`Delft-FIAT `_ format, see vulnerability_format_.
+The currently available sources are :ref:`jrc` and :ref:`hazus`.
+
+Note that the terms vulnerability curves and damage functions are used interchangeably
+in this module and documentation.
+
+.. _vulnerability_format:
+
+Vulnerability CSV format
+------------------------
+
+The format of a `Delft-FIAT `_ vulnerability
+curve database is a csv file structured as follows:
+
+.. image:: /_static/format_vulnerability_curves.PNG
+ :width: 300px
+ :align: center
+ :alt: Format vulnerability curves
+
+
+The first row contains the **unit** of the hazard values that are specified in the
+first column. The second row contains the **method** (mean or max) that should be
+used by FIAT to compute the hazard value for each asset when an aereal extraction
+method is used. This method links to the vulnerability curve specified in the same
+column below. For example, if the method of a certain vulnerability curves is set
+to `max`, the maximum value of the hazard values under the assets using that curve
+will be used to compute the damage. The third row contains the **name of the hazard
+and names of the damage functions**.
+
diff --git a/envs/hydromt-fiat-dev.yml b/envs/hydromt-fiat-dev.yml
index d65516ae..d540c690 100644
--- a/envs/hydromt-fiat-dev.yml
+++ b/envs/hydromt-fiat-dev.yml
@@ -7,24 +7,23 @@ dependencies:
- pip
- pip:
- census
+ - us
+ - xarray-spatial
- click
- black
- gdal>=3.1
- geopandas
- - hydromt
+ - geopy
+ - hydromt=0.8.0
- hydromt_sfincs
- - nbsphinx # docs
- numpy
- osmnx
- pandas
- pydantic=1.10.9
- - pygeos>=0.8
- pyogrio
- pytest # tests
- python=3.10
- requests
- - sphinx # docs
- - sphinx_rtd_theme # docs
- xarray
- tomli-w
- tomli
diff --git a/envs/hydromt-fiat-docs.yml b/envs/hydromt-fiat-docs.yml
new file mode 100644
index 00000000..4c42a90e
--- /dev/null
+++ b/envs/hydromt-fiat-docs.yml
@@ -0,0 +1,33 @@
+name: hydromt-fiat-docs
+
+channels:
+- conda-forge
+
+dependencies:
+- click
+- gdal>=3.1
+- geopandas
+- jupyterlab
+- matplotlib-base
+- mercantile
+- nbsphinx
+- notebook
+- numpy
+- packaging
+- pandas
+- pip
+- pyogrio
+- pydata-sphinx-theme
+- python=3.10
+- rasterio
+- requests
+- rioxarray
+- sphinx
+- tomli
+- tomli-w
+- universal_pathlib
+- xarray
+- pip:
+ - sphinx_autosummary_accessors
+ - sphinx_design
+ - census
diff --git a/examples/add_roadnetwork.ipynb b/examples/add_roadnetwork.ipynb
new file mode 100644
index 00000000..0ed940ae
--- /dev/null
+++ b/examples/add_roadnetwork.ipynb
@@ -0,0 +1,1112 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# **FIAT Model Setup for a Road Network**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Roads are vital to the infrastructure of a community and, when blocked and/or (partially) destroyed \n",
+ "cause numerous issues for various players. Therefore, it is imperative to assess the impact of loss in mobility when analyzing flood events and other hazards. \n",
+ "\n",
+ "FIAT offers a tool to compute the monetary impact of the road blockage/destruction based on the **extent of the hazard, the road type (e.g. motorway, trunk, primary roads), and the number of lanes.**\n",
+ "\n",
+ "To run the FIAT model on a road network it is necessary to create the [exposure data](https://github.com/Deltares/Delft-FIAT/blob/master/docs/user_guide/data/exposure.qmd) and [vulnerability data](https://github.com/Deltares/Delft-FIAT/blob/master/docs/user_guide/data/vulnerability.qmd).\n",
+ "\n",
+ "**Notebook Output**: \n",
+ "The output of this notebook will be: \n",
+ "- `exposure data`\n",
+ "- `vulnerability data` \n",
+ "\n",
+ " which are neccessary to run the FIAT model for various events and scenarios of a flood event on a road network.\n",
+ "\n",
+ "\n",
+ ">*Note: This notebook will cover the road impact assessment based on Open Sreet Map (OSM) data."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# **Let's get started!**"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# **Step 1**: Import required packages"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# First, all required packages must be installed.\n",
+ "\n",
+ "from hydromt_fiat.fiat import FiatModel\n",
+ "from hydromt.log import setuplog\n",
+ "from pathlib import Path\n",
+ "import geopandas as gpd\n",
+ "import pandas as pd\n",
+ "import os \n",
+ "import json\n",
+ "import yaml\n",
+ "from hydromt.config import configread\n",
+ "from shapely.geometry import Polygon\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# **Step 2:** Data Input"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To run FIAT the user must provide data about the road network and the sudy area of interest;\n",
+ "- Polygon of the region of interest (from the coordinates we will create a shapely polygon)\n",
+ "- Information on the road network within the study area (geospatial file format e.g. *.shp, *.geojson, *.gpkg ):\n",
+ " - road type\n",
+ " - number of lanes\n",
+ " - segment length\n",
+ "- Model configuration (see section **Step 2b: Create configuration file**) "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## **Step 2a:** Define the study area"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "FIAT requires the input of a region in form of a **shapely Polygon** to download the **OSM road dataset**. This can be done via **vector file** or **GeoJSON object** of the study area. We recommened to download the region from [Geojson.io](https://geojson.io/#map=2/0/20). \n",
+ "\n",
+ "**GeoJson.io** \n",
+ "Draw a polygon on the map and create a polygon from the GeoJson Object. You can download a vector file, you need to refer to in the \"region\" tab in the *yaml* file using the file path. You can also copy the GeoJson code directly into the configuration file (see example in [configuration file](C:/Users/rautenba/hydromt_fiat/examples/data/road_network/configuration.yml)).\n",
+ "\n",
+ "![Region_download.PNG](../docs/_static/charelston.PNG.svg)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Create a dictionary of the GeoJson Object\n",
+ "\n",
+ "region = {\n",
+ " \"type\": \"FeatureCollection\",\n",
+ " \"features\": [\n",
+ " {\n",
+ " \"type\": \"Feature\",\n",
+ " \"properties\": {},\n",
+ " \"geometry\": {\n",
+ " \"type\": \"Polygon\",\n",
+ " \"coordinates\": [\n",
+ " [\n",
+ " [-79.96177042294748, 32.79061007407526],\n",
+ " [-79.96177042294748, 32.76748645567278],\n",
+ " [-79.91724162366485, 32.76748645567278],\n",
+ " [-79.91724162366485, 32.79061007407526],\n",
+ " [-79.96177042294748, 32.79061007407526],\n",
+ " ]\n",
+ " ],\n",
+ " },\n",
+ " }\n",
+ " ],\n",
+ "}\n",
+ "\n",
+ "# Extract the coordinates\n",
+ "coordinates = region[\"features\"][0][\"geometry\"][\"coordinates\"]\n",
+ "\n",
+ "# Create a Shapely Polygon\n",
+ "polygon = Polygon(coordinates[0]) # Extract the coordinates of the exterior ring"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## **Step 2b:** Create the configuration file"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We must configure the FIAT model form of a `yaml`-file or a dictionary, in which the model parameters are defined. The configuration file must define the **vulnerability** and **exposure setup**. \n",
+ "The required fields are:\n",
+ " \n",
+ "*setup_road_vulnerability:*\n",
+ "- **vertical_unit**:Unit of vertical inundation (feet or meter)\n",
+ "- **threshold_value**: Threshold inundation height at which road is fully destroyed \n",
+ "- **min_hazard_value**: minimum inundation height (recommended: 0)\n",
+ "- **max_hazard_value**: maximum inundation height (e.g. 6 meters) \n",
+ "- **step_hazard_value**: discrete levels or steps of inundation height\n",
+ "\n",
+ "*setup_exposure_roads:*\n",
+ "- **roads_fn**: File path to road network file or OSM data (for OSM data use: \"OSM\")\n",
+ "- **road_types**: Road type keys (if OSM data use keys from from OSM e.g. \"motorway\", \"primary\")\n",
+ "- **road_damage**: Damages per lane and per length unit\n",
+ "- **unit**: Unit of road length (feet or meter)\n",
+ "\n",
+ "The provided parameters need to be incorporated into the YAML structure. Please check [here](C:/Users/rautenba/hydromt_fiat/examples/data/road_network/configuration.yaml) for the setup of the *configuration.yml*. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Load the configuration file"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**This step is not necessary to run the model. It's just for visualization purposes.** \n",
+ "\n",
+ "The configuration file can be read in python and its information are converted into a python dictionary.
\n",
+ "Note: Instead of loading a yaml file it is possible to create a python dictionary directly, built upon the structure seen below.
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "{\n",
+ " \"setup_road_vulnerability\": {\n",
+ " \"vertical_unit\": \"feet\",\n",
+ " \"threshold_value\": 0.6,\n",
+ " \"min_hazard_value\": 0,\n",
+ " \"max_hazard_value\": 10,\n",
+ " \"step_hazard_value\": 1\n",
+ " },\n",
+ " \"setup_exposure_roads\": {\n",
+ " \"roads_fn\": \"OSM\",\n",
+ " \"road_types\": [\n",
+ " \"motorway\",\n",
+ " \"primary\",\n",
+ " \"secondary\",\n",
+ " \"tertiary\"\n",
+ " ],\n",
+ " \"road_damage\": \"default_road_max_potential_damages\",\n",
+ " \"unit\": \"meter\"\n",
+ " }\n",
+ "}\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Let's read the configuration file holding the required information for the FIAT model \n",
+ "with open(Path(os.path.abspath(\"\")) / \"data\" / \"road_network\" / \"configuration.yaml\", 'r') as file:\n",
+ " config = yaml.safe_load(file)\n",
+ "\n",
+ "print(json.dumps(config, indent=4, sort_keys=False))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# **Step 3**: Define variables for the FIAT model "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To build the model some settings must be ingested as variables. So you need to set up the root path to the FIAT model, the logger settings, the region and the data catalog:\n",
+ "- `root`: Directory path where the FIAT model data is stored. \n",
+ "- `logger`: The logger variable defines the frequencies of log-entries during the initialisation of the model.\n",
+ "- `region`: Define the region as a variable from the config dictionary.\n",
+ "- `data_catalog_yml`: Define the data catalogue as a variable from the config dictionary.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2023-11-02 09:43:07,944 - hydromt_fiat - log - INFO - HydroMT version: 0.8.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Set up Fiat Model\n",
+ "root = Path(os.path.abspath(\"\")) / \"data\" / \"road_network\" / \"FIAT model\" \n",
+ "\n",
+ "# Set up the logger\n",
+ "logger = setuplog(\"hydromt_fiat\", log_level=10)\n",
+ "\n",
+ "# Set up datacatalog\n",
+ "data_catalog_yml = Path(os.path.abspath(\"\")) / \"data\" / \"road_network\" / \"hydromt_fiat_catalog_USA.yml\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# **Step 4:** Run the FIAT model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "It's time to run the road network FIAT model. The output will be stored in the root-directory. \n",
+ "\n",
+ "The output will be an **exposure folder** incl. `exposure.csv` together with vector data files, a **vulnerability folder** incl. `damage_curve.csv`, a **hazard folder** (empty) and an **output folder** (empty). \n",
+ "\n",
+ ">*Note*: Ignore the output folder (in this notebook only)! \n",
+ " In this example we don't provide any hazard data, so the `output folder` will be empty. In case hazard data is provided the `output` folder contains an updated `exposure.csv` with the damages of the event on the road segment."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2023-11-02 09:43:10,658 - hydromt_fiat - data_catalog - INFO - Parsing data catalog from c:\\Repositories\\hydromt_fiat\\examples\\data\\road_network\\hydromt_fiat_catalog_USA.yml\n",
+ "2023-11-02 09:43:10,665 - hydromt_fiat - log - DEBUG - Writing log messages to new file c:\\Repositories\\hydromt_fiat\\examples\\data\\road_network\\FIAT model\\hydromt.log.\n",
+ "2023-11-02 09:43:10,665 - hydromt_fiat - model_api - INFO - Initializing fiat model from hydromt_fiat (v0.2.1.dev0).\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2023-11-02 09:43:10,688 - hydromt_fiat - model_api - INFO - setup_region.region: {'geom': geometry\n",
+ "0 POLYGON ((-79.96177 32.79061, -79.96177 32.767...}\n",
+ "2023-11-02 09:43:10,689 - hydromt_fiat - basin_mask - DEBUG - Parsed region (kind=geom): {'geom': 'GeoDataFrame [-79.96177042 32.76748646 -79.91724162 32.79061007] (crs = EPSG:4326)'}\n",
+ "2023-11-02 09:43:10,691 - hydromt_fiat - model_api - INFO - setup_road_vulnerability.vertical_unit: feet\n",
+ "2023-11-02 09:43:10,692 - hydromt_fiat - model_api - INFO - setup_road_vulnerability.threshold_value: 0.6\n",
+ "2023-11-02 09:43:10,692 - hydromt_fiat - model_api - INFO - setup_road_vulnerability.min_hazard_value: 0\n",
+ "2023-11-02 09:43:10,693 - hydromt_fiat - model_api - INFO - setup_road_vulnerability.max_hazard_value: 10\n",
+ "2023-11-02 09:43:10,693 - hydromt_fiat - model_api - INFO - setup_road_vulnerability.step_hazard_value: 1\n",
+ "2023-11-02 09:43:10,694 - hydromt_fiat - model_api - INFO - setup_exposure_roads.roads_fn: OSM\n",
+ "2023-11-02 09:43:10,694 - hydromt_fiat - model_api - INFO - setup_exposure_roads.road_damage: default_road_max_potential_damages\n",
+ "2023-11-02 09:43:10,695 - hydromt_fiat - model_api - INFO - setup_exposure_roads.road_types: ['motorway', 'primary', 'secondary', 'tertiary']\n",
+ "2023-11-02 09:43:10,695 - hydromt_fiat - model_api - INFO - setup_exposure_roads.unit: meter\n",
+ "2023-11-02 09:43:10,696 - hydromt_fiat - exposure_vector - INFO - Setting up roads...\n",
+ "2023-11-02 09:43:10,791 - hydromt_fiat - exposure_vector - INFO - The damage function 'roads' is selected for all of the structure damage to the roads.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:hydromt_fiat:The damage function 'roads' is selected for all of the structure damage to the roads.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2023-11-02 09:43:10,792 - hydromt_fiat - data_catalog - INFO - DataCatalog: Getting default_road_max_potential_damages DataFrame csv data from c:\\Repositories\\hydromt_fiat\\examples\\data\\road_network\\us_road_damage.csv\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:hydromt_fiat:DataCatalog: Getting default_road_max_potential_damages DataFrame csv data from c:\\Repositories\\hydromt_fiat\\examples\\data\\road_network\\us_road_damage.csv\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2023-11-02 09:43:10,796 - hydromt_fiat - dataframe - INFO - DataFrame: Read csv data.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:hydromt_fiat:DataFrame: Read csv data.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2023-11-02 09:43:10,813 - hydromt_fiat - exposure_vector - INFO - Setting exposure geometries...\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:hydromt_fiat:Setting exposure geometries...\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2023-11-02 09:43:10,814 - hydromt_fiat - exposure_vector - INFO - Setting geometry name to roads...\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:hydromt_fiat:Setting geometry name to roads...\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2023-11-02 09:43:10,816 - hydromt_fiat - fiat - INFO - Updating all data objects...\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:hydromt_fiat:Updating all data objects...\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2023-11-02 09:43:10,818 - hydromt_fiat - model_api - ERROR - Default config file not found at c:\\Repositories\\hydromt_fiat\\examples\\data\\fiat\\settings.toml\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "ERROR:hydromt_fiat:Default config file not found at c:\\Repositories\\hydromt_fiat\\examples\\data\\fiat\\settings.toml\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2023-11-02 09:43:10,819 - hydromt_fiat - model_api - WARNING - Replacing geom: region\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "WARNING:hydromt_fiat:Replacing geom: region\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2023-11-02 09:43:10,820 - hydromt_fiat - fiat - INFO - Writing model data to c:\\Repositories\\hydromt_fiat\\examples\\data\\road_network\\FIAT model\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:hydromt_fiat:Writing model data to c:\\Repositories\\hydromt_fiat\\examples\\data\\road_network\\FIAT model\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2023-11-02 09:43:10,821 - hydromt_fiat - model_api - INFO - Writing model config to c:\\Repositories\\hydromt_fiat\\examples\\data\\road_network\\FIAT model\\settings.toml\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:hydromt_fiat:Writing model config to c:\\Repositories\\hydromt_fiat\\examples\\data\\road_network\\FIAT model\\settings.toml\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2023-11-02 09:43:10,822 - hydromt_fiat - model_api - DEBUG - Writing file exposure/region.gpkg\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "DEBUG:hydromt_fiat:Writing file exposure/region.gpkg\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2023-11-02 09:43:11,301 - hydromt_fiat - model_api - DEBUG - Writing file exposure/roads.gpkg\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "DEBUG:hydromt_fiat:Writing file exposure/roads.gpkg\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2023-11-02 09:43:11,327 - hydromt_fiat - fiat - INFO - Writing model exposure table file to exposure/exposure.csv.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:hydromt_fiat:Writing model exposure table file to exposure/exposure.csv.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2023-11-02 09:43:11,332 - hydromt_fiat - fiat - INFO - Writing model vulnerability_curves table file to vulnerability/vulnerability_curves.csv.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:hydromt_fiat:Writing model vulnerability_curves table file to vulnerability/vulnerability_curves.csv.\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Build the FIAT model \n",
+ "fiat_model = FiatModel(root=root, mode=\"w+\", data_libs=[data_catalog_yml], logger=logger)\n",
+ "\n",
+ "region = gpd.GeoDataFrame.from_features(region, crs=4326)\n",
+ "opt = configread(Path(os.path.abspath(\"\")) / \"data\" / \"road_network\" / \"configuration.yaml\")\n",
+ "fiat_model.build(region={\"geom\": region}, opt=opt, write=False)\n",
+ "fiat_model.write()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# **Done!**\n",
+ "Your **FIAT model** created the exposure and vulnerability files for the road network. Using that data in combination with a hazard map, the impact for the defined hazard can be computed. \n",
+ "\n",
+ "Let's have a look at the output!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "Make this Notebook Trusted to load map: File -> Trust Notebook
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Load exposure.csv into dataframe\n",
+ "df_exposure = pd.read_csv(Path(os.path.abspath(\"\")) / \"data\" / \"road_network\" / \"FIAT model\" / \"exposure\" / \"exposure.csv\")\n",
+ "\n",
+ "# Load exposure geopackage into GeoDataFrame\n",
+ "gdf_exposure =gpd.read_file(Path(os.path.abspath(\"\")) / \"data\" / \"road_network\" / \"FIAT model\" / \"exposure\" / \"roads.gpkg\")\n",
+ "\n",
+ "# Merge dataframe with GeoDataFrame\n",
+ "merged_gdf = gdf_exposure.merge(df_exposure, left_on='Object ID', right_on='Object ID', how='inner')\n",
+ "\n",
+ "# Display the road network\n",
+ "road_network = merged_gdf.explore(column = 'Max Potential Damage: Structure')\n",
+ "road_network"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The vulnerability is translated into a step function, which is determined by the water level threshold. Water levels beyond the threshold result in untraversible road segments. \n",
+ "\n",
+ "We can have a look at the `vulnerability.csv` output and the vulnerability curve. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " #UNIT=feet \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " #METHOD \n",
+ " mean \n",
+ " \n",
+ " \n",
+ " water depth [feet] \n",
+ " roads \n",
+ " \n",
+ " \n",
+ " 0.0 \n",
+ " 0.0 \n",
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+ " 0.59 \n",
+ " 0.0 \n",
+ " \n",
+ " \n",
+ " 0.6 \n",
+ " 1.0 \n",
+ " \n",
+ " \n",
+ " 1.0 \n",
+ " 1.0 \n",
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+ " 2.0 \n",
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+ " 1.0 \n",
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+ " 1.0 \n",
+ " \n",
+ " \n",
+ " 7.0 \n",
+ " 1.0 \n",
+ " \n",
+ " \n",
+ " 8.0 \n",
+ " 1.0 \n",
+ " \n",
+ " \n",
+ " 9.0 \n",
+ " 1.0 \n",
+ " \n",
+ " \n",
+ " 10.0 \n",
+ " 1.0 \n",
+ " \n",
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+ "
"
+ ],
+ "text/plain": [
+ " #UNIT=feet\n",
+ "#METHOD mean\n",
+ "water depth [feet] roads\n",
+ "0.0 0.0\n",
+ "0.59 0.0\n",
+ "0.6 1.0\n",
+ "1.0 1.0\n",
+ "2.0 1.0\n",
+ "3.0 1.0\n",
+ "4.0 1.0\n",
+ "5.0 1.0\n",
+ "6.0 1.0\n",
+ "7.0 1.0\n",
+ "8.0 1.0\n",
+ "9.0 1.0\n",
+ "10.0 1.0"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Display vulnerability.csv \n",
+ "df_vulnerability = pd.read_csv(Path(os.path.abspath(\"\")) / \"data\" / \"road_network\" / \"FIAT model\" / \"vulnerability\" / \"vulnerability_curves.csv\")\n",
+ "df_vulnerability"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Display vulnerability curves\n",
+ "water_depths = fiat_model.vulnerability.hazard_values\n",
+ "\n",
+ "# Plot damage curves for some occupancy types\n",
+ "line_styles = ['--', '-', ':']\n",
+ "for function_name, ls in zip(fiat_model.vulnerability.functions.keys(), line_styles):\n",
+ " dmg = [float(i) for i in fiat_model.vulnerability.functions[function_name]]\n",
+ " plt.plot(water_depths, dmg, label=function_name, ls=ls)\n",
+ "plt.xlabel('depth (m)')\n",
+ "plt.ylabel('damage fraction (-)')\n",
+ "plt.legend(bbox_to_anchor=(1.04, 1), loc=\"upper left\")\n",
+ "plt.show()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "hydromt-fiat-dev",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/aggregation_zones_example.ipynb b/examples/aggregation_zones_example.ipynb
new file mode 100644
index 00000000..b777167c
--- /dev/null
+++ b/examples/aggregation_zones_example.ipynb
@@ -0,0 +1,471 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Example Aggregation Zones"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In spatial analysis and urban planning the division of objects into spatial zones is a pivotal tool to facilitate analysis and/or visualization. This jupyter notebook demonstrates how to create a **spatial joint** of the FIAT model data with single or multiple **aggregation zones** in order to link an the zone and objects of interest. \n",
+ "The base for this notebook is the **DELFT FIAT toolbox**. \n",
+ "\n",
+ "*Note: The FIAT model was already initialized for this notebook. Unless the user does not wish to use own data, there is no need to create a new model and the user can simply continue with the example data found in the `/ \"example\" / \"data\"/ \"aggregation_zone\"` - directory.*"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Install Delft-FIAT\n",
+ "\n",
+ "HydroMT-FIAT is available on pypi and can be installed via: \n",
+ "\n",
+ "`pip install hydromt_fiat`\n",
+ "\n",
+ "If you want to know more about the installation process or if you are interested to install HydroMT-FIAT as a developer, please check out the [Installation Guide](../installation.rst).\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# **Step 0**: Import required packages\n",
+ "Now we can import the neccessary python packages to build the **FIAT model**."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Import packages \n",
+ "import geopandas as gpd\n",
+ "from pathlib import Path\n",
+ "import os\n",
+ "from hydromt_fiat.fiat import FiatModel\n",
+ "from hydromt.log import setuplog\n",
+ "import pandas as pd\n",
+ "import yaml\n",
+ "import json\n",
+ "import shutil\n",
+ "from hydromt.config import configread"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# **Before we start:** Study area and aggregation zones"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this notebook we will spatially join three different aggregation zones to the FIAT exposure objects . \n",
+ "\n",
+ "The aggregation zones handled in this notebook are:\n",
+ "- **Base_zones**\n",
+ "- **Land_use**\n",
+ "- **Accommodation_type** \n",
+ "\n",
+ "The data was obtained from https://gis.charleston-sc.gov/"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Let's have a look at the study area and one of the aggregation zones that we would like to join!**\n",
+ "\n",
+ "The map demonstrates the `Base_zones.gpkg` aggregation layer in Charelston, United States, which devides the county into base district zones. One of the three layers, that will be **spatially joined** with the objects in the `exposure data` (yellow markers) in this notebook. This can be achieved for multiple aggregation zone layers. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Load aggregation zones as GeoDataFrames\n",
+ "exposure=gpd.read_file(Path(os.path.abspath(\"\")) / \"data\" / \"aggregation_zones\" / \"fiat_model\" / \"exposure\" / \"buildings.gpkg\")\n",
+ "base_zone=gpd.read_file(Path(os.path.abspath(\"\")) / \"data\" / \"aggregation_zones\" / \"aggregation_zones\" / \"base_zones.gpkg\")\n",
+ "\n",
+ "# Create interactive map\n",
+ "m = base_zone.explore(column = 'ZONE_BASE')\n",
+ "m = exposure.explore(m=m, color = '#FFFACD')\n",
+ "\n",
+ "# Display map\n",
+ "m"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# **Step 1**: Configure and initialize the FIAT model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this notebook, the **FIAT model** has already been initialized and solely the exposure data will be updated by the add-on of the the aggregation zones. The required data is stored in the \n",
+ "`\"examples\"/ \"data\" / \"aggregation_zones / \"aggregation_zones\"` - directory. \n",
+ "\n",
+ "*Note: To configure and initialize your **own** FIAT model refer to global_OSM_JRC.ipynb . If you do so, **don't forget** to update the folder dependencies and variables throughout this notebook pointing to your data.* "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# **Step 2**: Define aggregation parameters in the configuration file "
+ ]
+ },
+ {
+ "attachments": {
+ "Aggregation_zones.PNG": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Two test cases are created in this jupyter notebook. For each test case we need a seperate configuration file ([configuration.yaml](data/aggregation_zones/configuration.yaml)/ [configuration_2.yaml](data/aggregation_zones/configuration_2.yaml)) with the required model input information:\n",
+ "\n",
+ "1. Spatial joint of a **single aggregation zone** to the exposure data\n",
+ "2. Spatial joint of **multiple aggregation zones** to the exposure data\n",
+ "\n",
+ "The configuration is saved in a *yaml* file, in which the following parameters must be specified by the user: \n",
+ "\n",
+ "- `aggregation_area_fn`: File path to the aggregation file (vector file). \n",
+ "- `attribute_names`: Name of the column that specifies the aggregation zone in the aggregation vector file (case-sensitive).\n",
+ "- `label_names`: New aggregation label name, specified by the user.\n",
+ "\n",
+ "Below an example of the input for the `attribute_names`. The column `\"ZONE_BASE\"`categorizes the aggregation zones in the vector file.\n",
+ "\n",
+ "![Aggregation_zones.PNG](attachment:Aggregation_zones.PNG)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# **Step 3:** Load the configuration file"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This step is not neccessarry to run the model. It is only for visualizing the model input. \n",
+ "The configuration files are located in the `\"example\"/ \"data\" /` - folder. The information in the file are converted into a python dictionary. \n",
+ "\n",
+ "*Note: Instead of loading a yaml file it is possible to create a python dictionary directly, built upon the structure seen below.*"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Let's read the yaml file with the required information \n",
+ "\n",
+ "# Test Case 1\n",
+ "with open(Path(os.path.abspath(\"\")) / \"data\" / \"aggregation_zones\" / \"configuration.yaml\", 'r') as file:\n",
+ " config_aggregation = yaml.safe_load(file)\n",
+ "\n",
+ "print(f\"\\\"Test Case 1\\\" {json.dumps(config_aggregation, indent=4, sort_keys=False)}\")\n",
+ "\n",
+ "# Test Case 2\n",
+ "with open(Path(os.path.abspath(\"\")) / \"data\" / \"aggregation_zones\" / \"configuration_2.yaml\", 'r') as file:\n",
+ " config_aggregation_2 = yaml.safe_load(file)\n",
+ "\n",
+ "print(f\"\\\"Test Case 2\\\" {json.dumps(config_aggregation_2, indent=4, sort_keys=False)}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# **Step 4**: Define variables for the FIAT model "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Set up the root path to the FIAT model, the path where the output should be stored and the logger settings to re-build your model:\n",
+ "\n",
+ "- `root`: Directory path from where the neccessary data for the FIAT model is stored. \n",
+ "- `new_root`: Directory path where the FIAT model output is stored. \n",
+ "- `logger`: The logger variable defines the frequencies of log-entries during the initialisation of the model."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Set up the root for the Fiat Model\n",
+ "root = Path(os.path.abspath(\"\")) / \"data\" / \"aggregation_zones\" / \"fiat_model\"\n",
+ "\n",
+ "# Set up the file path for the FIAT model output\n",
+ "new_root = Path(os.path.abspath(\"\")) / \"data\" / \"aggregation_zones\" / \"aggregation_zones_test1\"\n",
+ "\n",
+ "# Set up the logger\n",
+ "logger = setuplog(\"hydromt_fiat\", log_level=10)\n",
+ "\n",
+ "# Read the model configuration \n",
+ "opt = configread(Path(os.path.abspath(\"\")) / \"data\" / \"aggregation_zones\" / \"configuration.yaml\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# **Test case 1**: Run the FIAT model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "It's time to run the model with the new configuration of the aggregation zones. The output will be \n",
+ "stored in the prior defined `\"new_root\"` - directory. The output will be a **new exposure.csv **, in which each object is assigned **spatially** to an **aggregation zone**. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Read the existing FIAT model \n",
+ "fm = FiatModel(root=root, mode=\"r\", logger=logger)\n",
+ "fm.read()\n",
+ "\n",
+ "# If case exist\n",
+ "if new_root.exists():\n",
+ " shutil.rmtree(new_root)\n",
+ "\n",
+ "# Build the new model\n",
+ "fm.build(write=False, opt=opt)\n",
+ "fm.set_root(new_root)\n",
+ "fm.write()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# **Test case 2**: Run the FIAT model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To run the test case 2, select it the configuration dictionary when re-running the model. Hence, the model will select the parameters of the second test case instead of the first and create spatial joints to **multiple** aggregation zones. \n",
+ "\n",
+ "To run the second model, the `model variables` must be updated."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Set up the root for the Fiat Model\n",
+ "root = Path(os.path.abspath(\"\")) / \"data\" / \"aggregation_zones\" / \"fiat_model\"\n",
+ "\n",
+ "# Set up the file path for the FIAT model output\n",
+ "new_root = Path(os.path.abspath(\"\")) / \"data\" / \"aggregation_zones\" / \"aggregation_zones_test2\"\n",
+ "\n",
+ "# Set up the logger\n",
+ "logger = setuplog(\"hydromt_fiat\", log_level=10)\n",
+ "\n",
+ "# Read the model configuration \n",
+ "opt = configread(Path(os.path.abspath(\"\")) / \"data\" / \"aggregation_zones\" / \"configuration_2.yaml\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Read the existing FIAT model \n",
+ "fm = FiatModel(root=root, mode=\"r\", logger=logger)\n",
+ "fm.read()\n",
+ "\n",
+ "#If case exist\n",
+ "if new_root.exists():\n",
+ " shutil.rmtree(new_root)\n",
+ "\n",
+ "# Build the new model\n",
+ "fm.build(write=False, opt=opt)\n",
+ "fm.set_root(new_root)\n",
+ "fm.write()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# **Done!** \n",
+ "Your **FIAT model** created a **spatial joint** with your original objects and the aggregation zones.
Let's have a look at the output!
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# **Result**: Test Case 1"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Load the newly created *exposure.csv* file from the / \"output\" / \"aggregation_zones_test1\" / - directory and the original exposure geopackage from the \"exposure\" / \"buildings.gpkg\" . Before the results can be displayed, we must merge the **exposure.csv** with the **buildings.gpkg** via common *\"Object ID\"*. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Load *.csv into dataframe\n",
+ "df_single_aggregation = pd.read_csv(Path(os.path.abspath(\"\")) / \"data\" / \"aggregation_zones\" / \"aggregation_zones_test1\" / \"exposure\" / \"exposure.csv\")\n",
+ "\n",
+ "#Load original exposure geopackage into GeoDataFrame\n",
+ "new_exposure=gpd.read_file(Path(os.path.abspath(\"\")) / \"data\" / \"aggregation_zones\" / \"aggregation_zones_test1\" / \"exposure\" / \"buildings.gpkg\")\n",
+ "\n",
+ "#Merge dataframe with GeoDataFrame\n",
+ "merged_gdf = new_exposure.merge(df_single_aggregation, left_on='Object ID', right_on='Object ID', how='inner')\n",
+ "\n",
+ "#Display base_zones aggregation zone\n",
+ "base_zones_map = merged_gdf.explore(column = 'Base_zones')\n",
+ "base_zones_map\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# **Result**: Test Case 2"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Load the newly created *exposure.csv* file from the / \"output\" / \"aggregation_zones_test2\" / - directory and the original exposure geopackage from the \"exposure\" / \"buildings.gpkg\" . Before the results can be displayed, we must merge the **exposure.csv** with the **buildings.gpkg** via common *\"Object ID\"*. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Load *.csv into dataframe\n",
+ "df_multiple_aggregation = pd.read_csv(Path(os.path.abspath(\"\")) / \"data\" / \"aggregation_zones\" / \"aggregation_zones_test2\" / \"exposure\" / \"exposure.csv\")\n",
+ "\n",
+ "# Load original exposure geopackage into GeoDataFrame\n",
+ "new_exposure=gpd.read_file(Path(os.path.abspath(\"\")) / \"data\" / \"aggregation_zones\" / \"aggregation_zones_test2\" / \"exposure\" / \"buildings.gpkg\")\n",
+ "\n",
+ "# Merge dataframe with GeoDataFrame\n",
+ "merged_gdf_multiple = new_exposure.merge(df_multiple_aggregation, left_on='Object ID', right_on='Object ID', how='inner')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### **Base Zones**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Display base_zones aggregation zone\n",
+ "base_zones_map = merged_gdf.explore(column = 'Base_zones')\n",
+ "base_zones_map"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### **Land use**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Display land_use aggregation zone\n",
+ "land_use_map = merged_gdf_multiple.explore(column = 'Land_use')\n",
+ "land_use_map"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### **Accomodation type**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Display accomodation_type aggregation zone\n",
+ "Accom_type_map = merged_gdf_multiple.explore(column = 'Accommodation_type')\n",
+ "Accom_type_map"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "fiat_dev",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.12"
+ },
+ "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/data/aggregation_zones/aggregation_zones/accomodation_type.gpkg b/examples/data/aggregation_zones/aggregation_zones/accomodation_type.gpkg
new file mode 100644
index 00000000..0f763142
Binary files /dev/null and b/examples/data/aggregation_zones/aggregation_zones/accomodation_type.gpkg differ
diff --git a/examples/data/aggregation_zones/aggregation_zones/base_zones.gpkg b/examples/data/aggregation_zones/aggregation_zones/base_zones.gpkg
new file mode 100644
index 00000000..0cf5c6ed
Binary files /dev/null and b/examples/data/aggregation_zones/aggregation_zones/base_zones.gpkg differ
diff --git a/examples/data/aggregation_zones/aggregation_zones/council.gpkg b/examples/data/aggregation_zones/aggregation_zones/council.gpkg
new file mode 100644
index 00000000..67f52edb
Binary files /dev/null and b/examples/data/aggregation_zones/aggregation_zones/council.gpkg differ
diff --git a/examples/data/aggregation_zones/aggregation_zones/land_use.gpkg b/examples/data/aggregation_zones/aggregation_zones/land_use.gpkg
new file mode 100644
index 00000000..c6f0ba65
Binary files /dev/null and b/examples/data/aggregation_zones/aggregation_zones/land_use.gpkg differ
diff --git a/examples/data/aggregation_zones/configuration.yaml b/examples/data/aggregation_zones/configuration.yaml
new file mode 100644
index 00000000..7ddb658f
--- /dev/null
+++ b/examples/data/aggregation_zones/configuration.yaml
@@ -0,0 +1,8 @@
+setup_aggregation_areas:
+ aggregation_area_fn: "data/aggregation_zones/aggregation_zones/base_zones.gpkg"
+ attribute_names: ZONE_BASE
+ label_names: Base_zones
+
+
+
+
\ No newline at end of file
diff --git a/examples/data/aggregation_zones/configuration_2.yaml b/examples/data/aggregation_zones/configuration_2.yaml
new file mode 100644
index 00000000..4ec81f24
--- /dev/null
+++ b/examples/data/aggregation_zones/configuration_2.yaml
@@ -0,0 +1,13 @@
+setup_aggregation_areas:
+ aggregation_area_fn:
+ - "data/aggregation_zones/aggregation_zones/base_zones.gpkg"
+ - "data/aggregation_zones/aggregation_zones/land_use.gpkg"
+ - "data/aggregation_zones/aggregation_zones/accomodation_type.gpkg"
+ attribute_names:
+ - ZONE_BASE
+ - LAND_USE
+ - ACCOM
+ label_names:
+ - Base_zones
+ - Land_use
+ - Accommodation_type
\ No newline at end of file
diff --git a/examples/data/aggregation_zones/fiat_model/exposure/buildings.gpkg b/examples/data/aggregation_zones/fiat_model/exposure/buildings.gpkg
new file mode 100644
index 00000000..568539a9
Binary files /dev/null and b/examples/data/aggregation_zones/fiat_model/exposure/buildings.gpkg differ
diff --git a/examples/data/aggregation_zones/fiat_model/exposure/exposure.csv b/examples/data/aggregation_zones/fiat_model/exposure/exposure.csv
new file mode 100644
index 00000000..c7c27e42
--- /dev/null
+++ b/examples/data/aggregation_zones/fiat_model/exposure/exposure.csv
@@ -0,0 +1,3082 @@
+Object ID,Object Name,Primary Object Type,Secondary Object Type,Max Potential Damage: Structure,Max Potential Damage: Content,Ground Elevation,X Coordinate,Y Coordinate,Aggregation Label: Census Block,Ground Floor Height,Extraction Method,Damage Function: Structure
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diff --git a/examples/data/aggregation_zones/fiat_model/exposure/region.gpkg b/examples/data/aggregation_zones/fiat_model/exposure/region.gpkg
new file mode 100644
index 00000000..5a73425c
Binary files /dev/null and b/examples/data/aggregation_zones/fiat_model/exposure/region.gpkg differ
diff --git a/examples/data/aggregation_zones/fiat_model/output/aggregation_zones_test1/exposure/buildings.gpkg b/examples/data/aggregation_zones/fiat_model/output/aggregation_zones_test1/exposure/buildings.gpkg
new file mode 100644
index 00000000..6f61cf64
Binary files /dev/null and b/examples/data/aggregation_zones/fiat_model/output/aggregation_zones_test1/exposure/buildings.gpkg differ
diff --git a/examples/data/aggregation_zones/fiat_model/output/aggregation_zones_test1/exposure/exposure.csv b/examples/data/aggregation_zones/fiat_model/output/aggregation_zones_test1/exposure/exposure.csv
new file mode 100644
index 00000000..0db5ec57
--- /dev/null
+++ b/examples/data/aggregation_zones/fiat_model/output/aggregation_zones_test1/exposure/exposure.csv
@@ -0,0 +1,3082 @@
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+574486819,574486819,RES,RES1-1SNB,223773.423,111886.711,3.892720026645661,-79.94141607,32.775137429,450190001003008,1,centroid,RES1-1SNB,STR
+574486820,574486820,RES,RES1-1SNB,203264.754,101632.377,3.892720026645661,-79.94141607,32.775137429,450190001003008,1,centroid,RES1-1SNB,STR
+574486821,574486821,RES,RES1-1SNB,218561.014,109280.507,3.892720026645661,-79.94141607,32.775137429,450190001003008,1,centroid,RES1-1SNB,STR
+574486822,574486822,RES,RES1-1SNB,214165.178,107082.589,3.892720026645661,-79.94141607,32.775137429,450190001003008,1,centroid,RES1-1SNB,STR
diff --git a/examples/data/aggregation_zones/fiat_model/output/aggregation_zones_test1/settings.toml b/examples/data/aggregation_zones/fiat_model/output/aggregation_zones_test1/settings.toml
new file mode 100644
index 00000000..44b60c5b
--- /dev/null
+++ b/examples/data/aggregation_zones/fiat_model/output/aggregation_zones_test1/settings.toml
@@ -0,0 +1,22 @@
+[global]
+crs = "epsg:4326"
+
+[output]
+path = "output"
+
+[output.csv]
+name = "output.csv"
+
+[output.geom]
+name1 = "spatial.gpkg"
+
+[vulnerability]
+file = "./vulnerability/vulnerability_curves.csv"
+unit = "feet"
+step_size = 0.1
+
+[exposure.geom]
+csv = "./exposure/exposure.csv"
+crs = "EPSG:4326"
+unit = "ft"
+file1 = "./exposure/buildings.gpkg"
diff --git a/examples/data/aggregation_zones/fiat_model/output/aggregation_zones_test1/vulnerability/vulnerability_curves.csv b/examples/data/aggregation_zones/fiat_model/output/aggregation_zones_test1/vulnerability/vulnerability_curves.csv
new file mode 100644
index 00000000..6dcbb006
--- /dev/null
+++ b/examples/data/aggregation_zones/fiat_model/output/aggregation_zones_test1/vulnerability/vulnerability_curves.csv
@@ -0,0 +1,37 @@
+#UNIT=feet
+#METHOD,max,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean
+water depth [feet],AGR1,COM1,COM10,COM2,COM3,COM4,COM5,COM6,COM7,COM8,COM9,EDU1,EDU2,GOV1,GOV2,IND1,IND2,IND3,IND4,IND5,IND6,REL1,RES1-1SNB,RES1-1SWB,RES1-2SNB,RES1-2SWB,RES1-3SNB,RES1-3SWB,RES2,RES3A,RES3B,RES3C,RES3D,RES3E,RES3F,RES4,RES5,RES6
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+8.0,0.7,0.87,0.42,0.72,0.843,1.0,1.0,0.925,0.74,0.83,0.22,0.593,0.75,1.0,0.98,0.6,0.907,0.76,0.53,0.76,0.71,0.943,0.357,0.363,0.284,0.291,0.35,0.39,0.81,0.593,0.593,0.593,0.593,0.593,0.593,0.613,0.9,1.0
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diff --git a/examples/data/aggregation_zones/fiat_model/output/aggregation_zones_test2/exposure/buildings.gpkg b/examples/data/aggregation_zones/fiat_model/output/aggregation_zones_test2/exposure/buildings.gpkg
new file mode 100644
index 00000000..621045c5
Binary files /dev/null and b/examples/data/aggregation_zones/fiat_model/output/aggregation_zones_test2/exposure/buildings.gpkg differ
diff --git a/examples/data/aggregation_zones/fiat_model/output/aggregation_zones_test2/exposure/exposure.csv b/examples/data/aggregation_zones/fiat_model/output/aggregation_zones_test2/exposure/exposure.csv
new file mode 100644
index 00000000..4d284eb5
--- /dev/null
+++ b/examples/data/aggregation_zones/fiat_model/output/aggregation_zones_test2/exposure/exposure.csv
@@ -0,0 +1,3082 @@
+Object ID,Object Name,Primary Object Type,Secondary Object Type,Max Potential Damage: Structure,Max Potential Damage: Content,Ground Elevation,X Coordinate,Y Coordinate,Aggregation Label: Census Block,Ground Floor Height,Extraction Method,Damage Function: Structure,Aggregation Label: Base_zones,Aggregation Label: Land_use,Aggregation Label: Accommodation_type
+573783417,573783417,COM,RES4,1177614.2,588807.1,8.381748561544418,-79.93000008,32.770545239,450190002002010,1,centroid,RES4,SR-3,Neighborhood,
+573783433,573783433,COM,RES4,476216.254,238108.127,7.374183635444641,-79.92968382,32.771271301,450190002002010,1,centroid,RES4,SR-4,Neighborhood,
+573785665,573785665,RES,RES1-2SNB,330007.42,165003.71,7.364031290178299,-79.93179589,32.773364727,450190002002004,1,centroid,RES1-2SNB,SR-5,Neighborhood,
+574346729,574346729,RES,RES3A,472285.148,236142.574,10.902030923233031,-79.93064525,32.774854333,450190002001030,1,centroid,RES3A,SR-5,Neighborhood,
+574372707,574372707,RES,RES1-3SNB,389363.808,194681.904,6.365223632082939,-79.93187273,32.770989143,450190002002008,1,centroid,RES1-3SNB,SR-3,Neighborhood,
+574372721,574372721,RES,RES3A,503924.421,251962.21,7.1200097102165225,-79.93191719,32.771641938,450190002002008,1,centroid,RES3A,SR-4,Neighborhood,
+574476728,574476728,RES,RES1-1SNB,96964.414,48482.207,5.8671571920728685,-79.93241085,32.769968112,450190002002018,1,centroid,RES1-1SNB,GB,Neighborhood,
+574476811,574476811,RES,RES1-1SNB,152049.256,76024.6281,5.8671571920728685,-79.93241085,32.769968112,450190002002018,1,centroid,RES1-1SNB,GB,Neighborhood,
+574486899,574486899,RES,RES3A,480189.335,240094.667,12.333664919576645,-79.93027436,32.775360757,450190002001030,1,centroid,RES3A,SR-3,Neighborhood,
+574486932,574486932,RES,RES1-1SNB,177705.21,88852.6051,6.46012056552887,-79.92998998,32.773144664,450190002002005,1,centroid,RES1-1SNB,SR-4,Neighborhood,
+574372378,574372378,RES,RES1-3SNB,275317.367,137658.683,11.553864836273194,-79.92697274,32.77580358,450190002001011,1,centroid,RES1-3SNB,DR-1F,Neighborhood,
+574421614,574421614,PUB,REL1,1834696.67,1834696.67,-4.999869298858642,-79.92477287,32.775260615,450190002001002,1,centroid,REL1,DR-1F,,
+574437143,574437143,COM,COM2,642736.815,642736.815,9.078115437116622,-79.92640322,32.776823374,450190002001034,1,centroid,COM2,LB,Neighborhood,
+574446459,574446459,RES,RES1-3SNB,483801.666,241900.833,-4.999869298858642,-79.9250907,32.774485627,450190002001014,1,centroid,RES1-3SNB,DR-1F,,
+574488867,574488867,RES,RES1-2SNB,261232.301,130616.15,9.352340615797043,-79.92766193,32.773486846,450190002002000,1,centroid,RES1-2SNB,SR-3,Neighborhood,
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+573783427,573783427,RES,RES1-3SWB,780064.073,390032.036,7.003932398548126,-79.93010789,32.771055204,450190002002010,1,centroid,RES1-3SWB,SR-3,Neighborhood,
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+573831730,573831730,RES,RES3A,205336.005,102668.002,11.93792995847702,-79.93231375,32.775717083,450190002001026,1,centroid,RES3A,SR-5,Neighborhood,
+573831821,573831821,COM,COM2,104939.595,104939.595,10.039824678144456,-79.93136773,32.78120541,450190051002055,1,centroid,COM2,GB,City Centers,A-1
+573850799,573850799,COM,COM1,205297.671,205297.671,8.307100390386582,-79.9370822,32.780106517,450190004001006,1,centroid,COM1,DR-2F,Neighborhood,
+573860243,573860243,RES,RES3B,493484.753,246742.376,5.002420096139908,-79.94073347,32.773739974,450190001003003,1,centroid,RES3B,STR,Neighborhood,
+574268207,574268207,RES,RES3A,427020.79,213510.395,8.950609208221435,-79.93066806,32.772190687,450190002002009,1,centroid,RES3A,SR-3,Neighborhood,
+574268219,574268219,RES,RES3B,352245.479,176122.739,9.26148342247963,-79.93104665,32.771945208,450190002002009,1,centroid,RES3B,SR-3,Neighborhood,
+574268595,574268595,RES,RES3A,443323.388,221661.694,4.947173550362587,-79.93307183,32.770319309,450190002002018,1,centroid,RES3A,SR-3,Neighborhood,
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+574268926,574268926,RES,RES3A,625595.694,312797.847,5.074223357849121,-79.93722348,32.776803473,450190001001006,1,centroid,RES3A,DR-1F,Neighborhood,
+574269135,574269135,RES,RES3A,318561.495,159280.747,3.712516484827996,-79.93826701,32.773914061,450190001002002,1,centroid,RES3A,DR-1,Neighborhood,
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+574295941,574295941,RES,RES1-2SNB,189126.984,94563.4922,4.288736975607872,-79.94051647,32.778824337,450190005002018,1,centroid,RES1-2SNB,STR,Neighborhood,
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+573783435,573783435,RES,RES1-1SNB,415179.31,207589.655,6.924839691925049,-79.92959328,32.771352309,450190002002010,1,centroid,RES1-1SNB,SR-4,Neighborhood,
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+573831758,573831758,RES,RES1-2SNB,180241.924,90120.9621,10.893359307575226,-79.93181846,32.774972758,450190002001026,1,centroid,RES1-2SNB,SR-5,Neighborhood,
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+573841028,573841028,RES,RES1-3SNB,380290.602,190145.301,6.375420954608917,-79.93236844,32.770764112,450190002002007,1,centroid,RES1-3SNB,SR-4,Neighborhood,
+573860287,573860287,RES,RES1-3SNB,417497.598,208748.799,3.643869844532013,-79.94063884,32.775115733,450190001003003,1,centroid,RES1-3SNB,STR,Neighborhood,
+574267584,574267584,RES,RES3A,411551.65,205775.825,6.095345212216377,-79.92692669,32.78147486,450190051002033,1,centroid,RES3A,GB,City Centers,A-1
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+574268755,574268755,RES,RES1-3SWB,502546.635,251273.317,5.283719611101151,-79.93747767,32.774232104,450190001002001,1,centroid,RES1-3SWB,DR-1F,Neighborhood,
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+574293795,574293795,RES,RES3A,594474.301,297237.15,11.115810576028824,-79.93206727,32.774434528,450190002002002,1,centroid,RES3A,SR-5,Neighborhood,
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+574446467,574446467,RES,RES1-3SNB,394979.425,197489.712,11.48322785446167,-79.9290221,32.778559412,450190051002059,1,centroid,RES1-3SNB,SR-5,Neighborhood,
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+574476749,574476749,RES,RES1-1SNB,152049.256,76024.6281,5.8671571920728685,-79.93241085,32.769968112,450190002002018,1,centroid,RES1-1SNB,GB,Neighborhood,
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+574431318,574431318,COM,COM1,284787.922,284787.922,6.7361033772945405,-79.92857452,32.78132147,450190051002042,1,centroid,COM1,GB,City Centers,A-1
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+574446487,574446487,COM,COM1,41048.5634,41048.5634,11.621140647916794,-79.92783811,32.778163155,450190002001019,1,centroid,COM1,SR-5,Neighborhood,
+574446530,574446530,IND,IND2,125419.085,188128.627,10.071893073158265,-79.93454321,32.779150786,450190004001016,1,centroid,IND2,DR-1F,Neighborhood,
+574451481,574451481,COM,COM3,181296.844,181296.844,6.471536185131073,-79.93007513,32.780377243,450190051002051,1,centroid,COM3,GB,Neighborhood,A-1
+574461031,574461031,RES,RES3A,423287.305,211643.652,10.039619738283156,-79.9278853,32.779870634,450190051002029,1,centroid,RES3A,GB,Neighborhood,A-1
+574461075,574461075,RES,RES3A,266985.928,133492.964,12.440670904636384,-79.92903083,32.776368402,450190002001017,1,centroid,RES3A,LB,Neighborhood,
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+574486889,574486889,RES,RES3A,480189.335,240094.667,7.664248631649017,-79.93469895,32.773907663,450190001002004,1,centroid,RES3A,SR-3,Neighborhood,
+574488707,574488707,RES,RES1-2SNB,231245.703,115622.851,9.464215878095628,-79.92820274,32.778662139,450190051002049,1,centroid,RES1-2SNB,SR-5,Neighborhood,
+574488862,574488862,RES,RES1-2SNB,215788.475,107894.237,11.147458140335084,-79.92787671,32.774030712,450190002002000,1,centroid,RES1-2SNB,SR-4,Neighborhood,
+574488872,574488872,RES,RES3A,480189.335,240094.667,8.192235509195328,-79.92970963,32.771767424,450190002002010,1,centroid,RES3A,SR-4,Neighborhood,
+573387902,573387902,COM,RES4,3659120.0,1829560.0,9.259190755558016,-79.93201312,32.780098957,450190051002056,1,centroid,RES4,GB,City Centers,A-1
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+573782572,573782572,RES,RES1-1SNB,318258.702,159129.351,9.149605813541411,-79.92987574,32.772444946,450190002002005,1,centroid,RES1-1SNB,SR-4,Neighborhood,
+573785653,573785653,RES,RES1-2SNB,228334.317,114167.158,7.084059973249436,-79.93191169,32.773184427,450190002002004,1,centroid,RES1-2SNB,SR-4,Neighborhood,
+573785839,573785839,RES,RES3A,599084.053,299542.026,10.726813599529269,-79.93324946,32.775619435,450190002001025,1,centroid,RES3A,SR-4,Neighborhood,
+573785899,573785899,RES,RES3B,445783.209,222891.604,8.479128630828857,-79.93236015,32.772141633,450190002002003,1,centroid,RES3B,SR-4,Neighborhood,
+573789723,573789723,RES,RES1-1SNB,229106.199,114553.099,4.602850725879669,-79.93899668,32.773525213,450190001002008,1,centroid,RES1-1SNB,STR,Neighborhood,
+573795552,573795552,RES,RES3A,533913.604,266956.802,3.615668711633682,-79.93958966,32.774360446,450190001003004,1,centroid,RES3A,STR,Neighborhood,
+573795664,573795664,RES,RES1-2SNB,193189.366,96594.6832,4.263146479301453,-79.93997171,32.777265081,450190001001001,1,centroid,RES1-2SNB,STR,Neighborhood,
+573795687,573795687,RES,RES3A,566576.453,283288.226,4.983361862359047,-79.93978301,32.777964217,450190001001001,1,centroid,RES3A,DR-1F,Neighborhood,
+573807503,573807503,RES,RES1-1SWB,195452.158,97726.0793,5.986681326255798,-79.94603081,32.776689581,450190005002024,1,centroid,RES1-1SWB,DR-1,Neighborhood,
+574371567,574371567,COM,COM2,67867.9225,67867.9225,12.465729887065889,-79.93276684,32.781806628,450190051002053,1,centroid,COM2,GB,City Centers,A-1
+574372060,574372060,RES,RES3A,512672.958,256336.479,4.332204954853058,-79.94054419,32.772812341,450190001003006,1,centroid,RES3A,STR,Neighborhood,
+574372072,574372072,RES,RES1-2SWB,413199.939,206599.969,4.173259621472359,-79.94018291,32.773010452,450190001003006,1,centroid,RES1-2SWB,STR,Neighborhood,
+574371910,574371910,RES,RES1-2SNB,365670.564,182835.282,6.59976596124649,-79.93666954,32.773115876,450190001002006,1,centroid,RES1-2SNB,DR-1,Neighborhood,
+574421947,574421947,RES,RES3E,2435687.66,1217843.83,10.09828964017868,-79.93528261,32.780263613,450190004001009,1,centroid,RES3E,LB,Neighborhood,
+574447824,574447824,COM,COM1,1272140.18,1272140.18,11.85115826476097,-79.93411816,32.780945961,450190004001011,1,centroid,COM1,GB,City Centers,A-1
+574486868,574486868,RES,RES1-1SNB,151483.316,75741.658,5.233436599254608,-79.93622307,32.774620925,450190001002001,1,centroid,RES1-1SNB,DR-1F,Neighborhood,
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+574486999,574486999,RES,RES1-1SNB,130168.24,65084.12,10.258887398967742,-79.93570616,32.778083429,450190004001014,1,centroid,RES1-1SNB,DR-1F,Neighborhood,
+574488437,574488437,RES,RES1-1SWB,112626.724,56313.3622,11.802289494400023,-79.93392391,32.781461064,450190004002015,1,centroid,RES1-1SWB,GB,City Centers,A-1
+573783284,573783284,COM,COM1,184356.219,184356.219,11.53996177865982,-79.92950695,32.77844283,450190051002058,1,centroid,COM1,SR-5,Neighborhood,
+573783297,573783297,COM,COM5,1043513.5,1043513.5,11.68407204738617,-79.93094355,32.778464521,450190051002058,1,centroid,COM5,GB,City Centers,
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+574372310,574372310,COM,AGR1,1701682.41,1701682.41,7.491769836397171,-79.92536003,32.779720867,450190051002009,1,centroid,AGR1,LI,City Centers,A-3
+574446465,574446465,RES,RES1-2SWB,454600.939,227300.469,11.451791487722398,-79.92893785,32.778623909,450190051002059,1,centroid,RES1-2SWB,SR-5,Neighborhood,
+574460989,574460989,RES,RES3A,620742.284,310371.142,12.660816990756988,-79.93232572,32.778119861,450190051002057,1,centroid,RES3A,GB,City Centers,A-1
+574461288,574461288,RES,RES3A,270323.253,135161.626,9.784363229970932,-79.92738581,32.77970087,450190051002029,1,centroid,RES3A,GB,City Centers,A-1
+574488685,574488685,RES,RES1-1SNB,198089.072,99044.5363,11.487322740621568,-79.92947662,32.778394904,450190051002058,1,centroid,RES1-1SNB,SR-5,Neighborhood,
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+573387920,573387920,PUB,GOV1,409474.191,409474.191,9.010105909481048,-79.93191667,32.780486772,450190051002056,1,centroid,GOV1,GB,City Centers,A-1
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+574447812,574447812,IND,IND6,86307.4389,86307.4389,11.561221551523207,-79.93434929,32.780741534,450190004001011,1,centroid,IND6,GB,City Centers,
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+574447826,574447826,COM,COM8,990757.882,990757.882,11.85115826476097,-79.93411816,32.780945961,450190004001011,1,centroid,COM8,GB,City Centers,A-1
+574447836,574447836,COM,COM2,1347979.38,1347979.38,11.672280965442658,-79.93368836,32.78091163,450190004001011,1,centroid,COM2,GB,City Centers,A-1
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+573407532,573407532,RES,RES3A,312987.606,156493.803,9.388399080400466,-79.92681104,32.778289954,450190002001008,1,centroid,RES3A,LB,City Centers,
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+573467723,573467723,COM,RES4,146911.004,73455.5023,7.205438343849182,-79.92839527,32.77175843,450190002002011,1,centroid,RES4,SR-3,Neighborhood,
+573467726,573467726,RES,RES1-2SNB,421375.203,210687.601,7.971950971336365,-79.92922584,32.770686687,450190002002011,1,centroid,RES1-2SNB,SR-3,Neighborhood,
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+573467728,573467728,COM,RES4,447990.428,223995.214,6.607965902338028,-79.92929336,32.770856818,450190002002011,1,centroid,RES4,SR-3,Neighborhood,
+573467729,573467729,RES,RES3B,639434.436,319717.218,6.1844009685087205,-79.92860345,32.770856816,450190002002011,1,centroid,RES3B,SR-3,Neighborhood,
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+573467735,573467735,RES,RES1-2SNB,546238.414,273119.207,6.439359844694137,-79.92905401,32.77139283,450190002002011,1,centroid,RES1-2SNB,SR-4,Neighborhood,
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+573467746,573467746,COM,COM4,405574.23,405574.23,5.49663765604496,-79.92805491,32.771993839,450190002002011,1,centroid,COM4,SR-3,Neighborhood,
+573467747,573467747,RES,RES1-3SNB,486027.03,243013.515,6.769053326597214,-79.929019,32.770324,450190002002011,1,centroid,RES1-3SNB,SR-3,Neighborhood,
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+573782495,573782495,RES,RES1-3SNB,447372.524,223686.262,11.947352498817445,-79.92813575,32.778079241,450190002001020,1,centroid,RES1-3SNB,SR-5,Neighborhood,
+573782512,573782512,RES,RES1-1SNB,230522.952,115261.476,11.040000818252564,-79.92891453,32.777767176,450190002001020,1,centroid,RES1-1SNB,SR-5,Neighborhood,
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+573782540,573782540,RES,RES1-2SNB,278796.48,139398.24,12.306768518314362,-79.9284404,32.775666181,450190002001032,1,centroid,RES1-2SNB,SR-5,Neighborhood,
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+573782586,573782586,RES,RES3A,421325.574,210662.787,8.191982072114945,-79.93017261,32.772909565,450190002002005,1,centroid,RES3A,SR-3,Neighborhood,
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+574486800,574486800,RES,RES1-1SNB,146198.845,73099.4228,4.491402943100929,-79.93871033,32.777371745,450190001001003,1,centroid,RES1-1SNB,DR-1F,Neighborhood,
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+574486826,574486826,RES,RES1-2SNB,259280.726,129640.363,3.8786339311409,-79.9408639,32.775053967,450190001003003,1,centroid,RES1-2SNB,STR,Neighborhood,
+574486830,574486830,RES,RES3A,480189.335,240094.667,4.007586081156731,-79.94010269,32.773919714,450190001003004,1,centroid,RES3A,STR,Neighborhood,
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+574486842,574486842,RES,RES1-2SNB,224152.369,112076.184,4.433450330719948,-79.93872965,32.773556377,450190001002008,1,centroid,RES1-2SNB,STR,Neighborhood,
+574486851,574486851,RES,RES1-1SNB,229640.236,114820.118,6.58367896434784,-79.93801926,32.775587877,450190001002003,1,centroid,RES1-1SNB,DR-1F,Neighborhood,
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+574486971,574486971,RES,RES1-1SNB,195637.5,97818.75,4.777656605434418,-79.93777386,32.776445317,450190001001005,1,centroid,RES1-1SNB,DR-1F,Neighborhood,
+574486974,574486974,RES,RES1-1SNB,83839.2768,41919.6384,5.878535656547546,-79.93716135,32.776559813,450190001001008,1,centroid,RES1-1SNB,DR-1F,Neighborhood,
+573475701,573475701,RES,RES3A,598728.541,299364.27,8.664779896554947,-79.93392255,32.774543245,450190002001024,1,centroid,RES3A,SR-4,Neighborhood,
+573475712,573475712,RES,RES3B,569736.084,284868.042,10.158379259052277,-79.93353068,32.775133449,450190002001024,1,centroid,RES3B,SR-4,Neighborhood,
+573789241,573789241,RES,RES1-2SWB,278399.153,139199.576,4.880106980752945,-79.93780143,32.773427301,450190001002007,1,centroid,RES1-2SWB,SR-2,Neighborhood,
+573789165,573789165,RES,RES1-1SNB,121806.349,60903.1745,6.851357018451691,-79.93676377,32.776128349,450190001001008,1,centroid,RES1-1SNB,DR-1F,Neighborhood,
+573789719,573789719,RES,RES3A,251233.21,125616.605,3.980367799158096,-79.93898076,32.773420021,450190001002008,1,centroid,RES3A,STR,Neighborhood,
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+574268561,574268561,RES,RES1-3SNB,434213.667,217106.833,5.754943622560501,-79.93304805,32.769791258,450190002002018,1,centroid,RES1-3SNB,SR-2,Neighborhood,
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+574269166,574269166,IND,IND6,39790.686,39790.686,5.570518476042747,-79.93819474,32.774642053,450190001002002,1,centroid,IND6,DR-1F,Neighborhood,
+574296097,574296097,RES,RES1-1SNB,400265.735,200132.867,3.78304317457676,-79.94113564,32.772450975,450190001003023,1,centroid,RES1-1SNB,SR-2,Neighborhood,
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+574371857,574371857,RES,RES3A,480818.909,240409.454,5.197428978509903,-79.93642432,32.771931076,450190001002006,1,centroid,RES3A,SR-2,Neighborhood,
+574373119,574373119,RES,RES3A,677121.911,338560.955,4.5614900290250775,-79.93928608,32.772254657,450190001003021,1,centroid,RES3A,STR,Neighborhood,
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+573783140,573783140,RES,RES1-3SNB,395177.36,197588.68,7.854387454566956,-79.92927399,32.773426703,450190002002001,1,centroid,RES1-3SNB,SR-4,Neighborhood,
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+573387880,573387880,RES,RES1-1SNB,1294023.51,647011.755,10.441292479867936,-79.93187083,32.779462015,450190051002056,1,centroid,RES1-1SNB,GB,City Centers,A-5
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+574268041,574268041,COM,COM10,186797.434,93398.7172,8.962779663572311,-79.92859461,32.780244674,450190051002050,1,centroid,COM10,GB,Neighborhood,A-1
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+573782604,573782604,RES,RES1-1SNB,265634.564,132817.282,6.46012056552887,-79.929988,32.7731275,450190002002005,1,centroid,RES1-1SNB,SR-4,Neighborhood,
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+573783142,573783142,RES,RES1-2SNB,280661.052,140330.526,6.336098314275741,-79.92968705,32.773486552,450190002002001,1,centroid,RES1-2SNB,SR-4,Neighborhood,
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+573783341,573783341,RES,RES1-2SNB,419384.196,209692.098,12.966943953037262,-79.93049453,32.777679672,450190002001021,1,centroid,RES1-2SNB,GB,Neighborhood,
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+573783357,573783357,RES,RES1-2SNB,250638.035,125319.017,12.137243360147476,-79.92983308,32.777886226,450190002001021,1,centroid,RES1-2SNB,SR-5,Neighborhood,
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+573831703,573831703,PUB,GOV1,153865.515,153865.515,11.66566891716957,-79.93123444,32.774908892,450190002001026,1,centroid,GOV1,SR-5,Neighborhood,
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+573795569,573795569,RES,RES1-2SNB,425776.014,212888.007,3.7772074729156495,-79.93971747,32.774851675,450190001003004,1,centroid,RES1-2SNB,STR,Neighborhood,
+573860267,573860267,RES,RES1-3SNB,351072.614,175536.307,3.332380028476715,-79.94056439,32.774559418,450190001003003,1,centroid,RES1-3SNB,STR,Neighborhood,
+573860269,573860269,RES,RES1-2SNB,262068.691,131034.345,3.578634827156067,-79.94087048,32.774635646,450190001003003,1,centroid,RES1-2SNB,STR,Neighborhood,
+573860281,573860281,RES,RES1-2SNB,286122.249,143061.124,3.7722783563661575,-79.94093141,32.774953177,450190001003003,1,centroid,RES1-2SNB,STR,Neighborhood,
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+574270736,574270736,RES,RES3A,93254.6714,46627.3357,3.530285446367264,-79.9412164,32.773203632,450190001003007,1,centroid,RES3A,STR,Neighborhood,
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+573387864,573387864,COM,RES4,6186207.67,3093103.83,10.535970002508163,-79.93191116,32.779781997,450190051002056,1,centroid,RES4,GB,City Centers,A-5
+573467745,573467745,RES,RES3A,516183.793,258091.896,5.893531857123375,-79.92827685,32.772006634,450190002002011,1,centroid,RES3A,SR-3,Neighborhood,
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+573779405,573779405,RES,RES1-3SNB,398598.966,199299.483,10.82060956788063,-79.92862217,32.774811231,450190002002000,1,centroid,RES1-3SNB,SR-5,Neighborhood,
+573779423,573779423,RES,RES1-2SNB,157556.738,78778.3694,9.611099092655182,-79.92800806,32.778884243,450190051002060,1,centroid,RES1-2SNB,SR-5,Neighborhood,
+573779437,573779437,RES,RES1-2SNB,376216.45,188108.225,10.481997290945053,-79.92794544,32.779117807,450190051002060,1,centroid,RES1-2SNB,SR-5,Neighborhood,
+573782594,573782594,RES,RES3A,489787.119,244893.559,6.500304422607422,-79.92975812,32.773115965,450190002002005,1,centroid,RES3A,SR-4,Neighborhood,
+573782596,573782596,RES,RES3A,675402.418,337701.209,6.887255911560058,-79.93036119,32.773184386,450190002002005,1,centroid,RES3A,SR-3,Neighborhood,
+573782413,573782413,RES,RES3A,437002.054,218501.027,7.119138324699402,-79.92871845,32.772191724,450190002002006,1,centroid,RES3A,SR-4,Neighborhood,
+573782415,573782415,RES,RES1-3SNB,369429.359,184714.679,7.403951541404724,-79.92887994,32.772190428,450190002002006,1,centroid,RES1-3SNB,SR-4,Neighborhood,
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+573782453,573782453,RES,RES1-3SNB,448326.4,224163.2,5.318573467359543,-79.928964,32.772909407,450190002002006,1,centroid,RES1-3SNB,SR-4,Neighborhood,
+573782485,573782485,COM,COM8,284018.525,284018.525,11.023419462604522,-79.92914283,32.777854438,450190002001020,1,centroid,COM8,SR-5,Neighborhood,
+573782528,573782528,RES,RES3A,677628.089,338814.044,12.29409979314804,-79.9284514,32.775421312,450190002001032,1,centroid,RES3A,SR-5,Neighborhood,
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+573783175,573783175,RES,RES3A,327754.263,163877.131,9.728631320896149,-79.93046822,32.774471714,450190002002001,1,centroid,RES3A,SR-5,Neighborhood,
+573783185,573783185,RES,RES1-2SNB,438110.47,219055.235,10.557275143737792,-79.93039775,32.77458916,450190002002001,1,centroid,RES1-2SNB,SR-5,Neighborhood,
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+573783325,573783325,RES,RES1-2SNB,241257.251,120628.625,12.280113820934297,-79.9301985,32.778408,450190051002058,1,centroid,RES1-2SNB,SR-5,Neighborhood,
+573783419,573783419,RES,RES1-2SWB,899667.893,449833.946,7.065387761688233,-79.92958184,32.77059715,450190002002010,1,centroid,RES1-2SWB,SR-3,Neighborhood,
+573783425,573783425,RES,RES1-3SWB,615853.155,307926.577,6.638645868902207,-79.92967987,32.770989749,450190002002010,1,centroid,RES1-3SWB,SR-4,Neighborhood,
+573785435,573785435,COM,COM8,988173.174,988173.174,11.90498235581398,-79.93155196,32.777449246,450190002001022,1,centroid,COM8,GB,City Centers,
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+573785950,573785950,RES,RES1-2SNB,472439.483,236219.741,9.125092033720016,-79.93278733,32.773839298,450190002002003,1,centroid,RES1-2SNB,SR-4,Neighborhood,
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+573818743,573818743,RES,RES1-2SNB,280041.982,140020.991,4.967063276786805,-79.92587104,32.776016439,450190002001004,1,centroid,RES1-2SNB,DR-1F,Neighborhood,
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+573827727,573827727,PUB,GOV1,1531018.22,1531018.22,11.172797937307358,-79.93080905,32.776727923,450190002001028,1,centroid,GOV1,LB,Park,
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+573779439,573779439,COM,COM1,150501.212,150501.212,10.481997290945053,-79.92794544,32.779117807,450190051002060,1,centroid,COM1,SR-5,Neighborhood,
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+574372406,574372406,IND,IND6,725153.283,725153.283,8.06563742983818,-79.92772042,32.780506281,450190051002030,1,centroid,IND6,GB,City Centers,A-1
+574372407,574372407,RES,RES3A,60159.5554,30079.7777,10.174292604618072,-79.92813633,32.780414773,450190051002030,1,centroid,RES3A,GB,City Centers,A-1
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+574372452,574372452,COM,COM8,80863.1004,80863.1004,12.799124801883698,-79.92830278,32.776202619,450190002001017,1,centroid,COM8,SR-5,Neighborhood,
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+574421698,574421698,COM,COM5,243965.928,243965.928,10.52724206749916,-79.9290929,32.777519764,450190002001029,1,centroid,COM5,SR-5,Neighborhood,
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+574451488,574451488,COM,COM2,527204.302,527204.302,7.787686569633484,-79.93057458,32.779847015,450190051002051,1,centroid,COM2,GB,Neighborhood,A-1
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+574461302,574461302,RES,RES3A,266985.928,133492.964,7.195152240123749,-79.92673454,32.779105389,450190051002027,1,centroid,RES3A,LB,City Centers,A-1
+574461303,574461303,RES,RES3A,266985.928,133492.964,7.195152240123749,-79.92673454,32.779105389,450190051002027,1,centroid,RES3A,LB,City Centers,A-1
+574461304,574461304,RES,RES3A,266985.928,133492.964,8.221585713300705,-79.92679108,32.778909003,450190051002027,1,centroid,RES3A,LB,City Centers,A-1
+574461305,574461305,RES,RES3A,266985.928,133492.964,6.773325775156021,-79.92639653,32.779118318,450190051002027,1,centroid,RES3A,LB,City Centers,A-1
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+574486822,574486822,RES,RES1-1SNB,214165.178,107082.589,3.892720026645661,-79.94141607,32.775137429,450190001003008,1,centroid,RES1-1SNB,STR,Neighborhood,
diff --git a/examples/data/aggregation_zones/fiat_model/output/aggregation_zones_test2/settings.toml b/examples/data/aggregation_zones/fiat_model/output/aggregation_zones_test2/settings.toml
new file mode 100644
index 00000000..44b60c5b
--- /dev/null
+++ b/examples/data/aggregation_zones/fiat_model/output/aggregation_zones_test2/settings.toml
@@ -0,0 +1,22 @@
+[global]
+crs = "epsg:4326"
+
+[output]
+path = "output"
+
+[output.csv]
+name = "output.csv"
+
+[output.geom]
+name1 = "spatial.gpkg"
+
+[vulnerability]
+file = "./vulnerability/vulnerability_curves.csv"
+unit = "feet"
+step_size = 0.1
+
+[exposure.geom]
+csv = "./exposure/exposure.csv"
+crs = "EPSG:4326"
+unit = "ft"
+file1 = "./exposure/buildings.gpkg"
diff --git a/examples/data/aggregation_zones/fiat_model/output/aggregation_zones_test2/vulnerability/vulnerability_curves.csv b/examples/data/aggregation_zones/fiat_model/output/aggregation_zones_test2/vulnerability/vulnerability_curves.csv
new file mode 100644
index 00000000..6dcbb006
--- /dev/null
+++ b/examples/data/aggregation_zones/fiat_model/output/aggregation_zones_test2/vulnerability/vulnerability_curves.csv
@@ -0,0 +1,37 @@
+#UNIT=feet
+#METHOD,max,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean
+water depth [feet],AGR1,COM1,COM10,COM2,COM3,COM4,COM5,COM6,COM7,COM8,COM9,EDU1,EDU2,GOV1,GOV2,IND1,IND2,IND3,IND4,IND5,IND6,REL1,RES1-1SNB,RES1-1SWB,RES1-2SNB,RES1-2SWB,RES1-3SNB,RES1-3SWB,RES2,RES3A,RES3B,RES3C,RES3D,RES3E,RES3F,RES4,RES5,RES6
+-9.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-8.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1e-06,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-7.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.008,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.021,0.0,0.023,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.037,0.0,0.037,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.057,0.0,0.052,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.08,0.0,0.068,0.0,0.05,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.105,0.0,0.084,0.0,0.07,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.024,0.132,0.01,0.101,0.0,0.08,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+0.0,0.06,0.02,0.0,0.03,0.004,0.0,0.0,0.0,0.0,0.13,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.15,0.02,0.0,0.0,0.081,0.16,0.05,0.119,0.06,0.14,0.03,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+1.0,0.2,0.26,0.11,0.16,0.164,0.2,0.5,0.146,0.28,0.45,0.04,0.217,0.27,0.3,0.08,0.15,0.193,0.2,0.2,0.2,0.2,0.293,0.133,0.189,0.087,0.138,0.11,0.18,0.27,0.217,0.217,0.217,0.217,0.217,0.217,0.161,0.15,0.38
+2.0,0.43,0.42,0.17,0.27,0.289,0.3,0.74,0.27,0.51,0.55,0.06,0.304,0.38,0.59,0.2,0.24,0.31,0.41,0.26,0.41,0.35,0.484,0.179,0.218,0.122,0.157,0.15,0.22,0.49,0.304,0.304,0.304,0.304,0.304,0.304,0.263,0.25,0.6
+3.0,0.58,0.56,0.2,0.36,0.409,0.4,0.83,0.37,0.6,0.64,0.08,0.39,0.53,0.74,0.38,0.34,0.423,0.51,0.31,0.51,0.47,0.6,0.22,0.247,0.155,0.177,0.19,0.25,0.64,0.39,0.39,0.39,0.39,0.39,0.39,0.341,0.4,0.73
+4.0,0.65,0.68,0.23,0.49,0.577,0.575,1.0,0.534,0.63,0.73,0.09,0.45,0.64,0.83,0.55,0.41,0.523,0.62,0.37,0.62,0.56,0.693,0.257,0.274,0.185,0.198,0.23,0.29,0.7,0.45,0.45,0.45,0.45,0.45,0.45,0.397,0.5,0.81
+5.0,0.66,0.78,0.25,0.57,0.633,0.7,1.0,0.7,0.67,0.77,0.1,0.479,0.68,0.9,0.7,0.47,0.607,0.67,0.4,0.67,0.59,0.764,0.288,0.3,0.213,0.22,0.26,0.31,0.76,0.479,0.479,0.479,0.479,0.479,0.479,0.487,0.58,0.88
+6.0,0.66,0.83,0.29,0.63,0.707,0.8,1.0,0.791,0.71,0.8,0.12,0.519,0.7,1.0,0.81,0.52,0.72,0.71,0.44,0.71,0.66,0.814,0.315,0.324,0.239,0.243,0.29,0.34,0.78,0.519,0.519,0.519,0.519,0.519,0.519,0.524,0.65,0.94
+7.0,0.67,0.85,0.35,0.69,0.793,0.838,1.0,0.856,0.72,0.82,0.17,0.557,0.72,1.0,0.89,0.57,0.821,0.73,0.48,0.73,0.69,0.884,0.338,0.345,0.263,0.267,0.32,0.36,0.79,0.557,0.557,0.557,0.557,0.557,0.557,0.584,0.78,1.0
+8.0,0.7,0.87,0.42,0.72,0.843,1.0,1.0,0.925,0.74,0.83,0.22,0.593,0.75,1.0,0.98,0.6,0.907,0.76,0.53,0.76,0.71,0.943,0.357,0.363,0.284,0.291,0.35,0.39,0.81,0.593,0.593,0.593,0.593,0.593,0.593,0.613,0.9,1.0
+9.0,0.75,0.88,0.51,0.76,0.871,1.0,1.0,0.956,0.77,0.85,0.3,0.606,0.79,1.0,1.0,0.63,0.943,0.78,0.56,0.78,0.72,0.971,0.372,0.377,0.303,0.317,0.41,0.44,0.83,0.606,0.606,0.606,0.606,0.606,0.606,0.631,0.9,1.0
+10.0,0.76,0.89,0.63,0.8,0.871,1.0,1.0,0.963,0.81,0.87,0.41,0.634,0.83,1.0,1.0,0.64,0.95,0.79,0.57,0.79,0.78,0.971,0.384,0.386,0.32,0.344,0.43,0.46,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+11.0,0.76,0.9,0.77,0.82,0.871,1.0,1.0,0.963,0.86,0.89,0.57,0.634,0.88,1.0,1.0,0.66,0.95,0.82,0.6,0.82,0.79,0.971,0.392,0.391,0.334,0.372,0.45,0.47,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+12.0,0.76,0.91,0.93,0.84,0.871,1.0,1.0,0.963,0.92,0.9,0.66,0.634,0.94,1.0,1.0,0.68,0.95,0.83,0.62,0.83,0.8,0.971,0.397,0.391,0.347,0.4,0.48,0.5,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+13.0,0.77,0.92,1.0,0.86,0.871,1.0,1.0,0.963,0.94,0.91,0.73,0.634,1.0,1.0,1.0,0.69,0.95,0.84,0.63,0.84,0.8,0.971,0.4,0.391,0.356,0.43,0.5,0.52,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+14.0,0.77,0.92,1.0,0.87,0.871,1.0,1.0,0.963,0.97,0.92,0.79,0.634,1.0,1.0,1.0,0.72,0.95,0.86,0.63,0.86,0.81,0.971,0.4,0.391,0.364,0.461,0.52,0.53,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+15.0,0.77,0.92,1.0,0.87,0.871,1.0,1.0,0.963,0.99,0.93,0.84,0.634,1.0,1.0,1.0,0.73,0.95,0.87,0.63,0.87,0.81,0.971,0.4,0.391,0.369,0.493,0.54,0.55,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+16.0,0.78,0.93,1.0,0.88,0.871,1.0,1.0,0.963,1.0,0.94,0.9,0.634,1.0,1.0,1.0,0.73,0.95,0.87,0.64,0.87,0.81,0.971,0.4,0.391,0.372,0.526,0.56,0.57,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+17.0,0.78,0.93,1.0,0.89,0.871,1.0,1.0,0.963,1.0,0.95,0.97,0.634,1.0,1.0,1.0,0.73,0.95,0.88,0.65,0.88,0.82,0.971,0.4,0.391,0.372,0.526,0.58,0.58,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+18.0,0.78,0.94,1.0,0.89,0.871,1.0,1.0,0.963,1.0,0.96,0.98,0.634,1.0,1.0,1.0,0.74,0.95,0.88,0.65,0.88,0.82,0.971,0.4,0.391,0.372,0.526,0.6,0.6,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+19.0,0.79,0.94,1.0,0.89,0.871,1.0,1.0,0.963,1.0,0.96,1.0,0.634,1.0,1.0,1.0,0.74,0.95,0.88,0.65,0.88,0.82,0.971,0.4,0.391,0.372,0.526,0.6,0.6,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+20.0,0.79,0.94,1.0,0.89,0.871,1.0,1.0,0.963,1.0,0.96,1.0,0.634,1.0,1.0,1.0,0.74,0.95,0.88,0.65,0.88,0.83,0.971,0.4,0.391,0.372,0.526,0.6,0.6,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+21.0,0.79,0.94,1.0,0.89,0.871,1.0,1.0,0.963,1.0,0.96,1.0,0.634,1.0,1.0,1.0,0.74,0.95,0.88,0.65,0.88,0.83,0.971,0.4,0.391,0.372,0.526,0.6,0.6,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+22.0,0.79,0.94,1.0,0.89,0.871,1.0,1.0,0.963,1.0,0.96,1.0,0.634,1.0,1.0,1.0,0.75,0.95,0.88,0.65,0.88,0.83,0.971,0.4,0.391,0.372,0.526,0.6,0.6,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+23.0,0.8,0.94,1.0,0.89,0.871,1.0,1.0,0.963,1.0,0.96,1.0,0.634,1.0,1.0,1.0,0.75,0.95,0.88,0.65,0.88,0.83,0.971,0.4,0.391,0.372,0.526,0.6,0.6,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+24.0,0.8,0.94,1.0,0.89,0.871,1.0,1.0,0.963,1.0,0.96,1.0,0.634,1.0,1.0,1.0,0.75,0.95,0.88,0.65,0.88,0.83,0.971,0.4,0.391,0.372,0.526,0.6,0.6,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
diff --git a/examples/data/aggregation_zones/fiat_model/settings.toml b/examples/data/aggregation_zones/fiat_model/settings.toml
new file mode 100644
index 00000000..3ae7d2ea
--- /dev/null
+++ b/examples/data/aggregation_zones/fiat_model/settings.toml
@@ -0,0 +1,24 @@
+[global]
+crs = "epsg:4326"
+
+[output]
+path = "output"
+
+[output.csv]
+name = "output.csv"
+
+[output.geom]
+name1 = "spatial.gpkg"
+
+[vulnerability]
+file = "./vulnerability/vulnerability_curves.csv"
+unit = "feet"
+step_size = 0.1
+
+[exposure.csv]
+file = "./exposure/exposure.csv"
+
+[exposure.geom]
+crs = "EPSG:4326"
+unit = "ft"
+file1 = "./exposure/buildings.gpkg"
diff --git a/examples/data/aggregation_zones/fiat_model/vulnerability/vulnerability_curves.csv b/examples/data/aggregation_zones/fiat_model/vulnerability/vulnerability_curves.csv
new file mode 100644
index 00000000..6dcbb006
--- /dev/null
+++ b/examples/data/aggregation_zones/fiat_model/vulnerability/vulnerability_curves.csv
@@ -0,0 +1,37 @@
+#UNIT=feet
+#METHOD,max,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean
+water depth [feet],AGR1,COM1,COM10,COM2,COM3,COM4,COM5,COM6,COM7,COM8,COM9,EDU1,EDU2,GOV1,GOV2,IND1,IND2,IND3,IND4,IND5,IND6,REL1,RES1-1SNB,RES1-1SWB,RES1-2SNB,RES1-2SWB,RES1-3SNB,RES1-3SWB,RES2,RES3A,RES3B,RES3C,RES3D,RES3E,RES3F,RES4,RES5,RES6
+-9.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-8.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1e-06,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-7.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.008,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.021,0.0,0.023,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.037,0.0,0.037,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.057,0.0,0.052,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.08,0.0,0.068,0.0,0.05,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.105,0.0,0.084,0.0,0.07,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.024,0.132,0.01,0.101,0.0,0.08,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+0.0,0.06,0.02,0.0,0.03,0.004,0.0,0.0,0.0,0.0,0.13,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.15,0.02,0.0,0.0,0.081,0.16,0.05,0.119,0.06,0.14,0.03,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+1.0,0.2,0.26,0.11,0.16,0.164,0.2,0.5,0.146,0.28,0.45,0.04,0.217,0.27,0.3,0.08,0.15,0.193,0.2,0.2,0.2,0.2,0.293,0.133,0.189,0.087,0.138,0.11,0.18,0.27,0.217,0.217,0.217,0.217,0.217,0.217,0.161,0.15,0.38
+2.0,0.43,0.42,0.17,0.27,0.289,0.3,0.74,0.27,0.51,0.55,0.06,0.304,0.38,0.59,0.2,0.24,0.31,0.41,0.26,0.41,0.35,0.484,0.179,0.218,0.122,0.157,0.15,0.22,0.49,0.304,0.304,0.304,0.304,0.304,0.304,0.263,0.25,0.6
+3.0,0.58,0.56,0.2,0.36,0.409,0.4,0.83,0.37,0.6,0.64,0.08,0.39,0.53,0.74,0.38,0.34,0.423,0.51,0.31,0.51,0.47,0.6,0.22,0.247,0.155,0.177,0.19,0.25,0.64,0.39,0.39,0.39,0.39,0.39,0.39,0.341,0.4,0.73
+4.0,0.65,0.68,0.23,0.49,0.577,0.575,1.0,0.534,0.63,0.73,0.09,0.45,0.64,0.83,0.55,0.41,0.523,0.62,0.37,0.62,0.56,0.693,0.257,0.274,0.185,0.198,0.23,0.29,0.7,0.45,0.45,0.45,0.45,0.45,0.45,0.397,0.5,0.81
+5.0,0.66,0.78,0.25,0.57,0.633,0.7,1.0,0.7,0.67,0.77,0.1,0.479,0.68,0.9,0.7,0.47,0.607,0.67,0.4,0.67,0.59,0.764,0.288,0.3,0.213,0.22,0.26,0.31,0.76,0.479,0.479,0.479,0.479,0.479,0.479,0.487,0.58,0.88
+6.0,0.66,0.83,0.29,0.63,0.707,0.8,1.0,0.791,0.71,0.8,0.12,0.519,0.7,1.0,0.81,0.52,0.72,0.71,0.44,0.71,0.66,0.814,0.315,0.324,0.239,0.243,0.29,0.34,0.78,0.519,0.519,0.519,0.519,0.519,0.519,0.524,0.65,0.94
+7.0,0.67,0.85,0.35,0.69,0.793,0.838,1.0,0.856,0.72,0.82,0.17,0.557,0.72,1.0,0.89,0.57,0.821,0.73,0.48,0.73,0.69,0.884,0.338,0.345,0.263,0.267,0.32,0.36,0.79,0.557,0.557,0.557,0.557,0.557,0.557,0.584,0.78,1.0
+8.0,0.7,0.87,0.42,0.72,0.843,1.0,1.0,0.925,0.74,0.83,0.22,0.593,0.75,1.0,0.98,0.6,0.907,0.76,0.53,0.76,0.71,0.943,0.357,0.363,0.284,0.291,0.35,0.39,0.81,0.593,0.593,0.593,0.593,0.593,0.593,0.613,0.9,1.0
+9.0,0.75,0.88,0.51,0.76,0.871,1.0,1.0,0.956,0.77,0.85,0.3,0.606,0.79,1.0,1.0,0.63,0.943,0.78,0.56,0.78,0.72,0.971,0.372,0.377,0.303,0.317,0.41,0.44,0.83,0.606,0.606,0.606,0.606,0.606,0.606,0.631,0.9,1.0
+10.0,0.76,0.89,0.63,0.8,0.871,1.0,1.0,0.963,0.81,0.87,0.41,0.634,0.83,1.0,1.0,0.64,0.95,0.79,0.57,0.79,0.78,0.971,0.384,0.386,0.32,0.344,0.43,0.46,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+11.0,0.76,0.9,0.77,0.82,0.871,1.0,1.0,0.963,0.86,0.89,0.57,0.634,0.88,1.0,1.0,0.66,0.95,0.82,0.6,0.82,0.79,0.971,0.392,0.391,0.334,0.372,0.45,0.47,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+12.0,0.76,0.91,0.93,0.84,0.871,1.0,1.0,0.963,0.92,0.9,0.66,0.634,0.94,1.0,1.0,0.68,0.95,0.83,0.62,0.83,0.8,0.971,0.397,0.391,0.347,0.4,0.48,0.5,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+13.0,0.77,0.92,1.0,0.86,0.871,1.0,1.0,0.963,0.94,0.91,0.73,0.634,1.0,1.0,1.0,0.69,0.95,0.84,0.63,0.84,0.8,0.971,0.4,0.391,0.356,0.43,0.5,0.52,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+14.0,0.77,0.92,1.0,0.87,0.871,1.0,1.0,0.963,0.97,0.92,0.79,0.634,1.0,1.0,1.0,0.72,0.95,0.86,0.63,0.86,0.81,0.971,0.4,0.391,0.364,0.461,0.52,0.53,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+15.0,0.77,0.92,1.0,0.87,0.871,1.0,1.0,0.963,0.99,0.93,0.84,0.634,1.0,1.0,1.0,0.73,0.95,0.87,0.63,0.87,0.81,0.971,0.4,0.391,0.369,0.493,0.54,0.55,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+16.0,0.78,0.93,1.0,0.88,0.871,1.0,1.0,0.963,1.0,0.94,0.9,0.634,1.0,1.0,1.0,0.73,0.95,0.87,0.64,0.87,0.81,0.971,0.4,0.391,0.372,0.526,0.56,0.57,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+17.0,0.78,0.93,1.0,0.89,0.871,1.0,1.0,0.963,1.0,0.95,0.97,0.634,1.0,1.0,1.0,0.73,0.95,0.88,0.65,0.88,0.82,0.971,0.4,0.391,0.372,0.526,0.58,0.58,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+18.0,0.78,0.94,1.0,0.89,0.871,1.0,1.0,0.963,1.0,0.96,0.98,0.634,1.0,1.0,1.0,0.74,0.95,0.88,0.65,0.88,0.82,0.971,0.4,0.391,0.372,0.526,0.6,0.6,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+19.0,0.79,0.94,1.0,0.89,0.871,1.0,1.0,0.963,1.0,0.96,1.0,0.634,1.0,1.0,1.0,0.74,0.95,0.88,0.65,0.88,0.82,0.971,0.4,0.391,0.372,0.526,0.6,0.6,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+20.0,0.79,0.94,1.0,0.89,0.871,1.0,1.0,0.963,1.0,0.96,1.0,0.634,1.0,1.0,1.0,0.74,0.95,0.88,0.65,0.88,0.83,0.971,0.4,0.391,0.372,0.526,0.6,0.6,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+21.0,0.79,0.94,1.0,0.89,0.871,1.0,1.0,0.963,1.0,0.96,1.0,0.634,1.0,1.0,1.0,0.74,0.95,0.88,0.65,0.88,0.83,0.971,0.4,0.391,0.372,0.526,0.6,0.6,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+22.0,0.79,0.94,1.0,0.89,0.871,1.0,1.0,0.963,1.0,0.96,1.0,0.634,1.0,1.0,1.0,0.75,0.95,0.88,0.65,0.88,0.83,0.971,0.4,0.391,0.372,0.526,0.6,0.6,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+23.0,0.8,0.94,1.0,0.89,0.871,1.0,1.0,0.963,1.0,0.96,1.0,0.634,1.0,1.0,1.0,0.75,0.95,0.88,0.65,0.88,0.83,0.971,0.4,0.391,0.372,0.526,0.6,0.6,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+24.0,0.8,0.94,1.0,0.89,0.871,1.0,1.0,0.963,1.0,0.96,1.0,0.634,1.0,1.0,1.0,0.75,0.95,0.88,0.65,0.88,0.83,0.971,0.4,0.391,0.372,0.526,0.6,0.6,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
diff --git a/examples/data/aggregation_zones/fiat_model/vulnerability/vulnerability_identifiers.csv b/examples/data/aggregation_zones/fiat_model/vulnerability/vulnerability_identifiers.csv
new file mode 100644
index 00000000..c34b6f8d
--- /dev/null
+++ b/examples/data/aggregation_zones/fiat_model/vulnerability/vulnerability_identifiers.csv
@@ -0,0 +1,77 @@
+FIAT Damage Function Name,Exposure Link,Damage Type,ID,Occupancy,Description,Source,full_identifier
+AGR1,AGR1,structure,616,AGR1,"Average Agriculture, structure",USACE - Galveston,"616AGR1AverageAgriculture,structureUSACE-Galveston"
+COM1,COM1,structure,217,COM1,"Average Retail, Structure",USACE - Galveston,"217COM1AverageRetail,StructureUSACE-Galveston"
+COM10,COM10,structure,543,COM10,"Garage, structure",USACE - Galveston,"543COM10Garage,structureUSACE-Galveston"
+COM2,COM2,structure,341,COM2,"Average wholesale, Structure",USACE - Galveston,"341COM2Averagewholesale,StructureUSACE-Galveston"
+COM3,COM3,structure,32,COM3-S-MostLikely,Average Personal & Repair Services,USACE - IWR,32COM3-S-MostLikelyAveragePersonal&RepairServicesUSACE-IWR
+COM4,COM4,structure,35,COM4-S-MostLikely,Average Prof/Tech Services,USACE - IWR,35COM4-S-MostLikelyAverageProf/TechServicesUSACE-IWR
+COM5,COM5,structure,467,COM5,"Bank, Structure",USACE - Galveston,"467COM5Bank,StructureUSACE-Galveston"
+COM6,COM6,structure,38,COM6-S-MostLikely,Hospital,USACE - IWR,38COM6-S-MostLikelyHospitalUSACE-IWR
+COM7,COM7,structure,475,COM7,"Average Medical Office, Structure",USACE - Galveston,"475COM7AverageMedicalOffice,StructureUSACE-Galveston"
+COM8,COM8,structure,493,COM8,"Average Entertainment/Recreation, Structure",USACE - Galveston,"493COM8AverageEntertainment/Recreation,StructureUSACE-Galveston"
+COM9,COM9,structure,532,COM9,"Average theatre, Structure",USACE - Galveston,"532COM9Averagetheatre,StructureUSACE-Galveston"
+EDU1,EDU1,structure,53,EDU1-S-MostLikely,Average school,USACE - IWR,53EDU1-S-MostLikelyAverageschoolUSACE-IWR
+EDU2,EDU2,structure,652,EDU2,"Average college/university, structure",USACE - Galveston,"652EDU2Averagecollege/university,structureUSACE-Galveston"
+GOV1,GOV1,structure,631,GOV1,"Average government services, structure",USACE - Galveston,"631GOV1Averagegovernmentservices,structureUSACE-Galveston"
+GOV2,GOV2,structure,640,GOV2,"Average emergency response, Structure",USACE - Galveston,"640GOV2Averageemergencyresponse,StructureUSACE-Galveston"
+IND1,IND1,structure,545,IND1,"Average heavy industrial, Structure",USACE - Galveston,"545IND1Averageheavyindustrial,StructureUSACE-Galveston"
+IND2,IND2,structure,44,IND2-S-MostLikely,Average light industrial,USACE - IWR,44IND2-S-MostLikelyAveragelightindustrialUSACE-IWR
+IND3,IND3,structure,575,IND3,"Average Food/Drug/Chem, Structure",USACE - Galveston,"575IND3AverageFood/Drug/Chem,StructureUSACE-Galveston"
+IND4,IND4,structure,586,IND4,"Average Metals/Minerals processing, structure",USACE - Galveston,"586IND4AverageMetals/Mineralsprocessing,structureUSACE-Galveston"
+IND5,IND5,structure,591,IND5,"Average High Technology, structure",USACE - Galveston,"591IND5AverageHighTechnology,structureUSACE-Galveston"
+IND6,IND6,structure,592,IND6,"Average Construction, structure",USACE - Galveston,"592IND6AverageConstruction,structureUSACE-Galveston"
+REL1,REL1,structure,47,REL1-S-MostLikely,Church,USACE - IWR,47REL1-S-MostLikelyChurchUSACE-IWR
+RES1-1SNB,RES1-1SNB,structure,2,RES1-1SNB-S-MostLikely,"Res 1, 1 Story no Basement",USACE - IWR,"2RES1-1SNB-S-MostLikelyRes1,1StorynoBasementUSACE-IWR"
+RES1-1SWB,RES1-1SWB,structure,5,RES1-1SWB-S-MostLikely,"Res 1, 1 Story w/ Basement",USACE - IWR,"5RES1-1SWB-S-MostLikelyRes1,1Storyw/BasementUSACE-IWR"
+RES1-2SNB,RES1-2SNB,structure,8,RES1-2SNB-S-MostLikely,"Res 1, 2 Story no Basement",USACE - IWR,"8RES1-2SNB-S-MostLikelyRes1,2StorynoBasementUSACE-IWR"
+RES1-2SWB,RES1-2SWB,structure,11,RES1-2SWB-S-MostLikely,"Res 1, 2 Story w/ Basement",USACE - IWR,"11RES1-2SWB-S-MostLikelyRes1,2Storyw/BasementUSACE-IWR"
+RES1-3SNB,RES1-3SNB,structure,117,RES1,"three or more floors, no basement, Structure, V-Zone",FIA,"117RES1threeormorefloors,nobasement,Structure,V-ZoneFIA"
+RES1-3SWB,RES1-3SWB,structure,127,RES1,"three or more floors, w/ basement, Structure, V-Zone",FIA,"127RES1threeormorefloors,w/basement,Structure,V-ZoneFIA"
+RES2,RES2,structure,20,RES2-S-MostLikely,Mobile home,USACE - IWR,20RES2-S-MostLikelyMobilehomeUSACE-IWR
+RES3A,RES3A,structure,23,RES3-S-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"23RES3-S-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR"
+RES3B,RES3B,structure,23,RES3-S-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"23RES3-S-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR"
+RES3C,RES3C,structure,23,RES3-S-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"23RES3-S-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR"
+RES3D,RES3D,structure,23,RES3-S-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"23RES3-S-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR"
+RES3E,RES3E,structure,23,RES3-S-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"23RES3-S-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR"
+RES3F,RES3F,structure,23,RES3-S-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"23RES3-S-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR"
+RES4,RES4,structure,26,RES4-S-MostLikely,"Average Hotel, & Motel",USACE - IWR,"26RES4-S-MostLikelyAverageHotel,&MotelUSACE-IWR"
+RES5,RES5,structure,50,RES5-PRO-SVC-S-MostLikely,Nursing Home,USACE - IWR,50RES5-PRO-SVC-S-MostLikelyNursingHomeUSACE-IWR
+RES6,RES6,structure,215,RES6,"Nursing Home, structure",USACE - Galveston,"215RES6NursingHome,structureUSACE-Galveston"
+AGR1,AGR1,content,460,AGR1 ,"Average Agriculture, Contents (Equipment/Inventory)",USACE - Galveston,"460AGR1AverageAgriculture,Contents(Equipment/Inventory)USACE-Galveston"
+COM1,COM1,content,90,COM1 ,Average retail trade contents (Inventory/Equipment),USACE - Galveston,90COM1Averageretailtradecontents(Inventory/Equipment)USACE-Galveston
+COM10,COM10,content,357,COM10,"Garage, Contents (Inventory)",USACE - Galveston,"357COM10Garage,Contents(Inventory)USACE-Galveston"
+COM2,COM2,content,195,COM2 ,"Average wholesale trade, Contents",USACE - Galveston,"195COM2Averagewholesaletrade,ContentsUSACE-Galveston"
+COM3,COM3,content,92,COM3-C-MostLikely,Average Personal & Repair Services,USACE - IWR,92COM3-C-MostLikelyAveragePersonal&RepairServicesUSACE-IWR
+COM4,COM4,content,95,COM4-C-MostLikely,Average Prof/Tech Services,USACE - IWR,95COM4-C-MostLikelyAverageProf/TechServicesUSACE-IWR
+COM5,COM5,content,304,COM5 ,"Bank, Contents",USACE - Galveston,"304COM5Bank,ContentsUSACE-Galveston"
+COM6,COM6,content,98,COM6-C-MostLikely,Hospital,USACE - IWR,98COM6-C-MostLikelyHospitalUSACE-IWR
+COM7,COM7,content,312,COM7 ,"Average Medical Offic/Clinic, Contents (Equipment/Inventory)",USACE - Galveston,"312COM7AverageMedicalOffic/Clinic,Contents(Equipment/Inventory)USACE-Galveston"
+COM8,COM8,content,322,COM8 ,"Average Entertainment/Recreation, Contents (Equipment/Inventory)",USACE - Galveston,"322COM8AverageEntertainment/Recreation,Contents(Equipment/Inventory)USACE-Galveston"
+COM9,COM9,content,352,COM9 ,"Theater, Contents (Equipment)",USACE - Galveston,"352COM9Theater,Contents(Equipment)USACE-Galveston"
+EDU1,EDU1,content,113,EDU1-C-MostLikely,Average school,USACE - IWR,113EDU1-C-MostLikelyAverageschoolUSACE-IWR
+EDU2,EDU2,content,485,EDU2 ,"Average College/University, Contents (Equipment/Inventory)",USACE - Galveston,"485EDU2AverageCollege/University,Contents(Equipment/Inventory)USACE-Galveston"
+GOV1,GOV1,content,472,GOV1 ,"Average Govt Services, contents (Equipment)",USACE - Galveston,"472GOV1AverageGovtServices,contents(Equipment)USACE-Galveston"
+GOV2,GOV2,content,477,GOV2 ,"Average emergency response, contents (Equipment/Inventory)",USACE - Galveston,"477GOV2Averageemergencyresponse,contents(Equipment/Inventory)USACE-Galveston"
+IND1,IND1,content,358,IND1 ,"Average Heavy Industrial, Contents (Equipment/Inventory)",USACE - Galveston,"358IND1AverageHeavyIndustrial,Contents(Equipment/Inventory)USACE-Galveston"
+IND2,IND2,content,104,IND2-C-MostLikely,Average light industrial,USACE - IWR,104IND2-C-MostLikelyAveragelightindustrialUSACE-IWR
+IND3,IND3,content,408,IND3 ,"Average Food/Drugs/Chemicals, Contents (Equipment/Inventory)",USACE - Galveston,"408IND3AverageFood/Drugs/Chemicals,Contents(Equipment/Inventory)USACE-Galveston"
+IND4,IND4,content,433,IND4 ,"Average Metals/Minerals Processing, Contents (Equipment/Inventory)",USACE - Galveston,"433IND4AverageMetals/MineralsProcessing,Contents(Equipment/Inventory)USACE-Galveston"
+IND5,IND5,content,442,IND5 ,"Average High Technology, contents (Equipment/Inventory)",USACE - Galveston,"442IND5AverageHighTechnology,contents(Equipment/Inventory)USACE-Galveston"
+IND6,IND6,content,443,IND6 ,"Average Construction, contents (Equipment/Inventory)",USACE - Galveston,"443IND6AverageConstruction,contents(Equipment/Inventory)USACE-Galveston"
+REL1,REL1,content,107,REL1-C-MostLikely,Church,USACE - IWR,107REL1-C-MostLikelyChurchUSACE-IWR
+RES1-1SNB,RES1-1SNB,content,62,RES1-1SNB-C-MostLikely,"Res 1, 1 Story no Basement",USACE - IWR,"62RES1-1SNB-C-MostLikelyRes1,1StorynoBasementUSACE-IWR"
+RES1-1SWB,RES1-1SWB,content,65,RES1-1SWB-C-MostLikely,"Res 1, 1 Story w/ Basement",USACE - IWR,"65RES1-1SWB-C-MostLikelyRes1,1Storyw/BasementUSACE-IWR"
+RES1-2SNB,RES1-2SNB,content,68,RES1-2SNB-C-MostLikely,"Res 1, 2 Story no Basement",USACE - IWR,"68RES1-2SNB-C-MostLikelyRes1,2StorynoBasementUSACE-IWR"
+RES1-2SWB,RES1-2SWB,content,71,RES1-2SWB-C-MostLikely,"Res 1, 2 Story w/ Basement",USACE - IWR,"71RES1-2SWB-C-MostLikelyRes1,2Storyw/BasementUSACE-IWR"
+RES1-3SNB,RES1-3SNB,content,33,RES1 ,"three or more floors, no basement, Contents, V-Zone",FIA (MOD.),"33RES1threeormorefloors,nobasement,Contents,V-ZoneFIA(MOD.)"
+RES1-3SWB,RES1-3SWB,content,34,RES1 ,"three or more floors, w/ basement, Contents, V-Zone",FIA (MOD.),"34RES1threeormorefloors,w/basement,Contents,V-ZoneFIA(MOD.)"
+RES2,RES2,content,80,RES2-C-MostLikely,Mobile home,USACE - IWR,80RES2-C-MostLikelyMobilehomeUSACE-IWR
+RES3A,RES3A,content,83,RES3-C-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"83RES3-C-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR"
+RES3B,RES3B,content,83,RES3-C-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"83RES3-C-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR"
+RES3C,RES3C,content,83,RES3-C-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"83RES3-C-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR"
+RES3D,RES3D,content,83,RES3-C-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"83RES3-C-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR"
+RES3E,RES3E,content,83,RES3-C-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"83RES3-C-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR"
+RES3F,RES3F,content,83,RES3-C-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"83RES3-C-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR"
+RES4,RES4,content,86,RES4-C-MostLikely,"Average Hotel, & Motel",USACE - IWR,"86RES4-C-MostLikelyAverageHotel,&MotelUSACE-IWR"
+RES5,RES5,content,110,RES5-PRO-SVC-C-MostLikely,Nursing Home,USACE - IWR,110RES5-PRO-SVC-C-MostLikelyNursingHomeUSACE-IWR
+RES6,RES6,content,89,RES6 ,"Nursing Home, contents (Equipment)",USACE - Galveston,"89RES6NursingHome,contents(Equipment)USACE-Galveston"
diff --git a/examples/data/aggregation_zones/fiat_model_aggregation1/exposure/buildings.gpkg b/examples/data/aggregation_zones/fiat_model_aggregation1/exposure/buildings.gpkg
new file mode 100644
index 00000000..8a4c0264
Binary files /dev/null and b/examples/data/aggregation_zones/fiat_model_aggregation1/exposure/buildings.gpkg differ
diff --git a/examples/data/aggregation_zones/fiat_model_aggregation1/exposure/exposure.csv b/examples/data/aggregation_zones/fiat_model_aggregation1/exposure/exposure.csv
new file mode 100644
index 00000000..d63b6970
--- /dev/null
+++ b/examples/data/aggregation_zones/fiat_model_aggregation1/exposure/exposure.csv
@@ -0,0 +1,3082 @@
+Object ID,Object Name,Primary Object Type,Secondary Object Type,Max Potential Damage: Structure,Max Potential Damage: Content,Ground Elevation,X Coordinate,Y Coordinate,Aggregation Label: Census Block,Ground Floor Height,Extraction Method,Damage Function: Structure,Aggregation Label: Zoning_map
+573783417,573783417,COM,RES4,1177614.2,588807.1,8.381748561544418,-79.93000008,32.770545239,450190002002010,1,centroid,RES4,SR-3
+573783433,573783433,COM,RES4,476216.254,238108.127,7.374183635444641,-79.92968382,32.771271301,450190002002010,1,centroid,RES4,SR-4
+573785665,573785665,RES,RES1-2SNB,330007.42,165003.71,7.364031290178299,-79.93179589,32.773364727,450190002002004,1,centroid,RES1-2SNB,SR-5
+574346729,574346729,RES,RES3A,472285.148,236142.574,10.902030923233031,-79.93064525,32.774854333,450190002001030,1,centroid,RES3A,SR-5
+574372707,574372707,RES,RES1-3SNB,389363.808,194681.904,6.365223632082939,-79.93187273,32.770989143,450190002002008,1,centroid,RES1-3SNB,SR-3
+574372721,574372721,RES,RES3A,503924.421,251962.21,7.1200097102165225,-79.93191719,32.771641938,450190002002008,1,centroid,RES3A,SR-4
+574476728,574476728,RES,RES1-1SNB,96964.414,48482.207,5.8671571920728685,-79.93241085,32.769968112,450190002002018,1,centroid,RES1-1SNB,GB
+574476811,574476811,RES,RES1-1SNB,152049.256,76024.6281,5.8671571920728685,-79.93241085,32.769968112,450190002002018,1,centroid,RES1-1SNB,GB
+574486899,574486899,RES,RES3A,480189.335,240094.667,12.333664919576645,-79.93027436,32.775360757,450190002001030,1,centroid,RES3A,SR-3
+574486932,574486932,RES,RES1-1SNB,177705.21,88852.6051,6.46012056552887,-79.92998998,32.773144664,450190002002005,1,centroid,RES1-1SNB,SR-4
+574372378,574372378,RES,RES1-3SNB,275317.367,137658.683,11.553864836273194,-79.92697274,32.77580358,450190002001011,1,centroid,RES1-3SNB,DR-1F
+574421614,574421614,PUB,REL1,1834696.67,1834696.67,-4.999869298858642,-79.92477287,32.775260615,450190002001002,1,centroid,REL1,DR-1F
+574437143,574437143,COM,COM2,642736.815,642736.815,9.078115437116622,-79.92640322,32.776823374,450190002001034,1,centroid,COM2,LB
+574446459,574446459,RES,RES1-3SNB,483801.666,241900.833,-4.999869298858642,-79.9250907,32.774485627,450190002001014,1,centroid,RES1-3SNB,DR-1F
+574488867,574488867,RES,RES1-2SNB,261232.301,130616.15,9.352340615797043,-79.92766193,32.773486846,450190002002000,1,centroid,RES1-2SNB,SR-3
+573779435,573779435,COM,COM8,2419416.79,2419416.79,9.368649761247635,-79.92740748,32.778806247,450190051002060,1,centroid,COM8,LB
+573783353,573783353,RES,RES1-3SNB,303173.642,151586.821,12.800906683578493,-79.9304724,32.778022319,450190002001021,1,centroid,RES1-3SNB,GB
+573827729,573827729,COM,COM8,37755.4885,37755.4885,11.172797937307358,-79.93080905,32.776727923,450190002001028,1,centroid,COM8,LB
+574421669,574421669,RES,RES3A,170840.757,85420.3788,11.520179606552125,-79.92907633,32.777112595,450190002001029,1,centroid,RES3A,LB
+574451480,574451480,COM,COM5,833172.397,833172.397,6.471536185131073,-79.93007513,32.780377243,450190051002051,1,centroid,COM5,GB
+574461033,574461033,RES,RES3A,455033.853,227516.926,9.464215878095628,-79.92820274,32.778662139,450190051002049,1,centroid,RES3A,SR-5
+574488693,574488693,RES,RES1-1SNB,209832.08,104916.04,6.4312111385679245,-79.92587144,32.779767241,450190051002027,1,centroid,RES1-1SNB,MU-2
+574488726,574488726,RES,RES1-1SNB,224902.098,112451.049,9.339365263051986,-79.92670704,32.778289721,450190002001008,1,centroid,RES1-1SNB,LB
+574488773,574488773,RES,RES1-1SNB,203264.754,101632.377,12.694521775808337,-79.92806298,32.777600684,450190002001029,1,centroid,RES1-1SNB,SR-5
+574488781,574488781,RES,RES1-3SNB,374871.082,187435.541,5.998982020106316,-79.92578441,32.77724701,450190002001009,1,centroid,RES1-3SNB,PUD
+573387882,573387882,PUB,EDU1,68439.9689,68439.9689,10.441292479867936,-79.93187083,32.779462015,450190051002056,1,centroid,EDU1,GB
+573467952,573467952,RES,RES3A,362545.684,181272.842,8.01299291630745,-79.92942383,32.782461503,450190051002045,1,centroid,RES3A,CT
+573467954,573467954,RES,RES1-1SNB,199509.532,99754.7661,8.378793360033034,-79.92952055,32.782575733,450190051002045,1,centroid,RES1-1SNB,STR
+573467966,573467966,RES,RES1-1SNB,120393.335,60196.6675,9.856602184438705,-79.9300262,32.782961292,450190051002045,1,centroid,RES1-1SNB,STR
+573782570,573782570,RES,RES1-2SNB,319100.1,159550.05,8.620308728866577,-79.9296748,32.772410417,450190002002005,1,centroid,RES1-2SNB,SR-4
+573783130,573783130,RES,RES1-2SNB,359597.275,179798.637,12.57236197421074,-79.9295957,32.774689891,450190002002001,1,centroid,RES1-2SNB,SR-5
+573783164,573783164,RES,RES1-3SNB,456923.213,228461.606,8.737245692853927,-79.93049625,32.774344859,450190002002001,1,centroid,RES1-3SNB,SR-5
+573783333,573783333,RES,RES3A,503261.657,251630.828,10.19067214925766,-79.92949789,32.777677463,450190002001021,1,centroid,RES3A,SR-5
+573783427,573783427,RES,RES1-3SWB,780064.073,390032.036,7.003932398548126,-79.93010789,32.771055204,450190002002010,1,centroid,RES1-3SWB,SR-3
+573783441,573783441,RES,RES1-2SNB,438450.77,219225.385,7.641946950788498,-79.9295781,32.771610827,450190002002010,1,centroid,RES1-2SNB,SR-4
+573783447,573783447,RES,RES3A,483101.436,241550.718,8.568949391641617,-79.9295716,32.77192977,450190002002010,1,centroid,RES3A,SR-4
+573785448,573785448,COM,COM1,570343.785,570343.785,11.880905050964357,-79.93178469,32.776556116,450190002001022,1,centroid,COM1,LB
+573785816,573785816,RES,RES1-2SNB,342424.316,171212.158,10.153921425962448,-79.93316494,32.774836158,450190002001025,1,centroid,RES1-2SNB,SR-4
+573785864,573785864,RES,RES1-2SNB,160586.772,80293.3862,11.295057862195968,-79.93294774,32.776004938,450190002001025,1,centroid,RES1-2SNB,SR-5
+573785925,573785925,RES,RES3A,719359.22,359679.61,6.964915603580475,-79.93359647,32.773110601,450190002002003,1,centroid,RES3A,SR-3
+573785962,573785962,RES,RES1-2SNB,316735.013,158367.506,9.731189158096312,-79.93269833,32.774102068,450190002002003,1,centroid,RES1-2SNB,SR-5
+573789014,573789014,RES,RES1-3SNB,556818.256,278409.128,6.49139618745327,-79.93414417,32.77250666,450190001002004,1,centroid,RES1-3SNB,SR-3
+573789019,573789019,RES,RES1-2SNB,286631.996,143315.998,5.509031042160988,-79.93546136,32.772875167,450190001002004,1,centroid,RES1-2SNB,DR-1F
+573789026,573789026,RES,RES3A,558479.221,279239.61,6.404198187537193,-79.93555996,32.773442523,450190001002004,1,centroid,RES3A,DR-1F
+573789160,573789160,COM,RES4,377058.032,188529.016,6.820185039777756,-79.93504335,32.775873827,450190001002000,1,centroid,RES4,DR-1F
+573789173,573789173,RES,RES3A,302633.914,151316.957,6.443332314486503,-79.93721769,32.776455767,450190001001008,1,centroid,RES3A,DR-1F
+573789181,573789181,RES,RES1-2SNB,206939.675,103469.837,8.957930723876952,-79.93621898,32.776607519,450190001001008,1,centroid,RES1-2SNB,DR-1F
+573789195,573789195,COM,COM8,1517702.41,1517702.41,9.056210338354113,-79.93747764,32.778459962,450190004001013,1,centroid,COM8,DR-2F
+573789340,573789340,RES,RES1-2SNB,327023.262,163511.631,4.689106158876419,-79.93840551,32.774817257,450190001002003,1,centroid,RES1-2SNB,DR-1F
+573789361,573789361,RES,RES1-1SNB,298957.196,149478.598,6.58367896434784,-79.93801322,32.775612877,450190001002003,1,centroid,RES1-1SNB,DR-1F
+573795548,573795548,RES,RES1-2SNB,469463.63,234731.815,3.871138764152527,-79.93959894,32.77423144,450190001003004,1,centroid,RES1-2SNB,STR
+573795699,573795699,RES,RES1-1SNB,135672.901,67836.4506,4.104262158551216,-79.9407934,32.778432824,450190001001001,1,centroid,RES1-1SNB,STR
+573800815,573800815,RES,RES1-3SWB,399414.321,199707.16,7.180729010725021,-79.94320128,32.777364436,450190001003001,1,centroid,RES1-3SWB,STR
+573827798,573827798,RES,RES3A,448490.714,224245.357,10.322198171844482,-79.92911574,32.783303914,450190051002038,1,centroid,RES3A,STR
+573827800,573827800,RES,RES1-1SNB,181563.67,90781.835,9.58003975268364,-79.92897309,32.783345307,450190051002038,1,centroid,RES1-1SNB,STR
+573831730,573831730,RES,RES3A,205336.005,102668.002,11.93792995847702,-79.93231375,32.775717083,450190002001026,1,centroid,RES3A,SR-5
+573831821,573831821,COM,COM2,104939.595,104939.595,10.039824678144456,-79.93136773,32.78120541,450190051002055,1,centroid,COM2,GB
+573850799,573850799,COM,COM1,205297.671,205297.671,8.307100390386582,-79.9370822,32.780106517,450190004001006,1,centroid,COM1,DR-2F
+573860243,573860243,RES,RES3B,493484.753,246742.376,5.002420096139908,-79.94073347,32.773739974,450190001003003,1,centroid,RES3B,STR
+574268207,574268207,RES,RES3A,427020.79,213510.395,8.950609208221435,-79.93066806,32.772190687,450190002002009,1,centroid,RES3A,SR-3
+574268219,574268219,RES,RES3B,352245.479,176122.739,9.26148342247963,-79.93104665,32.771945208,450190002002009,1,centroid,RES3B,SR-3
+574268595,574268595,RES,RES3A,443323.388,221661.694,4.947173550362587,-79.93307183,32.770319309,450190002002018,1,centroid,RES3A,SR-3
+574268597,574268597,RES,RES1-2SNB,437360.158,218680.079,5.201977157225609,-79.93287143,32.770350664,450190002002018,1,centroid,RES1-2SNB,SR-3
+574268706,574268706,RES,RES3A,521059.31,260529.655,4.805387237086296,-79.93405413,32.771895369,450190001003028,1,centroid,RES3A,SR-3
+574268907,574268907,RES,RES3B,489917.358,244958.679,5.6097499885368345,-79.93643612,32.771395477,450190001003019,1,centroid,RES3B,SR-2
+574268926,574268926,RES,RES3A,625595.694,312797.847,5.074223357849121,-79.93722348,32.776803473,450190001001006,1,centroid,RES3A,DR-1F
+574269135,574269135,RES,RES3A,318561.495,159280.747,3.712516484827996,-79.93826701,32.773914061,450190001002002,1,centroid,RES3A,DR-1
+574269195,574269195,RES,RES1-2SNB,432462.562,216231.281,7.467605705881119,-79.9371539,32.775141232,450190001002002,1,centroid,RES1-2SNB,DR-1F
+574294815,574294815,COM,COM4,1760352.08,1760352.08,5.550618189740181,-79.9376746,32.779882371,450190004001005,1,centroid,COM4,DR-2F
+574295941,574295941,RES,RES1-2SNB,189126.984,94563.4922,4.288736975607872,-79.94051647,32.778824337,450190005002018,1,centroid,RES1-2SNB,STR
+574296073,574296073,RES,RES1-1SNB,333941.904,166970.952,4.209198798561096,-79.94069038,32.772157079,450190001003023,1,centroid,RES1-1SNB,SR-2
+574346751,574346751,RES,RES1-2SNB,359456.617,179728.308,13.39830168598175,-79.93008132,32.775745364,450190002001030,1,centroid,RES1-2SNB,SR-5
+574371565,574371565,COM,COM4,101801.883,101801.883,12.465729887065889,-79.93276684,32.781806628,450190051002053,1,centroid,COM4,GB
+574371581,574371581,IND,IND2,41563.4205,62345.1308,11.105838139801026,-79.93258546,32.781970217,450190051002053,1,centroid,IND2,GB
+574371591,574371591,COM,COM5,306327.578,306327.578,11.098756763601305,-79.93201544,32.781770845,450190051002053,1,centroid,COM5,GB
+574371783,574371783,RES,RES1-2SNB,174016.803,87008.4018,10.737201390972135,-79.93575382,32.778675453,450190004001014,1,centroid,RES1-2SNB,DR-2F
+574371920,574371920,RES,RES3A,428813.993,214406.996,6.443989373583793,-79.93667006,32.773601944,450190001002006,1,centroid,RES3A,DR-1
+574372557,574372557,COM,COM4,388628.286,388628.286,13.259950070915222,-79.92938979,32.776111957,450190002001031,1,centroid,COM4,SR-5
+574372970,574372970,RES,RES1-2SNB,158239.456,79119.7281,10.314655289850236,-79.93565177,32.776908084,450190001001007,1,centroid,RES1-2SNB,DR-1F
+574372985,574372985,RES,RES1-2SNB,190842.768,95421.384,8.877666261863709,-79.93591088,32.777347278,450190001001007,1,centroid,RES1-2SNB,DR-1F
+574409645,574409645,PUB,REL1,464815.304,464815.304,10.920502888212203,-79.93338057,32.776663,450190002001023,1,centroid,REL1,DR-1
+574409651,574409651,COM,COM4,158221.99,158221.99,12.526135520076751,-79.93305761,32.776982082,450190002001023,1,centroid,COM4,GB
+574409955,574409955,RES,RES1-2SNB,327402.067,163701.033,5.74792247677803,-79.93775815,32.770940705,450190001003029,1,centroid,RES1-2SNB,SR-2
+574409957,574409957,RES,RES1-3SNB,346937.745,173468.872,5.754260750427246,-79.93765334,32.771058635,450190001003029,1,centroid,RES1-3SNB,SR-2
+574421673,574421673,COM,COM7,102504.455,153756.682,11.520179606552125,-79.92907633,32.777112595,450190002001029,1,centroid,COM7,LB
+574421941,574421941,COM,RES5,1451765.99,725882.998,10.784049704608917,-79.93595137,32.779986363,450190004001009,1,centroid,RES5,DR-4
+574431443,574431443,RES,RES1-1SNB,287437.24,143718.62,4.927836848678589,-79.94106447,32.773697132,450190001003008,1,centroid,RES1-1SNB,STR
+574446469,574446469,PUB,REL1,760348.057,760348.057,12.118791732711792,-79.92923676,32.778549824,450190051002059,1,centroid,REL1,SR-5
+574446473,574446473,PUB,REL1,319528.811,319528.811,9.306437997932434,-79.92888433,32.779250513,450190051002059,1,centroid,REL1,SR-5
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+574421807,574421807,COM,COM1,562123.738,562123.738,12.195767770385745,-79.93325105,32.778823121,450190004001015,1,centroid,COM1,GB
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+573387868,573387868,COM,COM8,994244.826,994244.826,10.535970002508163,-79.93191116,32.779781997,450190051002056,1,centroid,COM8,GB
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+573387880,573387880,RES,RES1-1SNB,1294023.51,647011.755,10.441292479867936,-79.93187083,32.779462015,450190051002056,1,centroid,RES1-1SNB,GB
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+573387918,573387918,PUB,GOV2,682456.988,1023685.48,9.010105909481048,-79.93191667,32.780486772,450190051002056,1,centroid,GOV2,GB
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+573783301,573783301,PUB,REL1,345350.855,345350.855,10.730274892988206,-79.930667,32.77875707,450190051002058,1,centroid,REL1,GB
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+574421815,574421815,COM,COM1,820958.128,820958.128,11.83575883327484,-79.93327766,32.779045709,450190004001015,1,centroid,COM1,GB
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+573782391,573782391,COM,COM8,302401.643,302401.643,10.466302965078354,-79.92828742,32.778875554,450190051002049,1,centroid,COM8,SR-5
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+573783305,573783305,RES,RES1-3SWB,354634.731,177317.365,9.70969706790924,-79.93002133,32.779324182,450190051002058,1,centroid,RES1-3SWB,SR-5
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+574371445,574371445,COM,COM8,270490.857,270490.857,9.408347081708907,-79.92739084,32.779499094,450190051002029,1,centroid,COM8,GB
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+574346715,574346715,RES,RES1-1SNB,286480.0,143240.0,5.896219932823181,-79.92568075,32.776557065,450190002001003,1,centroid,RES1-1SNB,DR-1F
+574346727,574346727,COM,COM4,767618.732,767618.732,4.368724534144402,-79.92869731,32.780981693,450190051002041,1,centroid,COM4,GB
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+574346731,574346731,RES,RES3A,648409.579,324204.789,12.2211983040905,-79.93007543,32.774921478,450190002001030,1,centroid,RES3A,SR-5
+574346739,574346739,RES,RES3A,525561.945,262780.972,10.656163320093157,-79.93060147,32.775136919,450190002001030,1,centroid,RES3A,SR-5
+574346743,574346743,COM,COM1,206165.275,206165.275,10.378395497779849,-79.93054012,32.775380171,450190002001030,1,centroid,COM1,SR-3
+574346746,574346746,PUB,REL1,546050.336,546050.336,9.817510297002793,-79.9306795,32.775520193,450190002001030,1,centroid,REL1,SR-3
+574346747,574346747,RES,RES3A,384363.424,192181.712,13.530101483192444,-79.92978274,32.775564771,450190002001030,1,centroid,RES3A,SR-5
+574346748,574346748,RES,RES1-3SWB,511420.695,255710.347,13.404589115772248,-79.92993701,32.775607128,450190002001030,1,centroid,RES1-3SWB,SR-5
+574371448,574371448,COM,COM4,268598.889,268598.889,10.601216753082277,-79.92803347,32.77943033,450190051002029,1,centroid,COM4,GB
+574371449,574371449,COM,COM7,161159.334,241739.001,10.601216753082277,-79.92803347,32.77943033,450190051002029,1,centroid,COM7,GB
+574371450,574371450,RES,RES3A,1462551.51,731275.759,10.019820357484818,-79.9276305,32.779852059,450190051002029,1,centroid,RES3A,GB
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+574421706,574421706,RES,RES3A,439108.913,219554.456,12.670249699869157,-79.92801597,32.777036402,450190002001029,1,centroid,RES3A,LB
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+574455177,574455177,PUB,REL1,199618.895,199618.895,8.084265055246354,-79.92675798,32.779623429,450190051002028,1,centroid,REL1,GB
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+574461005,574461005,COM,RES4,143141.79,71570.8953,7.855873659667969,-79.93014784,32.782312749,450190051002045,1,centroid,RES4,CT
+574461007,574461007,COM,COM4,177990.619,177990.619,9.031458765716552,-79.93015454,32.781828328,450190051002054,1,centroid,COM4,GB
+574461027,574461027,PUB,REL1,253972.383,253972.383,10.80505604039192,-79.92996582,32.778916309,450190051002058,1,centroid,REL1,SR-5
+574461035,574461035,RES,RES3A,455033.853,227516.926,9.464215878095628,-79.92820274,32.778662139,450190051002049,1,centroid,RES3A,SR-5
+574461058,574461058,RES,RES3A,266985.928,133492.964,12.135287044906615,-79.9297417,32.776818232,450190002001028,1,centroid,RES3A,LB
+574461060,574461060,RES,RES3A,266985.928,133492.964,12.135287044906615,-79.9297417,32.776818232,450190002001028,1,centroid,RES3A,LB
+574461061,574461061,RES,RES3A,266985.928,133492.964,12.135287044906615,-79.9297417,32.776818232,450190002001028,1,centroid,RES3A,LB
+574461067,574461067,RES,RES3A,266985.928,133492.964,12.135287044906615,-79.9297417,32.776818232,450190002001028,1,centroid,RES3A,LB
+574461087,574461087,RES,RES3A,266985.928,133492.964,12.856005782165528,-79.92799481,32.777083557,450190002001029,1,centroid,RES3A,LB
+574461089,574461089,RES,RES3A,266985.928,133492.964,12.856005782165528,-79.92799481,32.777083557,450190002001029,1,centroid,RES3A,LB
+574461283,574461283,PUB,GOV1,177990.619,177990.619,5.085602995643615,-79.9260343,32.781508757,450190051002033,1,centroid,GOV1,GB
+574461291,574461291,RES,RES3A,266985.928,133492.964,7.61854704257965,-79.92681259,32.780233741,450190051002010,1,centroid,RES3A,GB
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+574461302,574461302,RES,RES3A,266985.928,133492.964,7.195152240123749,-79.92673454,32.779105389,450190051002027,1,centroid,RES3A,LB
+574461303,574461303,RES,RES3A,266985.928,133492.964,7.195152240123749,-79.92673454,32.779105389,450190051002027,1,centroid,RES3A,LB
+574461304,574461304,RES,RES3A,266985.928,133492.964,8.221585713300705,-79.92679108,32.778909003,450190051002027,1,centroid,RES3A,LB
+574461305,574461305,RES,RES3A,266985.928,133492.964,6.773325775156021,-79.92639653,32.779118318,450190051002027,1,centroid,RES3A,LB
+574461307,574461307,RES,RES3A,180221.236,90110.618,9.339365263051986,-79.92670704,32.778289721,450190002001008,1,centroid,RES3A,LB
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+574461310,574461310,COM,COM4,110480.451,110480.451,13.58082331665039,-79.92724476,32.777075418,450190002001018,1,centroid,COM4,LB
+574461313,574461313,RES,RES3A,265539.431,132769.715,12.944874589500428,-79.92732872,32.776630152,450190002001017,1,centroid,RES3A,LB
+574486905,574486905,RES,RES1-2SNB,295499.127,147749.563,6.217263309931755,-79.93024242,32.773552267,450190002002001,1,centroid,RES1-2SNB,SR-3
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+574487017,574487017,RES,RES1-1SNB,121637.067,60818.5339,9.095769207077026,-79.93029798,32.779364989,450190051002058,1,centroid,RES1-1SNB,GB
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+574487102,574487102,RES,RES3B,349484.965,174742.482,13.185107910881042,-79.93042173,32.777686122,450190002001021,1,centroid,RES3B,GB
+574488427,574488427,RES,RES1-1SNB,115418.403,57709.2019,9.96529696782112,-79.92997893,32.782866752,450190051002045,1,centroid,RES1-1SNB,STR
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+574487057,574487057,RES,RES1-1SNB,120596.338,60298.1694,10.10845606528282,-79.93516687,32.777103356,450190001001007,1,centroid,RES1-1SNB,DR-1F
+574487062,574487062,RES,RES1-1SNB,120596.338,60298.1694,10.10845606528282,-79.93516687,32.777103356,450190001001007,1,centroid,RES1-1SNB,DR-1F
+574487067,574487067,RES,RES1-1SNB,116570.718,58285.359,10.10845606528282,-79.93516687,32.777103356,450190001001007,1,centroid,RES1-1SNB,DR-1F
+574487071,574487071,RES,RES1-1SNB,126848.892,63424.4462,10.10845606528282,-79.93516687,32.777103356,450190001001007,1,centroid,RES1-1SNB,DR-1F
+574487080,574487080,RES,RES1-2SNB,286207.332,143103.666,10.273896506061554,-79.93466484,32.77742362,450190002001023,1,centroid,RES1-2SNB,DR-1F
+574487083,574487083,RES,RES1-2SNB,281541.181,140770.59,9.454224668750763,-79.93460986,32.777125964,450190002001023,1,centroid,RES1-2SNB,DR-1F
+574487089,574487089,RES,RES3A,480189.335,240094.667,9.52555860253334,-79.93615824,32.776447461,450190001001008,1,centroid,RES3A,DR-1F
+574487092,574487092,RES,RES1-1SNB,137409.92,68704.9601,9.545346250133514,-79.93544376,32.776459504,450190001001007,1,centroid,RES1-1SNB,DR-1F
+574487095,574487095,RES,RES1-2SNB,192151.108,96075.5541,7.858934459962845,-79.93602696,32.776203231,450190001001008,1,centroid,RES1-2SNB,DR-1F
+573800692,573800692,RES,RES3A,561796.474,280898.237,4.752157236967086,-79.94221862,32.773550407,450190001003009,1,centroid,RES3A,SR-2
+573800698,573800698,PUB,GOV1,238917.062,238917.062,4.688891441349983,-79.94320565,32.77510739,450190001003009,1,centroid,GOV1,SR-2
+573800714,573800714,RES,RES3A,495571.048,247785.524,4.547155580825805,-79.94176831,32.772408153,450190001003010,1,centroid,RES3A,SR-2
+573800716,573800716,RES,RES3B,339046.689,169523.344,4.726986006941796,-79.94191753,32.772502839,450190001003010,1,centroid,RES3B,SR-2
+573800728,573800728,RES,RES1-2SNB,366904.339,183452.169,4.914053078579903,-79.94171108,32.772902534,450190001003010,1,centroid,RES1-2SNB,SR-2
+573800732,573800732,RES,RES1-2SNB,368737.359,184368.679,4.34344222984314,-79.94203934,32.773043173,450190001003010,1,centroid,RES1-2SNB,SR-2
+573800791,573800791,COM,COM4,107840.171,107840.171,4.958322435259819,-79.94360752,32.775436262,450190001003001,1,centroid,COM4,LB
+573800793,573800793,COM,COM3,64704.1031,64704.1031,4.958322435259819,-79.94360752,32.775436262,450190001003001,1,centroid,COM3,LB
+573800795,573800795,PUB,REL1,1701559.68,1701559.68,6.6316763491916655,-79.94304606,32.775950202,450190001003001,1,centroid,REL1,LB
+573800797,573800797,COM,COM4,1063474.8,1063474.8,6.6316763491916655,-79.94304606,32.775950202,450190001003001,1,centroid,COM4,LB
+573800801,573800801,COM,COM5,987899.431,987899.431,4.327391605858803,-79.9431408,32.776523032,450190001003001,1,centroid,COM5,LB
+573800805,573800805,RES,RES1-1SNB,240399.36,120199.68,4.997913374724388,-79.94356051,32.77701734,450190001003001,1,centroid,RES1-1SNB,STR
+573800809,573800809,RES,RES1-1SNB,218710.262,109355.131,5.465973724851608,-79.94368712,32.777222326,450190001003001,1,centroid,RES1-1SNB,STR
+573800813,573800813,RES,RES3A,571424.938,285712.469,6.713976129980087,-79.9433719,32.777366401,450190001003001,1,centroid,RES3A,STR
+573800817,573800817,RES,RES3A,260917.33,130458.665,7.194719676218033,-79.94308681,32.777419457,450190001003001,1,centroid,RES3A,STR
+573800819,573800819,RES,RES1-3SWB,422957.134,211478.567,8.788730966405868,-79.94253555,32.777464761,450190001003001,1,centroid,RES1-3SWB,STR
+573800823,573800823,RES,RES3A,513882.369,256941.184,5.465973724851608,-79.94369184,32.777227272,450190001003001,1,centroid,RES3A,STR
+573800826,573800826,RES,RES1-1SNB,254092.507,127046.253,4.997913374724388,-79.94356439,32.777033379,450190001003001,1,centroid,RES1-1SNB,STR
+573800969,573800969,RES,RES1-3SNB,398297.048,199148.524,4.917524931917191,-79.94242512,32.77358757,450190001003024,1,centroid,RES1-3SNB,SR-2
+573806311,573806311,RES,RES1-3SNB,243505.92,121752.96,7.777054727668762,-79.94320725,32.777784572,450190005002028,1,centroid,RES1-3SNB,STR
+573806312,573806312,RES,RES3A,147490.131,73745.0657,4.716619335255623,-79.94375884,32.777573207,450190005002028,1,centroid,RES3A,STR
+573860813,573860813,RES,RES3A,441312.99,220656.495,6.993137074022293,-79.94170131,32.778348266,450190005002017,1,centroid,RES3A,STR
+574296105,574296105,RES,RES1-2SNB,326388.249,163194.124,4.209433462524414,-79.94120099,32.772665114,450190001003023,1,centroid,RES1-2SNB,SR-2
+574422150,574422150,RES,RES3A,555462.891,277731.445,5.793754695453644,-79.94551694,32.776478611,450190005002029,1,centroid,RES3A,SR-2
+574431435,574431435,RES,RES1-1SNB,247455.456,123727.728,4.078337266097069,-79.94199805,32.775231928,450190001003008,1,centroid,RES1-1SNB,STR
+574431441,574431441,RES,RES1-2SNB,269317.399,134658.699,5.11859831331253,-79.94126543,32.773665544,450190001003008,1,centroid,RES1-2SNB,STR
+574431459,574431459,RES,RES1-1SNB,273155.575,136577.787,4.453472642283439,-79.94120491,32.773905274,450190001003008,1,centroid,RES1-1SNB,STR
+574431599,574431599,RES,RES1-1SNB,242593.004,121296.502,4.4480519047307965,-79.94445838,32.776922222,450190001003002,1,centroid,RES1-1SNB,SR-2
+574460772,574460772,COM,COM8,207879.805,207879.805,4.022045683469773,-79.94152807,32.774410541,450190001003008,1,centroid,COM8,STR
+574485649,574485649,RES,RES1-1SNB,102927.253,51463.6265,6.47364151198864,-79.94146718,32.778522856,450190005002017,1,centroid,RES1-1SNB,STR
+574485650,574485650,RES,RES1-1SNB,82253.4757,41126.7378,6.47364151198864,-79.94146718,32.778522856,450190005002017,1,centroid,RES1-1SNB,STR
+574485655,574485655,RES,RES1-1SNB,88088.0016,44044.0008,6.47364151198864,-79.94146718,32.778522856,450190005002017,1,centroid,RES1-1SNB,STR
+574485659,574485659,RES,RES1-1SNB,65421.18,32710.59,6.47364151198864,-79.94146718,32.778522856,450190005002017,1,centroid,RES1-1SNB,STR
+574485661,574485661,RES,RES1-1SNB,102927.253,51463.6265,6.47364151198864,-79.94146718,32.778522856,450190005002017,1,centroid,RES1-1SNB,STR
+574485666,574485666,RES,RES1-1SNB,74951.1782,37475.5891,6.47364151198864,-79.94146718,32.778522856,450190005002017,1,centroid,RES1-1SNB,STR
+574485667,574485667,RES,RES1-1SNB,65421.18,32710.59,6.47364151198864,-79.94146718,32.778522856,450190005002017,1,centroid,RES1-1SNB,STR
+574486695,574486695,RES,RES1-1SNB,102927.253,51463.6265,6.47364151198864,-79.94146718,32.778522856,450190005002017,1,centroid,RES1-1SNB,STR
+574486696,574486696,RES,RES1-1SNB,88320.5879,44160.2939,6.47364151198864,-79.94146718,32.778522856,450190005002017,1,centroid,RES1-1SNB,STR
+574486698,574486698,RES,RES1-1SNB,42145.0128,21072.5064,6.47364151198864,-79.94146718,32.778522856,450190005002017,1,centroid,RES1-1SNB,STR
+574486701,574486701,RES,RES1-1SNB,88320.5879,44160.2939,6.47364151198864,-79.94146718,32.778522856,450190005002017,1,centroid,RES1-1SNB,STR
+574486704,574486704,RES,RES1-1SNB,106483.093,53241.5467,6.47364151198864,-79.94146718,32.778522856,450190005002017,1,centroid,RES1-1SNB,STR
+574486730,574486730,RES,RES1-2SWB,176858.069,88429.0348,6.316693560042381,-79.94519888,32.77698147,450190005002024,1,centroid,RES1-2SWB,DR-1
+574486732,574486732,RES,RES1-2SWB,181426.792,90713.3963,6.223732995400429,-79.94502746,32.777048185,450190005002024,1,centroid,RES1-2SWB,DR-1
+574486736,574486736,RES,RES1-2SNB,253426.0,126713.0,5.118224415397644,-79.94338557,32.777176344,450190001003001,1,centroid,RES1-2SNB,STR
+574486737,574486737,RES,RES1-1SNB,215463.869,107731.934,7.85760235086441,-79.9427401,32.777416396,450190001003001,1,centroid,RES1-1SNB,STR
+574486746,574486746,RES,RES1-1SNB,123940.895,61970.4475,7.85760235086441,-79.9427401,32.777416396,450190001003001,1,centroid,RES1-1SNB,STR
+574486748,574486748,RES,RES1-1SNB,166009.113,83004.5568,7.85760235086441,-79.9427401,32.777416396,450190001003001,1,centroid,RES1-1SNB,STR
+574486750,574486750,RES,RES1-1SNB,206321.87,103160.935,7.85760235086441,-79.9427401,32.777416396,450190001003001,1,centroid,RES1-1SNB,STR
+574486753,574486753,RES,RES1-1SNB,213835.163,106917.581,7.85760235086441,-79.9427401,32.777416396,450190001003001,1,centroid,RES1-1SNB,STR
+574486755,574486755,RES,RES1-2SNB,250080.442,125040.221,7.85760235086441,-79.9427401,32.777416396,450190001003001,1,centroid,RES1-2SNB,STR
+574486769,574486769,RES,RES1-1SNB,214329.384,107164.692,6.284586445474624,-79.94233945,32.774283067,450190001003024,1,centroid,RES1-1SNB,DR-2
+574486774,574486774,RES,RES1-3SNB,386376.796,193188.398,6.284586445474624,-79.94233945,32.774283067,450190001003024,1,centroid,RES1-3SNB,DR-2
+574486777,574486777,RES,RES1-2SNB,222200.794,111100.397,6.284586445474624,-79.94233945,32.774283067,450190001003024,1,centroid,RES1-2SNB,DR-2
+574486815,574486815,RES,RES1-1SNB,203264.754,101632.377,3.892720026645661,-79.94141607,32.775137429,450190001003008,1,centroid,RES1-1SNB,STR
+574486817,574486817,RES,RES1-1SNB,218561.014,109280.507,3.892720026645661,-79.94141607,32.775137429,450190001003008,1,centroid,RES1-1SNB,STR
+574486819,574486819,RES,RES1-1SNB,223773.423,111886.711,3.892720026645661,-79.94141607,32.775137429,450190001003008,1,centroid,RES1-1SNB,STR
+574486820,574486820,RES,RES1-1SNB,203264.754,101632.377,3.892720026645661,-79.94141607,32.775137429,450190001003008,1,centroid,RES1-1SNB,STR
+574486821,574486821,RES,RES1-1SNB,218561.014,109280.507,3.892720026645661,-79.94141607,32.775137429,450190001003008,1,centroid,RES1-1SNB,STR
+574486822,574486822,RES,RES1-1SNB,214165.178,107082.589,3.892720026645661,-79.94141607,32.775137429,450190001003008,1,centroid,RES1-1SNB,STR
diff --git a/examples/data/aggregation_zones/fiat_model_aggregation1/settings.toml b/examples/data/aggregation_zones/fiat_model_aggregation1/settings.toml
new file mode 100644
index 00000000..3ae7d2ea
--- /dev/null
+++ b/examples/data/aggregation_zones/fiat_model_aggregation1/settings.toml
@@ -0,0 +1,24 @@
+[global]
+crs = "epsg:4326"
+
+[output]
+path = "output"
+
+[output.csv]
+name = "output.csv"
+
+[output.geom]
+name1 = "spatial.gpkg"
+
+[vulnerability]
+file = "./vulnerability/vulnerability_curves.csv"
+unit = "feet"
+step_size = 0.1
+
+[exposure.csv]
+file = "./exposure/exposure.csv"
+
+[exposure.geom]
+crs = "EPSG:4326"
+unit = "ft"
+file1 = "./exposure/buildings.gpkg"
diff --git a/examples/data/aggregation_zones/fiat_model_aggregation1/vulnerability/vulnerability_curves.csv b/examples/data/aggregation_zones/fiat_model_aggregation1/vulnerability/vulnerability_curves.csv
new file mode 100644
index 00000000..6dcbb006
--- /dev/null
+++ b/examples/data/aggregation_zones/fiat_model_aggregation1/vulnerability/vulnerability_curves.csv
@@ -0,0 +1,37 @@
+#UNIT=feet
+#METHOD,max,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean
+water depth [feet],AGR1,COM1,COM10,COM2,COM3,COM4,COM5,COM6,COM7,COM8,COM9,EDU1,EDU2,GOV1,GOV2,IND1,IND2,IND3,IND4,IND5,IND6,REL1,RES1-1SNB,RES1-1SWB,RES1-2SNB,RES1-2SWB,RES1-3SNB,RES1-3SWB,RES2,RES3A,RES3B,RES3C,RES3D,RES3E,RES3F,RES4,RES5,RES6
+-9.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-8.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1e-06,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-7.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.008,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.021,0.0,0.023,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.037,0.0,0.037,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.057,0.0,0.052,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.08,0.0,0.068,0.0,0.05,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.105,0.0,0.084,0.0,0.07,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.024,0.132,0.01,0.101,0.0,0.08,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+0.0,0.06,0.02,0.0,0.03,0.004,0.0,0.0,0.0,0.0,0.13,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.02,0.15,0.02,0.0,0.0,0.081,0.16,0.05,0.119,0.06,0.14,0.03,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+1.0,0.2,0.26,0.11,0.16,0.164,0.2,0.5,0.146,0.28,0.45,0.04,0.217,0.27,0.3,0.08,0.15,0.193,0.2,0.2,0.2,0.2,0.293,0.133,0.189,0.087,0.138,0.11,0.18,0.27,0.217,0.217,0.217,0.217,0.217,0.217,0.161,0.15,0.38
+2.0,0.43,0.42,0.17,0.27,0.289,0.3,0.74,0.27,0.51,0.55,0.06,0.304,0.38,0.59,0.2,0.24,0.31,0.41,0.26,0.41,0.35,0.484,0.179,0.218,0.122,0.157,0.15,0.22,0.49,0.304,0.304,0.304,0.304,0.304,0.304,0.263,0.25,0.6
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diff --git a/examples/data/aggregation_zones/fiat_model_aggregation2/exposure/buildings.gpkg b/examples/data/aggregation_zones/fiat_model_aggregation2/exposure/buildings.gpkg
new file mode 100644
index 00000000..d70c2a51
Binary files /dev/null and b/examples/data/aggregation_zones/fiat_model_aggregation2/exposure/buildings.gpkg differ
diff --git a/examples/data/aggregation_zones/fiat_model_aggregation2/exposure/exposure.csv b/examples/data/aggregation_zones/fiat_model_aggregation2/exposure/exposure.csv
new file mode 100644
index 00000000..bbdebbd2
--- /dev/null
+++ b/examples/data/aggregation_zones/fiat_model_aggregation2/exposure/exposure.csv
@@ -0,0 +1,3082 @@
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+574451483,574451483,IND,IND6,107332.574,107332.574,6.471536185131073,-79.93007513,32.780377243,450190051002051,1,centroid,IND6,GB,Neighborhood,A-1
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+574431597,574431597,RES,RES1-2SWB,379812.9,189906.45,4.620224463510513,-79.9445626,32.776857362,450190001003002,1,centroid,RES1-2SWB,SR-2,Neighborhood,
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+574461290,574461290,RES,RES3A,266985.928,133492.964,7.61854704257965,-79.92681259,32.780233741,450190051002010,1,centroid,RES3A,GB,City Centers,A-1
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+573783435,573783435,RES,RES1-1SNB,415179.31,207589.655,6.924839691925049,-79.92959328,32.771352309,450190002002010,1,centroid,RES1-1SNB,SR-4,Neighborhood,
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+573831840,573831840,COM,RES4,2267449.63,1133724.81,9.66924804055214,-79.93102303,32.781120425,450190051002055,1,centroid,RES4,GB,City Centers,A-1
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+574431316,574431316,COM,COM2,451365.337,451365.337,6.7361033772945405,-79.92857452,32.78132147,450190051002042,1,centroid,COM2,GB,City Centers,A-1
+574488423,574488423,RES,RES1-1SNB,203256.59,101628.295,10.77225471159935,-79.93010382,32.782773607,450190051002045,1,centroid,RES1-1SNB,STR,Neighborhood,
+574488424,574488424,RES,RES1-1SNB,92284.9737,46142.4868,9.96529696782112,-79.92997893,32.782866752,450190051002045,1,centroid,RES1-1SNB,STR,Neighborhood,
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+573782445,573782445,RES,RES1-1SNB,382535.481,191267.74,7.318574533843994,-79.92933467,32.772737024,450190002002006,1,centroid,RES1-1SNB,SR-4,Neighborhood,
+573782562,573782562,RES,RES1-2SNB,279509.397,139754.698,10.393835604352953,-79.92991604,32.772163112,450190002002005,1,centroid,RES1-2SNB,SR-4,Neighborhood,
+573783148,573783148,RES,RES3A,542841.252,271420.626,8.270848719120025,-79.92915473,32.773637584,450190002002001,1,centroid,RES3A,SR-4,Neighborhood,
+573785663,573785663,RES,RES1-3SNB,564423.557,282211.778,7.208511659555435,-79.93083678,32.773332097,450190002002004,1,centroid,RES1-3SNB,SR-3,Neighborhood,
+573785802,573785802,RES,RES1-3SWB,446801.709,223400.854,11.72030416332245,-79.93279414,32.775415461,450190002001025,1,centroid,RES1-3SWB,SR-5,Neighborhood,
+573785837,573785837,RES,RES3A,297602.261,148801.13,11.473363363656995,-79.93287592,32.775610735,450190002001025,1,centroid,RES3A,SR-5,Neighborhood,
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+574486915,574486915,RES,RES1-2SNB,235443.627,117721.813,5.578542810268402,-79.93327038,32.771857448,450190002002003,1,centroid,RES1-2SNB,SR-4,Neighborhood,
+574486923,574486923,RES,RES1-1SNB,123186.308,61593.154,5.276184942345619,-79.93337915,32.770923728,450190002002007,1,centroid,RES1-1SNB,SR-3,Neighborhood,
+574488875,574488875,RES,RES1-3SWB,422398.369,211199.184,6.096695703325271,-79.92813581,32.772327859,450190002002006,1,centroid,RES1-3SWB,SR-3,Neighborhood,
+573467947,573467947,RES,RES1-3SWB,392529.15,196264.575,7.667483865489959,-79.93036329,32.782270155,450190051002045,1,centroid,RES1-3SWB,CT,City Centers,A-1
+574488430,574488430,RES,RES1-1SNB,113677.177,56838.5889,9.96529696782112,-79.92997893,32.782866752,450190051002045,1,centroid,RES1-1SNB,STR,Neighborhood,
+573831778,573831778,RES,RES3A,303712.56,151856.28,12.889066805744172,-79.93226152,32.778060838,450190051002057,1,centroid,RES3A,GB,City Centers,A-1
+574421795,574421795,RES,RES1-3SNB,286299.939,143149.969,12.801361931667328,-79.93357708,32.778422236,450190004001015,1,centroid,RES1-3SNB,GB,Neighborhood,
+574421797,574421797,COM,COM4,389608.347,389608.347,12.55276987991333,-79.93317162,32.778454651,450190004001015,1,centroid,COM4,GB,City Centers,
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+574268559,574268559,RES,RES3A,475021.661,237510.83,5.453385958752632,-79.93523879,32.77004074,450190002002018,1,centroid,RES3A,SR-2,Neighborhood,
+574268789,574268789,RES,RES1-3SWB,736436.61,368218.305,6.868733884935379,-79.93662462,32.77499479,450190001002001,1,centroid,RES1-3SWB,DR-1F,Neighborhood,
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+574409965,574409965,RES,RES1-3SNB,403001.842,201500.921,5.001365672731399,-79.93831937,32.771336188,450190001003029,1,centroid,RES1-3SNB,SR-2,Neighborhood,
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+574461031,574461031,RES,RES3A,423287.305,211643.652,10.039619738283156,-79.9278853,32.779870634,450190051002029,1,centroid,RES3A,GB,Neighborhood,A-1
+574461075,574461075,RES,RES3A,266985.928,133492.964,12.440670904636384,-79.92903083,32.776368402,450190002001017,1,centroid,RES3A,LB,Neighborhood,
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+574488872,574488872,RES,RES3A,480189.335,240094.667,8.192235509195328,-79.92970963,32.771767424,450190002002010,1,centroid,RES3A,SR-4,Neighborhood,
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+573782540,573782540,RES,RES1-2SNB,278796.48,139398.24,12.306768518314362,-79.9284404,32.775666181,450190002001032,1,centroid,RES1-2SNB,SR-5,Neighborhood,
+573782554,573782554,COM,COM4,426627.449,426627.449,13.15236603029251,-79.92852646,32.775996194,450190002001032,1,centroid,COM4,SR-5,Neighborhood,
+573782586,573782586,RES,RES3A,421325.574,210662.787,8.191982072114945,-79.93017261,32.772909565,450190002002005,1,centroid,RES3A,SR-3,Neighborhood,
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+573783282,573783282,RES,RES1-2SNB,285578.58,142789.29,11.53996177865982,-79.92950695,32.77844283,450190051002058,1,centroid,RES1-2SNB,SR-5,Neighborhood,
+573783290,573783290,IND,IND1,4806682.88,7210024.32,12.308836690044403,-79.92970399,32.778582016,450190051002058,1,centroid,IND1,SR-5,Neighborhood,
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+573782532,573782532,COM,COM2,292043.637,292043.637,13.342008930034638,-79.92889412,32.775479003,450190002001032,1,centroid,COM2,SR-5,Neighborhood,
+573782548,573782548,COM,COM1,170039.86,170039.86,13.133367635822296,-79.92901056,32.775913829,450190002001032,1,centroid,COM1,SR-5,Neighborhood,
+573783140,573783140,RES,RES1-3SNB,395177.36,197588.68,7.854387454566956,-79.92927399,32.773426703,450190002002001,1,centroid,RES1-3SNB,SR-4,Neighborhood,
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+573783311,573783311,COM,COM5,1339369.98,1339369.98,10.064074852113723,-79.93112351,32.779280644,450190051002058,1,centroid,COM5,GB,City Centers,
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+573785883,573785883,RES,RES1-3SNB,413001.405,206500.702,6.074873519163131,-79.93343889,32.771793801,450190002002003,1,centroid,RES1-3SNB,SR-4,Neighborhood,
+573785917,573785917,RES,RES3B,463332.046,231666.023,8.033795876655578,-79.93251053,32.77281289,450190002002003,1,centroid,RES3B,SR-4,Neighborhood,
+573789248,573789248,RES,RES1-3SNB,343962.335,171981.167,4.514737927613258,-79.93761682,32.773771677,450190001002007,1,centroid,RES1-3SNB,DR-1,Neighborhood,
+573789111,573789111,RES,RES3B,627180.555,313590.277,10.413322099866868,-79.93500732,32.779001874,450190004001018,1,centroid,RES3B,DR-2F,Neighborhood,
+573789174,573789174,RES,RES1-2SNB,276298.841,138149.42,7.016759912996292,-79.93677075,32.776509311,450190001001008,1,centroid,RES1-2SNB,DR-1F,Neighborhood,
+573789229,573789229,RES,RES1-2SWB,429714.924,214857.462,4.683147649741173,-79.93758824,32.77268194,450190001002007,1,centroid,RES1-2SWB,SR-2,Neighborhood,
+573789338,573789338,RES,RES1-2SNB,255098.779,127549.389,3.8232931286716463,-79.93892889,32.77475913,450190001002003,1,centroid,RES1-2SNB,STR,Neighborhood,
+573789703,573789703,RES,RES1-2SNB,220946.21,110473.105,4.353516353788375,-79.93828502,32.773000626,450190001002008,1,centroid,RES1-2SNB,SR-2,Neighborhood,
+573795573,573795573,RES,RES1-2SNB,283199.628,141599.814,3.610267529411316,-79.93971208,32.774966193,450190001003004,1,centroid,RES1-2SNB,STR,Neighborhood,
+573795584,573795584,RES,RES3A,524573.953,262286.976,4.211809044046402,-79.94000052,32.775354477,450190001003004,1,centroid,RES3A,STR,Neighborhood,
+573795681,573795681,RES,RES1-2SNB,156893.971,78446.9856,4.402486420760154,-79.93977192,32.777724528,450190001001001,1,centroid,RES1-2SNB,DR-1F,Neighborhood,
+573800799,573800799,COM,COM7,1175569.92,1763354.88,4.327391605858803,-79.9431408,32.776523032,450190001003001,1,centroid,COM7,LB,Neighborhood,
+573827140,573827140,COM,COM4,1316722.49,1316722.49,13.127205360145568,-79.92774466,32.777705241,450190002001018,1,centroid,COM4,LB,Neighborhood,
+573827762,573827762,COM,COM1,198783.687,198783.687,12.08352486787796,-79.92950468,32.777282449,450190002001028,1,centroid,COM1,SR-5,Neighborhood,
+573827805,573827805,COM,COM1,1131876.27,1131876.27,9.535113336906432,-79.9283487,32.783341755,450190051002038,1,centroid,COM1,LB,Neighborhood,
+573831732,573831732,RES,RES1-3SNB,343962.335,171981.167,11.883929869451524,-79.93253579,32.775843272,450190002001026,1,centroid,RES1-3SNB,SR-5,Neighborhood,
+573856074,573856074,RES,RES3A,459404.56,229702.28,4.967277603206634,-79.93948096,32.776016515,450190001001009,1,centroid,RES3A,STR,Neighborhood,
+574267729,574267729,RES,RES1-3SNB,362693.252,181346.626,12.50860846865654,-79.92821535,32.775841025,450190002001016,1,centroid,RES1-3SNB,SR-5,Neighborhood,
+574268037,574268037,COM,COM8,958947.744,958947.744,8.962779663572311,-79.92859461,32.780244674,450190051002050,1,centroid,COM8,GB,Neighborhood,A-1
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+574269206,574269206,RES,RES1-2SNB,308979.521,154489.76,7.474012814292908,-79.93733677,32.775455194,450190001002002,1,centroid,RES1-2SNB,DR-1F,Neighborhood,
+574293775,574293775,RES,RES1-1SNB,327984.273,163992.136,8.2410636044693,-79.9316132,32.77376274,450190002002002,1,centroid,RES1-1SNB,SR-5,Neighborhood,
+574371556,574371556,COM,COM4,336608.853,336608.853,11.054359906167983,-79.9326316,32.781124507,450190051002053,1,centroid,COM4,GB,City Centers,A-1
+574372304,574372304,PUB,GOV1,37564089.4,37564089.4,2.082344260106087,-79.92438222,32.783062593,450190051002009,1,centroid,GOV1,LI,Future Planning Area,
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+574371890,574371890,RES,RES1-3SNB,382538.219,191269.109,4.378379000701904,-79.9367562,32.772173076,450190001002006,1,centroid,RES1-3SNB,DR-1,Neighborhood,
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+574485657,574485657,RES,RES1-1SNB,88320.5879,44160.2939,6.47364151198864,-79.94146718,32.778522856,450190005002017,1,centroid,RES1-1SNB,STR,Neighborhood,
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+573387874,573387874,COM,COM2,733950.496,733950.496,10.535970002508163,-79.93191116,32.779781997,450190051002056,1,centroid,COM2,GB,City Centers,A-5
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+573387878,573387878,COM,COM5,267963.343,267963.343,10.535970002508163,-79.93191116,32.779781997,450190051002056,1,centroid,COM5,GB,City Centers,A-5
+573387880,573387880,RES,RES1-1SNB,1294023.51,647011.755,10.441292479867936,-79.93187083,32.779462015,450190051002056,1,centroid,RES1-1SNB,GB,City Centers,A-5
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+573387898,573387898,COM,COM1,651663.557,651663.557,11.384618928222656,-79.93293388,32.779311996,450190051002056,1,centroid,COM1,GB,City Centers,A-1
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+573475695,573475695,COM,COM8,1236901.38,1236901.38,10.538166457204818,-79.93147255,32.782216093,450190051002046,1,centroid,COM8,GB,City Centers,A-1
+573783286,573783286,COM,COM4,343812.621,343812.621,12.148350005531311,-79.93029658,32.778403404,450190051002058,1,centroid,COM4,SR-5,Neighborhood,
+573783301,573783301,PUB,REL1,345350.855,345350.855,10.730274892988206,-79.930667,32.77875707,450190051002058,1,centroid,REL1,GB,City Centers,
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+574294737,574294737,RES,RES3A,226536.779,113268.389,9.202286308879852,-79.93656366,32.779805929,450190004001007,1,centroid,RES3A,DR-2F,Neighborhood,
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+574371777,574371777,RES,RES1-2SNB,167666.784,83833.392,10.522791274328233,-79.93600432,32.778566182,450190004001014,1,centroid,RES1-2SNB,DR-1F,Neighborhood,
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+574421815,574421815,COM,COM1,820958.128,820958.128,11.83575883327484,-79.93327766,32.779045709,450190004001015,1,centroid,COM1,GB,City Centers,
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+574451491,574451491,COM,COM4,2792902.33,2792902.33,9.38372770310402,-79.93092389,32.780416852,450190051002051,1,centroid,COM4,GB,City Centers,A-1
+574451492,574451492,COM,COM1,1839700.14,1839700.14,9.38372770310402,-79.93092389,32.780416852,450190051002051,1,centroid,COM1,GB,City Centers,A-1
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+574451499,574451499,COM,COM5,743044.998,743044.998,7.93391350730896,-79.93087903,32.780089433,450190051002051,1,centroid,COM5,GB,City Centers,A-1
+574451500,574451500,COM,COM3,104658.619,104658.619,7.93391350730896,-79.93087903,32.780089433,450190051002051,1,centroid,COM3,GB,City Centers,A-1
+574455889,574455889,COM,COM4,4109796.25,4109796.25,11.890168802022934,-79.93401099,32.780256033,450190004001010,1,centroid,COM4,GB,City Centers,A-1
+574455890,574455890,COM,COM2,1337319.96,1337319.96,11.890168802022934,-79.93401099,32.780256033,450190004001010,1,centroid,COM2,GB,City Centers,A-1
+574460931,574460931,RES,RES3A,266985.928,133492.964,11.422025928401949,-79.93355205,32.781223407,450190004001011,1,centroid,RES3A,GB,City Centers,A-1
+574460937,574460937,COM,RES4,182368.213,91184.1065,11.48962948738098,-79.93305405,32.781484465,450190051002053,1,centroid,RES4,GB,City Centers,A-1
+574460939,574460939,COM,RES4,182368.213,91184.1065,11.389748682460786,-79.93285367,32.781098819,450190051002053,1,centroid,RES4,GB,City Centers,A-1
+574460945,574460945,RES,RES3B,497367.854,248683.927,12.48675656039238,-79.93420524,32.779937346,450190004001017,1,centroid,RES3B,GB,Neighborhood,
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+574487008,574487008,RES,RES1-1SWB,136014.752,68007.3762,12.734522594995498,-79.93341863,32.778558501,450190004001015,1,centroid,RES1-1SWB,GB,City Centers,
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+573407534,573407534,COM,COM4,266044.264,266044.264,9.388399080400466,-79.92681104,32.778289954,450190002001008,1,centroid,COM4,LB,City Centers,
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+573783305,573783305,RES,RES1-3SWB,354634.731,177317.365,9.70969706790924,-79.93002133,32.779324182,450190051002058,1,centroid,RES1-3SWB,SR-5,Neighborhood,
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+574267600,574267600,COM,COM8,4511607.27,4511607.27,8.103889220285415,-79.92682845,32.782317397,450190051002033,1,centroid,COM8,LI,Future Planning Area,
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+574268041,574268041,COM,COM10,186797.434,93398.7172,8.962779663572311,-79.92859461,32.780244674,450190051002050,1,centroid,COM10,GB,Neighborhood,A-1
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+574371445,574371445,COM,COM8,270490.857,270490.857,9.408347081708907,-79.92739084,32.779499094,450190051002029,1,centroid,COM8,GB,City Centers,A-1
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+574372312,574372312,PUB,GOV1,878466.733,878466.733,0.198340682876259,-79.92476988,32.780379241,450190051002009,1,centroid,GOV1,LI,Natural/Wetland,
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+574446477,574446477,COM,COM8,4541890.36,4541890.36,11.32063936305046,-79.92744911,32.778311544,450190002001019,1,centroid,COM8,LB,Neighborhood,
+574451297,574451297,IND,IND2,4079100.73,6118651.09,7.781633021593094,-79.92643612,32.780279013,450190051002010,1,centroid,IND2,GB,City Centers,A-1
+574451456,574451456,COM,COM4,935142.526,935142.526,4.331387933154106,-79.92927368,32.780925182,450190051002047,1,centroid,COM4,GB,City Centers,A-1
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+574461287,574461287,RES,RES3A,266985.928,133492.964,9.784363229970932,-79.92738581,32.77970087,450190051002029,1,centroid,RES3A,GB,City Centers,A-1
+574488426,574488426,RES,RES1-1SNB,95269.9324,47634.9662,9.96529696782112,-79.92997893,32.782866752,450190051002045,1,centroid,RES1-1SNB,STR,Neighborhood,
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+573782604,573782604,RES,RES1-1SNB,265634.564,132817.282,6.46012056552887,-79.929988,32.7731275,450190002002005,1,centroid,RES1-1SNB,SR-4,Neighborhood,
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+573783191,573783191,RES,RES3A,502800.575,251400.287,12.530213979759216,-79.92999374,32.774658193,450190002002001,1,centroid,RES3A,SR-5,Neighborhood,
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+573783341,573783341,RES,RES1-2SNB,419384.196,209692.098,12.966943953037262,-79.93049453,32.777679672,450190002001021,1,centroid,RES1-2SNB,GB,Neighborhood,
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+573783357,573783357,RES,RES1-2SNB,250638.035,125319.017,12.137243360147476,-79.92983308,32.777886226,450190002001021,1,centroid,RES1-2SNB,SR-5,Neighborhood,
+573783411,573783411,RES,RES1-2SNB,508266.901,254133.45,7.1560001222705845,-79.92991341,32.770870666,450190002002010,1,centroid,RES1-2SNB,SR-3,Neighborhood,
+573783413,573783413,RES,RES1-3SNB,526257.487,263128.743,7.021283451995849,-79.93010077,32.770889895,450190002002010,1,centroid,RES1-3SNB,SR-3,Neighborhood,
+573783415,573783415,RES,RES1-3SWB,801030.917,400515.458,6.976162265129089,-79.92960692,32.770433827,450190002002010,1,centroid,RES1-3SWB,SR-3,Neighborhood,
+573783437,573783437,RES,RES3A,572861.422,286430.711,7.835369504766464,-79.93002946,32.771528793,450190002002010,1,centroid,RES3A,SR-3,Neighborhood,
+573785641,573785641,RES,RES3A,675819.872,337909.936,9.287814283590317,-79.9309266,32.772982975,450190002002004,1,centroid,RES3A,SR-3,Neighborhood,
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+573827767,573827767,COM,COM4,215407.622,215407.622,12.095946413669584,-79.9299812,32.777132109,450190002001028,1,centroid,COM4,LB,City Centers,
+573831660,573831660,RES,RES1-2SNB,378218.61,189109.305,10.287529700117112,-79.93101707,32.774974017,450190002001026,1,centroid,RES1-2SNB,SR-5,Neighborhood,
+573831683,573831683,RES,RES3A,505428.316,252714.158,10.25983700580597,-79.93099317,32.774783956,450190002001026,1,centroid,RES3A,SR-5,Neighborhood,
+573831701,573831701,RES,RES3A,461596.546,230798.273,11.66566891716957,-79.93123444,32.774908892,450190002001026,1,centroid,RES3A,SR-5,Neighborhood,
+573831703,573831703,PUB,GOV1,153865.515,153865.515,11.66566891716957,-79.93123444,32.774908892,450190002001026,1,centroid,GOV1,SR-5,Neighborhood,
+574268215,574268215,RES,RES3A,431803.856,215901.928,8.648343250350953,-79.93120313,32.771908318,450190002002009,1,centroid,RES3A,SR-3,Neighborhood,
+574268227,574268227,RES,RES1-3SNB,423994.154,211997.077,8.360911966028214,-79.93069143,32.772115719,450190002002009,1,centroid,RES1-3SNB,SR-3,Neighborhood,
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+574293783,574293783,COM,COM8,1271320.74,1271320.74,7.700334473714828,-79.93116856,32.7739649,450190002002002,1,centroid,COM8,SR-3,Neighborhood,
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+574487021,574487021,RES,RES1-1SNB,94772.4393,47386.2196,9.095769207077026,-79.93029798,32.779364989,450190051002058,1,centroid,RES1-1SNB,GB,Neighborhood,
+574487027,574487027,RES,RES1-1SNB,162374.542,81187.2711,9.095769207077026,-79.93029798,32.779364989,450190051002058,1,centroid,RES1-1SNB,GB,Neighborhood,
+574487099,574487099,RES,RES1-3SNB,444486.354,222243.177,11.501480017528534,-79.93099298,32.777963075,450190002001021,1,centroid,RES1-3SNB,GB,City Centers,
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+573789687,573789687,RES,RES3A,399569.107,199784.553,5.071663565115928,-79.93825076,32.772566349,450190001002008,1,centroid,RES3A,SR-2,Neighborhood,
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+574476734,574476734,RES,RES1-2SNB,214533.891,107266.945,5.8671571920728685,-79.93241085,32.769968112,450190002002018,1,centroid,RES1-2SNB,GB,Neighborhood,
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+574476702,574476702,RES,RES1-2SNB,259838.319,129919.159,6.07861132499218,-79.93406237,32.770313028,450190002002018,1,centroid,RES1-2SNB,SR-3,Neighborhood,
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+574487089,574487089,RES,RES3A,480189.335,240094.667,9.52555860253334,-79.93615824,32.776447461,450190001001008,1,centroid,RES3A,DR-1F,Neighborhood,
+574487092,574487092,RES,RES1-1SNB,137409.92,68704.9601,9.545346250133514,-79.93544376,32.776459504,450190001001007,1,centroid,RES1-1SNB,DR-1F,Neighborhood,
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+573800692,573800692,RES,RES3A,561796.474,280898.237,4.752157236967086,-79.94221862,32.773550407,450190001003009,1,centroid,RES3A,SR-2,Neighborhood,
+573800698,573800698,PUB,GOV1,238917.062,238917.062,4.688891441349983,-79.94320565,32.77510739,450190001003009,1,centroid,GOV1,SR-2,Neighborhood,
+573800714,573800714,RES,RES3A,495571.048,247785.524,4.547155580825805,-79.94176831,32.772408153,450190001003010,1,centroid,RES3A,SR-2,Neighborhood,
+573800716,573800716,RES,RES3B,339046.689,169523.344,4.726986006941796,-79.94191753,32.772502839,450190001003010,1,centroid,RES3B,SR-2,Neighborhood,
+573800728,573800728,RES,RES1-2SNB,366904.339,183452.169,4.914053078579903,-79.94171108,32.772902534,450190001003010,1,centroid,RES1-2SNB,SR-2,Neighborhood,
+573800732,573800732,RES,RES1-2SNB,368737.359,184368.679,4.34344222984314,-79.94203934,32.773043173,450190001003010,1,centroid,RES1-2SNB,SR-2,Neighborhood,
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+573800793,573800793,COM,COM3,64704.1031,64704.1031,4.958322435259819,-79.94360752,32.775436262,450190001003001,1,centroid,COM3,LB,Neighborhood,
+573800795,573800795,PUB,REL1,1701559.68,1701559.68,6.6316763491916655,-79.94304606,32.775950202,450190001003001,1,centroid,REL1,LB,Neighborhood,
+573800797,573800797,COM,COM4,1063474.8,1063474.8,6.6316763491916655,-79.94304606,32.775950202,450190001003001,1,centroid,COM4,LB,Neighborhood,
+573800801,573800801,COM,COM5,987899.431,987899.431,4.327391605858803,-79.9431408,32.776523032,450190001003001,1,centroid,COM5,LB,Neighborhood,
+573800805,573800805,RES,RES1-1SNB,240399.36,120199.68,4.997913374724388,-79.94356051,32.77701734,450190001003001,1,centroid,RES1-1SNB,STR,Neighborhood,
+573800809,573800809,RES,RES1-1SNB,218710.262,109355.131,5.465973724851608,-79.94368712,32.777222326,450190001003001,1,centroid,RES1-1SNB,STR,Neighborhood,
+573800813,573800813,RES,RES3A,571424.938,285712.469,6.713976129980087,-79.9433719,32.777366401,450190001003001,1,centroid,RES3A,STR,Neighborhood,
+573800817,573800817,RES,RES3A,260917.33,130458.665,7.194719676218033,-79.94308681,32.777419457,450190001003001,1,centroid,RES3A,STR,Neighborhood,
+573800819,573800819,RES,RES1-3SWB,422957.134,211478.567,8.788730966405868,-79.94253555,32.777464761,450190001003001,1,centroid,RES1-3SWB,STR,Neighborhood,
+573800823,573800823,RES,RES3A,513882.369,256941.184,5.465973724851608,-79.94369184,32.777227272,450190001003001,1,centroid,RES3A,STR,Neighborhood,
+573800826,573800826,RES,RES1-1SNB,254092.507,127046.253,4.997913374724388,-79.94356439,32.777033379,450190001003001,1,centroid,RES1-1SNB,STR,Neighborhood,
+573800969,573800969,RES,RES1-3SNB,398297.048,199148.524,4.917524931917191,-79.94242512,32.77358757,450190001003024,1,centroid,RES1-3SNB,SR-2,Neighborhood,
+573806311,573806311,RES,RES1-3SNB,243505.92,121752.96,7.777054727668762,-79.94320725,32.777784572,450190005002028,1,centroid,RES1-3SNB,STR,Neighborhood,
+573806312,573806312,RES,RES3A,147490.131,73745.0657,4.716619335255623,-79.94375884,32.777573207,450190005002028,1,centroid,RES3A,STR,Neighborhood,
+573860813,573860813,RES,RES3A,441312.99,220656.495,6.993137074022293,-79.94170131,32.778348266,450190005002017,1,centroid,RES3A,STR,Neighborhood,
+574296105,574296105,RES,RES1-2SNB,326388.249,163194.124,4.209433462524414,-79.94120099,32.772665114,450190001003023,1,centroid,RES1-2SNB,SR-2,Neighborhood,
+574422150,574422150,RES,RES3A,555462.891,277731.445,5.793754695453644,-79.94551694,32.776478611,450190005002029,1,centroid,RES3A,SR-2,Neighborhood,
+574431435,574431435,RES,RES1-1SNB,247455.456,123727.728,4.078337266097069,-79.94199805,32.775231928,450190001003008,1,centroid,RES1-1SNB,STR,Neighborhood,
+574431441,574431441,RES,RES1-2SNB,269317.399,134658.699,5.11859831331253,-79.94126543,32.773665544,450190001003008,1,centroid,RES1-2SNB,STR,Neighborhood,
+574431459,574431459,RES,RES1-1SNB,273155.575,136577.787,4.453472642283439,-79.94120491,32.773905274,450190001003008,1,centroid,RES1-1SNB,STR,Neighborhood,
+574431599,574431599,RES,RES1-1SNB,242593.004,121296.502,4.4480519047307965,-79.94445838,32.776922222,450190001003002,1,centroid,RES1-1SNB,SR-2,Neighborhood,
+574460772,574460772,COM,COM8,207879.805,207879.805,4.022045683469773,-79.94152807,32.774410541,450190001003008,1,centroid,COM8,STR,Park,
+574485649,574485649,RES,RES1-1SNB,102927.253,51463.6265,6.47364151198864,-79.94146718,32.778522856,450190005002017,1,centroid,RES1-1SNB,STR,Neighborhood,
+574485650,574485650,RES,RES1-1SNB,82253.4757,41126.7378,6.47364151198864,-79.94146718,32.778522856,450190005002017,1,centroid,RES1-1SNB,STR,Neighborhood,
+574485655,574485655,RES,RES1-1SNB,88088.0016,44044.0008,6.47364151198864,-79.94146718,32.778522856,450190005002017,1,centroid,RES1-1SNB,STR,Neighborhood,
+574485659,574485659,RES,RES1-1SNB,65421.18,32710.59,6.47364151198864,-79.94146718,32.778522856,450190005002017,1,centroid,RES1-1SNB,STR,Neighborhood,
+574485661,574485661,RES,RES1-1SNB,102927.253,51463.6265,6.47364151198864,-79.94146718,32.778522856,450190005002017,1,centroid,RES1-1SNB,STR,Neighborhood,
+574485666,574485666,RES,RES1-1SNB,74951.1782,37475.5891,6.47364151198864,-79.94146718,32.778522856,450190005002017,1,centroid,RES1-1SNB,STR,Neighborhood,
+574485667,574485667,RES,RES1-1SNB,65421.18,32710.59,6.47364151198864,-79.94146718,32.778522856,450190005002017,1,centroid,RES1-1SNB,STR,Neighborhood,
+574486695,574486695,RES,RES1-1SNB,102927.253,51463.6265,6.47364151198864,-79.94146718,32.778522856,450190005002017,1,centroid,RES1-1SNB,STR,Neighborhood,
+574486696,574486696,RES,RES1-1SNB,88320.5879,44160.2939,6.47364151198864,-79.94146718,32.778522856,450190005002017,1,centroid,RES1-1SNB,STR,Neighborhood,
+574486698,574486698,RES,RES1-1SNB,42145.0128,21072.5064,6.47364151198864,-79.94146718,32.778522856,450190005002017,1,centroid,RES1-1SNB,STR,Neighborhood,
+574486701,574486701,RES,RES1-1SNB,88320.5879,44160.2939,6.47364151198864,-79.94146718,32.778522856,450190005002017,1,centroid,RES1-1SNB,STR,Neighborhood,
+574486704,574486704,RES,RES1-1SNB,106483.093,53241.5467,6.47364151198864,-79.94146718,32.778522856,450190005002017,1,centroid,RES1-1SNB,STR,Neighborhood,
+574486730,574486730,RES,RES1-2SWB,176858.069,88429.0348,6.316693560042381,-79.94519888,32.77698147,450190005002024,1,centroid,RES1-2SWB,DR-1,Neighborhood,
+574486732,574486732,RES,RES1-2SWB,181426.792,90713.3963,6.223732995400429,-79.94502746,32.777048185,450190005002024,1,centroid,RES1-2SWB,DR-1,Neighborhood,
+574486736,574486736,RES,RES1-2SNB,253426.0,126713.0,5.118224415397644,-79.94338557,32.777176344,450190001003001,1,centroid,RES1-2SNB,STR,Neighborhood,
+574486737,574486737,RES,RES1-1SNB,215463.869,107731.934,7.85760235086441,-79.9427401,32.777416396,450190001003001,1,centroid,RES1-1SNB,STR,Neighborhood,
+574486746,574486746,RES,RES1-1SNB,123940.895,61970.4475,7.85760235086441,-79.9427401,32.777416396,450190001003001,1,centroid,RES1-1SNB,STR,Neighborhood,
+574486748,574486748,RES,RES1-1SNB,166009.113,83004.5568,7.85760235086441,-79.9427401,32.777416396,450190001003001,1,centroid,RES1-1SNB,STR,Neighborhood,
+574486750,574486750,RES,RES1-1SNB,206321.87,103160.935,7.85760235086441,-79.9427401,32.777416396,450190001003001,1,centroid,RES1-1SNB,STR,Neighborhood,
+574486753,574486753,RES,RES1-1SNB,213835.163,106917.581,7.85760235086441,-79.9427401,32.777416396,450190001003001,1,centroid,RES1-1SNB,STR,Neighborhood,
+574486755,574486755,RES,RES1-2SNB,250080.442,125040.221,7.85760235086441,-79.9427401,32.777416396,450190001003001,1,centroid,RES1-2SNB,STR,Neighborhood,
+574486769,574486769,RES,RES1-1SNB,214329.384,107164.692,6.284586445474624,-79.94233945,32.774283067,450190001003024,1,centroid,RES1-1SNB,DR-2,Neighborhood,
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+574486777,574486777,RES,RES1-2SNB,222200.794,111100.397,6.284586445474624,-79.94233945,32.774283067,450190001003024,1,centroid,RES1-2SNB,DR-2,Neighborhood,
+574486815,574486815,RES,RES1-1SNB,203264.754,101632.377,3.892720026645661,-79.94141607,32.775137429,450190001003008,1,centroid,RES1-1SNB,STR,Neighborhood,
+574486817,574486817,RES,RES1-1SNB,218561.014,109280.507,3.892720026645661,-79.94141607,32.775137429,450190001003008,1,centroid,RES1-1SNB,STR,Neighborhood,
+574486819,574486819,RES,RES1-1SNB,223773.423,111886.711,3.892720026645661,-79.94141607,32.775137429,450190001003008,1,centroid,RES1-1SNB,STR,Neighborhood,
+574486820,574486820,RES,RES1-1SNB,203264.754,101632.377,3.892720026645661,-79.94141607,32.775137429,450190001003008,1,centroid,RES1-1SNB,STR,Neighborhood,
+574486821,574486821,RES,RES1-1SNB,218561.014,109280.507,3.892720026645661,-79.94141607,32.775137429,450190001003008,1,centroid,RES1-1SNB,STR,Neighborhood,
+574486822,574486822,RES,RES1-1SNB,214165.178,107082.589,3.892720026645661,-79.94141607,32.775137429,450190001003008,1,centroid,RES1-1SNB,STR,Neighborhood,
diff --git a/examples/data/aggregation_zones/fiat_model_aggregation2/settings.toml b/examples/data/aggregation_zones/fiat_model_aggregation2/settings.toml
new file mode 100644
index 00000000..3ae7d2ea
--- /dev/null
+++ b/examples/data/aggregation_zones/fiat_model_aggregation2/settings.toml
@@ -0,0 +1,24 @@
+[global]
+crs = "epsg:4326"
+
+[output]
+path = "output"
+
+[output.csv]
+name = "output.csv"
+
+[output.geom]
+name1 = "spatial.gpkg"
+
+[vulnerability]
+file = "./vulnerability/vulnerability_curves.csv"
+unit = "feet"
+step_size = 0.1
+
+[exposure.csv]
+file = "./exposure/exposure.csv"
+
+[exposure.geom]
+crs = "EPSG:4326"
+unit = "ft"
+file1 = "./exposure/buildings.gpkg"
diff --git a/examples/data/aggregation_zones/fiat_model_aggregation2/vulnerability/vulnerability_curves.csv b/examples/data/aggregation_zones/fiat_model_aggregation2/vulnerability/vulnerability_curves.csv
new file mode 100644
index 00000000..6dcbb006
--- /dev/null
+++ b/examples/data/aggregation_zones/fiat_model_aggregation2/vulnerability/vulnerability_curves.csv
@@ -0,0 +1,37 @@
+#UNIT=feet
+#METHOD,max,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean
+water depth [feet],AGR1,COM1,COM10,COM2,COM3,COM4,COM5,COM6,COM7,COM8,COM9,EDU1,EDU2,GOV1,GOV2,IND1,IND2,IND3,IND4,IND5,IND6,REL1,RES1-1SNB,RES1-1SWB,RES1-2SNB,RES1-2SWB,RES1-3SNB,RES1-3SWB,RES2,RES3A,RES3B,RES3C,RES3D,RES3E,RES3F,RES4,RES5,RES6
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+-8.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1e-06,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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diff --git a/examples/data/building_footprints/building_footprints/building_footprints.gpkg b/examples/data/building_footprints/building_footprints/building_footprints.gpkg
new file mode 100644
index 00000000..c9a6ddee
Binary files /dev/null and b/examples/data/building_footprints/building_footprints/building_footprints.gpkg differ
diff --git a/examples/data/building_footprints/fiat_model/exposure/building_footprints/building_footprints.gpkg b/examples/data/building_footprints/fiat_model/exposure/building_footprints/building_footprints.gpkg
new file mode 100644
index 00000000..c9a6ddee
Binary files /dev/null and b/examples/data/building_footprints/fiat_model/exposure/building_footprints/building_footprints.gpkg differ
diff --git a/examples/data/building_footprints/fiat_model/exposure/buildings.gpkg b/examples/data/building_footprints/fiat_model/exposure/buildings.gpkg
new file mode 100644
index 00000000..568539a9
Binary files /dev/null and b/examples/data/building_footprints/fiat_model/exposure/buildings.gpkg differ
diff --git a/examples/data/building_footprints/fiat_model/exposure/exposure.csv b/examples/data/building_footprints/fiat_model/exposure/exposure.csv
new file mode 100644
index 00000000..471e0e26
--- /dev/null
+++ b/examples/data/building_footprints/fiat_model/exposure/exposure.csv
@@ -0,0 +1,3082 @@
+Object ID,Object Name,Primary Object Type,Secondary Object Type,Max Potential Damage: Structure,Max Potential Damage: Content,Ground Elevation,Aggregation Label: Census Block,Ground Floor Height,Extraction Method,Damage Function: Structure
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+574421614,574421614,PUB,REL1,1834696.67,1834696.67,-4.999869299,4.5019E+14,1,centroid,REL1
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+573800692,573800692,RES,RES3A,561796.474,280898.237,4.752157237,4.5019E+14,1,centroid,RES3A
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+573800791,573800791,COM,COM4,107840.171,107840.171,4.958322435,4.5019E+14,1,centroid,COM4
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+573800795,573800795,PUB,REL1,1701559.68,1701559.68,6.631676349,4.5019E+14,1,centroid,REL1
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diff --git a/examples/data/building_footprints/fiat_model/output/building_footprints/building_footprints.gpkg b/examples/data/building_footprints/fiat_model/output/building_footprints/building_footprints.gpkg
new file mode 100644
index 00000000..c9a6ddee
Binary files /dev/null and b/examples/data/building_footprints/fiat_model/output/building_footprints/building_footprints.gpkg differ
diff --git a/examples/data/building_footprints/fiat_model/settings.toml b/examples/data/building_footprints/fiat_model/settings.toml
new file mode 100644
index 00000000..6b0cb707
--- /dev/null
+++ b/examples/data/building_footprints/fiat_model/settings.toml
@@ -0,0 +1,22 @@
+[global]
+crs = "epsg:4326"
+
+[output]
+path = "output"
+
+[output.csv]
+name = "output.csv"
+
+[output.geom]
+name1 = "spatial.gpkg"
+
+[vulnerability]
+file = "./vulnerability/vulnerability_curves.csv"
+scale = 0.1
+
+[exposure.csv]
+file = "./exposure/exposure.csv"
+
+[exposure.geom]
+crs = "EPSG:4326"
+file1 = "./exposure/buildings.gpkg"
diff --git a/examples/data/building_footprints/fiat_model/vulnerability/vulnerability_curves.csv b/examples/data/building_footprints/fiat_model/vulnerability/vulnerability_curves.csv
new file mode 100644
index 00000000..6dcbb006
--- /dev/null
+++ b/examples/data/building_footprints/fiat_model/vulnerability/vulnerability_curves.csv
@@ -0,0 +1,37 @@
+#UNIT=feet
+#METHOD,max,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean
+water depth [feet],AGR1,COM1,COM10,COM2,COM3,COM4,COM5,COM6,COM7,COM8,COM9,EDU1,EDU2,GOV1,GOV2,IND1,IND2,IND3,IND4,IND5,IND6,REL1,RES1-1SNB,RES1-1SWB,RES1-2SNB,RES1-2SWB,RES1-3SNB,RES1-3SWB,RES2,RES3A,RES3B,RES3C,RES3D,RES3E,RES3F,RES4,RES5,RES6
+-9.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-8.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1e-06,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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diff --git a/examples/data/damage_functions/flooding/JRC_damage_functions.csv b/examples/data/damage_functions/flooding/JRC_damage_functions.csv
new file mode 100644
index 00000000..b0d1a72c
--- /dev/null
+++ b/examples/data/damage_functions/flooding/JRC_damage_functions.csv
@@ -0,0 +1,36 @@
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diff --git a/examples/data/hydromt_fiat_catalog_USA.yml b/examples/data/hydromt_fiat_catalog_USA.yml
new file mode 100644
index 00000000..38ebb75e
--- /dev/null
+++ b/examples/data/hydromt_fiat_catalog_USA.yml
@@ -0,0 +1,45 @@
+
+NSI:
+ path: "https://nsi.sec.usace.army.mil/nsiapi/structures?fmt=fc"
+ data_type: GeoDataFrame
+ driver: vector
+ crs: 4326
+ translation_fn: "attribute_linking/NSI_attributes_to_FIAT.json"
+ meta:
+ category: exposure
+
+default_vulnerability_curves:
+ path: damage_functions/flooding/Hazus_IWR_curves.csv
+ data_type: DataFrame
+ driver: csv
+ meta:
+ category: vulnerability
+ source: HAZUS SQL database, USACE-IWR and FEMA expert elicitation curves. The source of these curves is the Draft Report Nonresidential Flood Depth-Damage Functions Derived from Expert Elicitation, April 2009, Revised 2013. FEMA Contract Number HSFEHQ-06-D-0162. Task Order HSFEHQ-08-J-0014. Not for distribution, but data shared by USACE.
+
+jrc_vulnerability_curves:
+ path: damage_functions/flooding/JRC_damage_functions.xlsx
+ data_type: DataFrame
+ driver: xlsx
+ meta:
+ category: vulnerability
+
+hazus_max_potential_damages:
+ path: max_potential_damages/damage_values_fema_hazus-inventory-technical-manual-4.2.3.xlsx
+ data_type: DataFrame
+ driver: xlsx
+ meta:
+ category: vulnerability
+
+social_vulnerability:
+ path: social_vulnerability/census_vulnerability_data_codebook.xlsx
+ data_type: DataFrame
+ driver: xlsx
+ meta:
+ category: social_vulnerability
+
+default_hazus_iwr_linking:
+ path: vulnerability_linking/default_hazus_iwr_curve_linking.csv
+ data_type: DataFrame
+ driver: csv
+ meta:
+ category: vulnerability
\ No newline at end of file
diff --git a/examples/data/hydromt_fiat_catalog_global.yml b/examples/data/hydromt_fiat_catalog_global.yml
new file mode 100644
index 00000000..982f52df
--- /dev/null
+++ b/examples/data/hydromt_fiat_catalog_global.yml
@@ -0,0 +1,30 @@
+
+jrc_vulnerability_curves:
+ path: damage_functions/flooding/JRC_damage_functions.csv
+ data_type: DataFrame
+ driver: csv
+ meta:
+ category: vulnerability
+ description: JRC depth-damage functions for flooding, processed into a handy format for HydroMT-FIAT.
+ source_url: https://publications.jrc.ec.europa.eu/repository/handle/JRC105688
+ paper_ref: Huizinga, J., De Moel, H. and Szewczyk, W., Global flood depth-damage functions - Methodology and the database with guidelines, EUR 28552 EN, Publications Office of the European Union, Luxembourg, 2017, ISBN 978-92-79-67781-6, doi:10.2760/16510, JRC105688.
+ paper_doi: https://dx.doi.org/10.2760/16510
+
+jrc_vulnerability_curves_linking:
+ path: vulnerability_linking/JRC_damage_functions_linking.csv
+ data_type: DataFrame
+ driver: csv
+ meta:
+ category: vulnerability
+ description: Default linking table for the JRC damage functions (e.g., the residential damage function links to residential buildings).
+
+jrc_damage_values:
+ path: max_potential_damages/JRC_base_damage_values.csv
+ data_type: DataFrame
+ driver: csv
+ meta:
+ category: vulnerability
+ description: Base damage values from the JRC publicated Excel from the tab "MaxDamage-Data", processed into a handy format for HydroMT-FIAT.
+ source_url: https://publications.jrc.ec.europa.eu/repository/handle/JRC105688
+ paper_ref: Huizinga, J., De Moel, H. and Szewczyk, W., Global flood depth-damage functions - Methodology and the database with guidelines, EUR 28552 EN, Publications Office of the European Union, Luxembourg, 2017, ISBN 978-92-79-67781-6, doi:10.2760/16510, JRC105688.
+ paper_doi: https://dx.doi.org/10.2760/16510
\ No newline at end of file
diff --git a/examples/data/max_potential_damages/JRC_base_damage_values.csv b/examples/data/max_potential_damages/JRC_base_damage_values.csv
new file mode 100644
index 00000000..60c698e3
--- /dev/null
+++ b/examples/data/max_potential_damages/JRC_base_damage_values.csv
@@ -0,0 +1,249 @@
+Country,ISO A3 code,Construction Cost Residential (2010 €),Construction Cost Commercial (2010 €),Construction Cost Industrial (2010 €),GDP per capita (2010 US$)
+Afghanistan,AFG,275.790831,323.1586611,241.0331217,561.1976175
+Albania,ALB,597.450818,662.0776912,462.6412974,4175.121638
+Algeria,DZA,606.945797,671.8348045,468.835172,4349.569325
+American Samoa,ASM,0,0,0,
+Andorra,AND,0,0,0,
+Angola,AGO,599.8424625,664.5364111,464.2028853,4218.649126
+Antigua and Barbuda,ATG,925.8358632,994.0579546,669.4173335,13017.31039
+Argentina,ARG,881.5030426,949.8160048,642.2775019,11460.37615
+Armenia,ARM,534.3491473,596.9397752,421.0716996,3124.784854
+Aruba,ABW,1177.283822,1242.30466,819.8028211,24289.14152
+Australia,AUS,1576.098086,1628.49359,1048.521336,51800.93139
+Austria,AUT,1511.201454,1566.185027,1011.987128,46444.18444
+Azerbaijan,AZE,680.0220464,746.5730574,516.0173514,5842.805784
+"Bahamas, The",BHS,1132.084932,1197.989467,793.1745966,21941.8693
+Bahrain,BHR,1103.780434,1170.17358,776.414905,20545.96702
+Bangladesh,BGD,310.4029399,360.625262,266.308433,762.8037395
+Barbados,BRB,997.8679814,1065.620182,713.0866498,15812.27978
+Belarus,BLR,678.9469233,745.4778393,515.3291481,5818.854859
+Belgium,BEL,1484.68729,1540.673085,996.9904855,44358.26064
+Belize,BLZ,616.3835677,681.5222187,474.9767133,4527.336639
+Benin,BEN,298.6383882,347.9260805,257.7695017,690.0022811
+Bermuda,BMU,1934.767604,1969.723079,1246.474681,88207.32756
+Bhutan,BTN,467.7154928,527.5476903,376.3289318,2211.340513
+Bolivia,BOL,444.243464,502.9384002,360.3356987,1934.666067
+Bosnia and Herzegovina,BIH,608.6103253,673.544147,469.9194277,4380.604285
+Botswana,BWA,728.2528456,795.5798592,546.7199689,6980.361812
+Brazil,BRA,867.0295965,935.3378024,633.3712371,10978.26024
+Brunei Darussalam,BRN,1291.355959,1353.612025,886.3121928,30880.34486
+Bulgaria,BGR,711.9044621,778.995481,536.3496982,6580.813875
+Burkina Faso,BFA,281.6373864,329.510134,245.3358608,592.6074699
+Burundi,BDI,192.1160722,231.0636218,177.6806883,219.5297995
+Cabo Verde,CPV,552.8374016,616.079408,433.3271136,3413.26337
+Cambodia,KHM,313.4844445,363.945784,268.5366067,782.6188886
+Cameroon,CMR,363.0163506,417.0123338,303.9067621,1145.368992
+Canada,CAN,1523.914831,1578.406357,1019.163284,47465.34593
+Cayman Islands,CYM,0,0,0,
+Central African Republic,CAF,254.718351,300.1839723,225.4036291,456.5634087
+Chad,TCD,332.1348161,383.993383,281.9505512,909.2999069
+Channel Islands,CHA,0,0,0,
+Chile,CHL,916.5691459,984.8231166,663.7615377,12681.7652
+China,CHN,611.4222514,676.4310131,471.7500326,4433.340886
+Colombia,COL,694.8690263,761.6848526,525.5038566,6179.770329
+Comoros,COM,309.4612565,359.6100632,265.6268313,756.8106118
+"Congo, Dem. Rep.",COD,229.0934606,272.0590788,206.1207618,346.7042747
+"Congo, Rep.",COG,520.6060175,582.6815345,411.9187266,2920.406708
+Costa Rica,CRI,759.0653322,826.7646519,566.16709,7773.185487
+Cote d'Ivoire,CIV,382.4394043,437.6749266,317.5654555,1311.326565
+Croatia,HRV,938.9350803,1007.100749,677.3971539,13500.85443
+Cuba,CUB,673.6603667,740.0906478,511.9426519,5701.962671
+Curacao,CUW,0,0,0,
+Cyprus,CYP,1241.656317,1305.20806,857.4530452,27889.03718
+Czech Republic,CZE,1087.405517,1154.0579,766.6883148,19764.01554
+Denmark,DNK,1642.382404,1691.943646,1085.594448,57647.92502
+Djibouti,DJI,387.0973395,442.6187238,320.824687,1353.193028
+Dominica,DMA,726.0971561,793.3946024,545.3546531,6926.847597
+Dominican Republic,DOM,654.7359791,720.7808145,499.7855535,5295.39969
+Ecuador,ECU,622.0766024,687.360658,478.6742762,4636.693098
+"Egypt, Arab Rep.",EGY,512.4796597,574.2378458,406.4887842,2803.532963
+El Salvador,SLV,554.7780738,618.0857491,434.6097835,3444.456148
+Equatorial Guinea,GNQ,1017.629948,1085.186967,724.9797255,16638.13107
+Eritrea,ERI,234.5981433,278.1191478,210.2903648,368.7477582
+Estonia,EST,968.4306262,1036.421489,695.3020278,14629.64939
+Ethiopia,ETH,228.3242145,271.2113894,205.5368436,343.6901516
+Faeroe Islands,FRO,0,0,0,
+Fiji,FJI,567.2667272,630.9849618,442.8473691,3649.382362
+Finland,FIN,1508.166341,1563.266288,1010.272543,46202.41516
+France,FRA,1436.35291,1494.080603,969.5434816,40706.07833
+French Polynesia,PYF,0,0,0,
+Gabon,GAB,815.4430557,883.5900983,601.434557,9362.113583
+"Gambia, The",GMB,276.7630811,324.2155444,241.7496257,566.3481084
+Georgia,GEO,498.8281616,560.0314127,397.3364967,2613.756925
+Germany,DEU,1450.075218,1507.319721,977.3502816,41723.3659
+Ghana,GHA,384.0924639,439.4299309,318.7228352,1326.092033
+Greece,GRC,1223.830116,1287.812776,847.0581133,26861.4598
+Greenland,GRL,0,0,0,
+Grenada,GRD,743.4820588,811.0047431,556.3475655,7365.666529
+Guam,GUM,0,0,0,
+Guatemala,GTM,517.9847047,579.9589069,410.1686539,2882.386026
+Guinea,GIN,250.114583,295.1467169,221.9625409,435.448888
+Guinea-Bissau,GNB,270.592359,317.5030796,237.1953335,534.1475781
+Guyana,GUY,517.4003498,579.3518311,409.7783311,2873.952008
+Haiti,HTI,295.1353889,344.1378689,255.2168608,669.1869294
+Honduras,HND,456.6712768,515.979746,368.8197349,2078.32576
+"Hong Kong SAR, China",HKG,1317.816811,1379.327906,901.6060265,32549.99823
+Hungary,HUN,924.2232407,992.4513611,668.4337339,12958.53036
+Iceland,ISL,1449.7087,1506.966227,977.1419154,41695.99365
+India,IND,394.0367851,449.9761073,325.6689774,1417.073614
+Indonesia,IDN,522.4035424,584.5479623,413.1180146,2946.656061
+"Iran, Islamic Rep.",IRN,672.4280463,738.8344312,511.1526473,5674.923927
+Iraq,IRQ,613.5608975,678.626014,473.14144,4473.71044
+Ireland,IRL,1529.285536,1583.566984,1022.191997,47900.83929
+Isle of Man,IMN,0,0,0,
+Israel,ISR,1286.031985,1348.433368,883.2291431,30550.9204
+Italy,ITA,1368.137169,1428.129461,930.5586385,35875.72655
+Jamaica,JAM,636.3032182,701.9338614,487.8912838,4917.023202
+Japan,JPN,1468.555098,1525.134621,987.8454877,43117.82967
+Jordan,JOR,608.0810369,673.0006451,469.5747053,4370.721045
+Kazakhstan,KAZ,805.5689623,873.6586347,595.286063,9070.649972
+Kenya,KEN,341.5552543,394.0884321,288.6809109,977.7780694
+Kiribati,KIR,406.770906,463.452974,334.5239165,1539.047436
+"Korea, Dem. Rep.",PRK,432.0672802,490.1355459,351.9873315,1800
+"Korea, Rep.",KOR,1136.233273,1202.061951,795.6253707,22151.20912
+Kosovo,KOS,544.640388,607.5993904,427.9015587,3283.429596
+Kuwait,KWT,1407.040382,1465.76952,952.8278994,38584.4796
+Kyrgyz Republic,KGZ,327.9761899,379.5304071,278.9699811,880.0377751
+Lao PDR,LAO,360.2505816,414.063661,301.9526333,1122.852316
+Latvia,LVA,881.092001,949.4050662,642.0248841,11446.50818
+Lebanon,LBN,794.6826286,862.6988576,588.4935637,8755.849968
+Lesotho,LSO,355.2721427,408.7518639,298.4292267,1083.012065
+Liberia,LBR,223.8832668,266.3135253,202.1597549,326.6042804
+Libya,LBY,907.9771115,976.2546195,658.5095224,12375.44953
+Liechtenstein,LIE,0,0,0,
+Lithuania,LTU,892.9915407,961.2960566,649.3306751,11852.1696
+Luxembourg,LUX,2052.734797,2080.914052,1310.278866,102856.9717
+"Macao SAR, China",MAC,1590.582595,1642.374927,1056.64309,53045.87903
+"Macedonia, FYR",MKD,611.8979047,676.919248,472.0595594,4442.299972
+Madagascar,MDG,245.3277238,289.9020277,218.3740678,414.1427872
+Malawi,MWI,232.3339239,275.6277399,208.5771733,359.5795268
+Malaysia,MYS,794.6264382,862.6422601,588.4584662,8754.24282
+Maldives,MDV,710.7223976,777.7952946,535.5984431,6552.484491
+Mali,MLI,295.8996139,344.9645921,255.7741553,673.6946614
+Malta,MLT,1085.946695,1152.621327,765.8206764,19695.25572
+Marshall Islands,MHL,534.4631835,597.057974,421.1474927,3126.516365
+Mauritania,MRT,341.4708011,393.9980211,288.6207041,977.1505551
+Mauritius,MUS,752.0055697,819.6277895,561.7224544,7586.893454
+Mexico,MEX,800.4125209,868.4687473,592.070518,8920.689016
+"Micronesia, Fed. Sts.",FSM,514.928846,576.7836822,408.1267129,2838.448547
+Moldova,MDA,416.0184836,473.2208128,340.9271882,1631.535832
+Monaco,MCO,2344.468082,2353.968073,1465.68601,145229.8379
+Mongolia,MNG,473.7078907,533.8159787,380.3916466,2285.645378
+Montenegro,MNE,714.2011167,781.326934,537.8087674,6636.070538
+Morocco,MAR,513.8288217,575.640357,407.391207,2822.733739
+Mozambique,MOZ,247.5910579,292.3827458,220.0721313,424.134902
+Myanmar,MMR,377.7509832,432.6943819,314.2786109,1270
+Namibia,NAM,649.0906145,715.0127142,496.1483559,5177.681179
+Nepal,NPL,282.2156901,330.1378607,245.7606959,595.7716261
+Netherlands,NLD,1558.804244,1611.907838,1038.809007,50338.25483
+New Caledonia,NCL,0,0,0,
+New Zealand,NZL,1322.427218,1383.804681,904.265806,32846.45443
+Nicaragua,NIC,406.3781041,463.0377209,334.2514286,1535.192167
+Niger,NER,232.3890957,275.6884683,208.6189492,359.801242
+Nigeria,NGA,475.7141306,535.913304,381.7500316,2310.860518
+Northern Mariana Islands,MNP,0,0,0,
+Norway,NOR,1916.796979,1952.742448,1236.702354,86096.13602
+Oman,OMN,1111.53211,1177.796506,781.0114561,20922.65677
+Pakistan,PAK,347.5813879,400.5355391,292.970969,1023.195756
+Palau,PLW,825.0494476,893.243978,607.4051799,9651.133366
+Panama,PAN,761.3438091,829.0669876,567.6001738,7833.903551
+Papua New Guinea,PNG,390.2667108,445.9801458,323.0388349,1382.143749
+Paraguay,PRY,532.7678328,595.3005511,420.0204319,3100.835119
+Peru,PER,644.1248989,709.9360463,492.9449479,5075.47667
+Philippines,PHL,461.5050106,521.0451568,372.1097517,2135.918407
+Poland,POL,911.0387691,979.3085581,660.3818969,12484.07315
+Portugal,PRT,1143.847869,1209.534515,800.1203128,22538.65408
+Puerto Rico,PRI,1216.361923,1280.519692,842.6961812,26437.99218
+Qatar,QAT,1784.525908,1827.396678,1164.321909,71510.15575
+Romania,ROU,772.636806,840.4709292,574.6932186,8139.146673
+Russian Federation,RUS,858.7993592,927.0970961,628.2963785,10709.76936
+Rwanda,RWA,268.9663648,315.7325058,235.9925769,525.8549278
+Samoa,WSM,555.5411892,618.8745482,435.1139652,3456.76966
+San Marino,SMR,0,0,0,
+Sao Tome and Principe,STP,360.8836083,414.7386942,302.4000991,1127.981667
+Saudi Arabia,SAU,1078.070945,1144.863283,761.1333947,19326.58255
+Senegal,SEN,344.3388056,397.0674482,290.6640058,998.5995764
+Serbia,SCG,659.6547978,725.8036465,502.9506504,5399.29762
+Seychelles,SYC,862.8920742,931.1957262,630.8209406,10842.77306
+Sierra Leone,SLE,252.9154757,298.2121289,224.0572387,448.2215321
+Singapore,SGP,1512.773171,1567.696315,1012.874806,46569.68843
+Sint Maarten (Dutch part),SXM,0,0,0,
+Slovak Republic,SVK,1014.601583,1082.190305,723.1595686,16509.89667
+Slovenia,SVN,1160.829551,1226.186556,810.1278932,23417.64466
+Solomon Islands,SLB,380.5634073,435.6825729,316.2510383,1294.692732
+Somalia,SOM,212.14785,253.3365088,193.1844317,284
+South Africa,ZAF,736.0334895,803.4633501,551.6426293,7175.624804
+South Sudan,SSD,411.1219459,468.0507742,337.5395011,1582.150503
+Spain,ESP,1289.027494,1351.347305,884.9640521,30736.00228
+Sri Lanka,LKA,482.7017419,543.2131961,386.4742332,2400.015575
+St. Kitts and Nevis,KNA,931.5511801,999.7502804,672.9011663,13226.95231
+St. Lucia,LCA,729.6105529,796.9559492,547.5795542,7014.196449
+St. Martin (French part),MAF,0,0,0,
+St. Vincent and the Grenadines,VCT,697.1130351,763.9668472,526.934895,6231.713226
+Sudan,SDN,396.4297508,452.5110168,327.3363534,1439.523185
+Suriname,SUR,779.2555534,847.1491,578.8428506,8321.392197
+Swaziland,SWZ,543.2422879,606.1521047,426.9748931,3261.593263
+Sweden,SWE,1579.319664,1631.581807,1050.328746,52076.25591
+Switzerland,CHE,1810.809955,1852.35646,1178.769909,74276.71842
+Syrian Arab Republic,SYR,645.3219567,711.1601083,493.7175284,5100
+Tajikistan,TJK,306.7523615,356.6884442,263.6642858,739.7321918
+Tanzania,TZA,268.7374388,315.4831622,235.8231478,524.6937952
+Thailand,THA,630.5612984,696.0549175,484.1751854,4802.662758
+Timor-Leste,TLS,327.372186,378.8818605,278.536589,875.8365693
+Togo,TGO,264.4345572,310.7936467,232.6343396,503.161824
+Tonga,TON,561.0692414,624.5863264,438.7630607,3546.77782
+Trinidad and Tobago,TTO,993.4224002,1061.214673,710.4061679,15630.05045
+Tunisia,TUN,599.4428035,664.1255927,463.942002,4211.356086
+Turkey,TUR,840.7678195,909.0226081,617.1511737,10135.74889
+Turkmenistan,TKM,609.258149,674.2093209,470.3412876,4392.719583
+Turks and Caicos Islands,TCA,0,0,0,
+Tuvalu,TUV,541.7571852,604.6144612,425.9901528,3238.496111
+Uganda,UGA,257.9339136,303.6984072,227.8013233,471.6772799
+Ukraine,UKR,524.2653458,586.480644,414.3595076,2973.996481
+United Arab Emirates,ARE,1338.39364,1399.29964,913.4657812,33885.92544
+United Kingdom,GBR,1403.929998,1462.762918,951.0510097,38363.44144
+United States,USA,1535.128339,1589.179758,1025.485052,48377.39385
+Uruguay,URY,883.5808782,951.8931086,643.5542154,11530.6375
+Uzbekistan,UZB,389.7155571,445.3957364,322.6539974,1377.08214
+Vanuatu,VUT,523.7050483,585.8990684,413.9859612,2965.75223
+"Venezuela, RB",VEN,940.4940871,1008.652162,678.3457096,13559.1268
+Vietnam,VNM,384.9268446,440.3155609,319.3067255,1333.583526
+Virgin Islands (U.S.),VIR,0,0,0,
+West Bank and Gaza,WBK,477.9151691,538.2135379,383.2392798,2338.719877
+"Yemen, Rep.",YEM,391.6104795,447.4047457,323.9767516,1394.532356
+Zambia,ZMB,406.1834985,462.8319817,334.1164149,1533.284355
+Zimbabwe,ZWE,304.0875047,353.8125042,261.7309963,723.1646486
+Arab World,,688.4632967,755.1677558,521.4148354,6032.961957
+Caribbean small states,,790.1940967,858.176897,585.6887235,8628.040548
+Central Europe and the Baltics,,910.1484031,978.4205134,659.8374901,12452.42393
+East Asia & Pacific (all income levels),,747.1326956,814.69888,558.6508158,7459.925785
+East Asia & Pacific (developing only),,581.1203983,645.2701263,451.952199,3885.285375
+Euro area,,1396.099176,1455.191248,946.5747105,37810.39975
+Europe & Central Asia (all income levels),,1160.579708,1225.941692,809.9808238,23404.56246
+Europe & Central Asia (developing only),,695.6676619,762.497068,526.0132414,6198.226006
+European Union,,1333.40006,1394.454954,910.5902978,33558.68486
+Fragile and conflict affected situations,,385.0336564,440.4289233,319.3814569,1334.544411
+Heavily indebted poor countries (HIPC),,304.396205,354.1457495,261.9550863,725.0720456
+High income,,1370.68967,1430.60143,932.0227703,36049.74575
+High income: nonOECD,,1011.16617,1078.790074,721.0937306,16365.16378
+High income: OECD,,1436.538508,1494.259726,969.6491483,40719.73453
+Latin America & Caribbean (all income levels),,800.4477823,868.5042456,592.092518,8921.709283
+Latin America & Caribbean (developing only),,789.8817706,857.8621759,585.4934613,8619.190138
+Least developed countries: UN classification,,306.9202877,356.8696113,263.786024,740.783932
+Low & middle income,,550.2755514,613.4300903,431.6328016,3372.353103
+Low income,,278.0739669,325.6401173,242.7150655,573.3383774
+Lower middle income,,428.2373211,486.1031184,349.3538185,1758.870697
+Middle East & North Africa (all income levels),,731.6080402,798.9801455,548.8437391,7064.157652
+Middle East & North Africa (developing only),,587.8962986,652.2480899,456.3930076,4003.990437
+Middle income,,578.1200708,642.1784542,449.9832508,3833.423532
+North America,,1534.116169,1588.207548,1024.914724,48294.6308
+Not classified,,0,0,0,
+OECD members,,1365.472771,1425.548772,929.029866,35694.63064
+Other small states,,603.3144103,668.104474,466.4681079,4282.33261
+Pacific island small states,,519.2084856,581.2301108,410.9858615,2900.098166
+Small states,,651.8023465,717.7838518,497.896087,5234.023815
+South Asia,,380.9688613,436.1132346,316.5352054,1298.2767
+Sub-Saharan Africa (all income levels),,404.7294533,461.2945231,333.1073044,1519.075782
+Sub-Saharan Africa (developing only),,403.459255,459.9511318,332.2253196,1506.730202
+Upper middle income,,686.6152167,753.2867342,520.2340306,5991.009883
+World,,819.1951012,887.361663,603.7678544,9474.35556
diff --git a/examples/data/road_network/configuration.yaml b/examples/data/road_network/configuration.yaml
new file mode 100644
index 00000000..f0c0b7ca
--- /dev/null
+++ b/examples/data/road_network/configuration.yaml
@@ -0,0 +1,12 @@
+setup_road_vulnerability:
+ vertical_unit: feet
+ threshold_value: 0.6
+ min_hazard_value: 0
+ max_hazard_value: 10
+ step_hazard_value: 1
+setup_exposure_roads:
+ roads_fn: OSM
+ road_types: ["motorway", "primary", "secondary", "tertiary"]
+ road_damage: default_road_max_potential_damages
+ unit: meter
+
\ No newline at end of file
diff --git a/examples/data/road_network/hydromt_fiat_catalog_USA.yml b/examples/data/road_network/hydromt_fiat_catalog_USA.yml
new file mode 100644
index 00000000..40481d85
--- /dev/null
+++ b/examples/data/road_network/hydromt_fiat_catalog_USA.yml
@@ -0,0 +1,57 @@
+
+NSI:
+ path: "https://nsi.sec.usace.army.mil/nsiapi/structures?fmt=fc"
+ data_type: GeoDataFrame
+ driver: vector
+ crs: 4326
+ translation_fn: "attribute_linking/NSI_attributes_to_FIAT.json"
+ meta:
+ category: exposure
+
+default_vulnerability_curves:
+ path: damage_functions/flooding/Hazus_IWR_curves.csv
+ data_type: DataFrame
+ driver: csv
+ meta:
+ unit: ft
+ category: vulnerability
+ source: HAZUS SQL database, USACE-IWR and FEMA expert elicitation curves. The source of these curves is the Draft Report Nonresidential Flood Depth-Damage Functions Derived from Expert Elicitation, April 2009, Revised 2013. FEMA Contract Number HSFEHQ-06-D-0162. Task Order HSFEHQ-08-J-0014. Not for distribution, but data shared by USACE.
+
+jrc_vulnerability_curves:
+ path: damage_functions/flooding/JRC_damage_functions.xlsx
+ data_type: DataFrame
+ driver: xlsx
+ meta:
+ unit: m
+ category: vulnerability
+
+hazus_max_potential_damages:
+ path: max_potential_damages/damage_values_fema_hazus-inventory-technical-manual-4.2.3.xlsx
+ data_type: DataFrame
+ driver: xlsx
+ meta:
+ unit: ft
+ category: vulnerability
+
+social_vulnerability:
+ path: social_vulnerability/census_vulnerability_data_codebook.xlsx
+ data_type: DataFrame
+ driver: xlsx
+ meta:
+ category: social_vulnerability
+
+default_hazus_iwr_linking:
+ path: vulnerability_linking/default_hazus_iwr_curve_linking.csv
+ data_type: DataFrame
+ driver: csv
+ meta:
+ category: vulnerability
+
+default_road_max_potential_damages:
+ path: us_road_damage.csv
+ data_type: DataFrame
+ driver: csv
+ meta:
+ unit: ft
+ category: exposure
+ source: Bouwer, Laurens & Haasnoot, Marjolijn & Wagenaar, Dennis & Roscoe, Kathryn. (2018). Assessment of alternative flood mitigation strategies for the C-7 Basin in Miami, Florida.
\ No newline at end of file
diff --git a/examples/data/road_network/us_road_damage.csv b/examples/data/road_network/us_road_damage.csv
new file mode 100644
index 00000000..54ceaf71
--- /dev/null
+++ b/examples/data/road_network/us_road_damage.csv
@@ -0,0 +1,7 @@
+lanes,cost [USD/ft]
+1,240
+2,240
+3,360
+4,480
+5,600
+6,720
diff --git a/examples/data/setup_exposure_buildings/test_vulnerability_and_exposure_NSI/exposure/buildings.gpkg b/examples/data/setup_exposure_buildings/test_vulnerability_and_exposure_NSI/exposure/buildings.gpkg
new file mode 100644
index 00000000..243e8336
Binary files /dev/null and b/examples/data/setup_exposure_buildings/test_vulnerability_and_exposure_NSI/exposure/buildings.gpkg differ
diff --git a/examples/data/setup_exposure_buildings/test_vulnerability_and_exposure_NSI/exposure/exposure.csv b/examples/data/setup_exposure_buildings/test_vulnerability_and_exposure_NSI/exposure/exposure.csv
new file mode 100644
index 00000000..e1022c4f
--- /dev/null
+++ b/examples/data/setup_exposure_buildings/test_vulnerability_and_exposure_NSI/exposure/exposure.csv
@@ -0,0 +1,1995 @@
+Object ID,Object Name,Primary Object Type,Secondary Object Type,Max Potential Damage: Structure,Max Potential Damage: Content,Ground Floor Height,Ground Elevation,Aggregation Label: Census Block,Extraction Method,Damage Function: Structure,Damage Function: Content
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diff --git a/examples/data/setup_exposure_buildings/test_vulnerability_and_exposure_NSI/exposure/region.gpkg b/examples/data/setup_exposure_buildings/test_vulnerability_and_exposure_NSI/exposure/region.gpkg
new file mode 100644
index 00000000..5be1ab4b
Binary files /dev/null and b/examples/data/setup_exposure_buildings/test_vulnerability_and_exposure_NSI/exposure/region.gpkg differ
diff --git a/examples/data/setup_exposure_buildings/test_vulnerability_and_exposure_NSI/settings.toml b/examples/data/setup_exposure_buildings/test_vulnerability_and_exposure_NSI/settings.toml
new file mode 100644
index 00000000..3ae7d2ea
--- /dev/null
+++ b/examples/data/setup_exposure_buildings/test_vulnerability_and_exposure_NSI/settings.toml
@@ -0,0 +1,24 @@
+[global]
+crs = "epsg:4326"
+
+[output]
+path = "output"
+
+[output.csv]
+name = "output.csv"
+
+[output.geom]
+name1 = "spatial.gpkg"
+
+[vulnerability]
+file = "./vulnerability/vulnerability_curves.csv"
+unit = "feet"
+step_size = 0.1
+
+[exposure.csv]
+file = "./exposure/exposure.csv"
+
+[exposure.geom]
+crs = "EPSG:4326"
+unit = "ft"
+file1 = "./exposure/buildings.gpkg"
diff --git a/examples/data/setup_exposure_buildings/test_vulnerability_and_exposure_NSI/vulnerability/vulnerability_curves.csv b/examples/data/setup_exposure_buildings/test_vulnerability_and_exposure_NSI/vulnerability/vulnerability_curves.csv
new file mode 100644
index 00000000..b7baee0f
--- /dev/null
+++ b/examples/data/setup_exposure_buildings/test_vulnerability_and_exposure_NSI/vulnerability/vulnerability_curves.csv
@@ -0,0 +1,37 @@
+#UNIT=feet
+#METHOD,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean
+water depth [feet],AGR1_structure,COM1_structure,COM10_structure,COM2_structure,COM3_structure,COM4_structure,COM5_structure,COM6_structure,COM7_structure,COM8_structure,COM9_structure,EDU1_structure,EDU2_structure,GOV1_structure,GOV2_structure,IND1_structure,IND2_structure,IND3_structure,IND4_structure,IND5_structure,IND6_structure,REL1_structure,RES1-1SNB_structure,RES1-1SWB_structure,RES1-2SNB_structure,RES1-2SWB_structure,RES1-3SNB_structure,RES1-3SWB_structure,RES2_structure,RES3A_structure,RES3B_structure,RES3C_structure,RES3D_structure,RES3E_structure,RES3F_structure,RES4_structure,RES5_structure,RES6_structure,AGR1_content,COM1_content,COM10_content,COM2_content,COM3_content,COM4_content,COM5_content,COM6_content,COM7_content,COM8_content,COM9_content,EDU1_content,EDU2_content,GOV1_content,GOV2_content,IND1_content,IND2_content,IND3_content,IND4_content,IND5_content,IND6_content,REL1_content,RES1-1SNB_content,RES1-1SWB_content,RES1-2SNB_content,RES1-2SWB_content,RES1-3SNB_content,RES1-3SWB_content,RES2_content,RES3A_content,RES3B_content,RES3C_content,RES3D_content,RES3E_content,RES3F_content,RES4_content,RES5_content,RES6_content
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+-7.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.007,0.0,0.017,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.008,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.008,0.0,0.019,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.021,0.0,0.023,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.024,0.0,0.029,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.037,0.0,0.037,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.052,0.0,0.047,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.057,0.0,0.052,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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+21.0,0.92,0.74,0.86,0.68,0.525,0.624,0.67,0.843,0.59,0.69,0.8,0.52,0.79,0.87,0.76,0.57,0.562,0.44,0.5,0.44,0.75,0.7,0.807,0.811,0.692,0.764,0.6,0.56,0.82,0.467,0.467,0.467,0.467,0.467,0.467,0.475,0.508,0.77,0.79,0.94,1.0,0.89,0.871,1.0,1.0,0.963,1.0,0.96,1.0,0.634,1.0,1.0,1.0,0.74,0.95,0.88,0.65,0.88,0.83,0.971,0.4,0.391,0.372,0.526,0.6,0.6,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+22.0,0.93,0.76,0.91,0.7,0.525,0.624,0.71,0.843,0.61,0.73,0.86,0.52,0.84,0.91,0.8,0.57,0.562,0.45,0.5,0.45,0.76,0.7,0.807,0.811,0.692,0.764,0.62,0.57,0.82,0.467,0.467,0.467,0.467,0.467,0.467,0.475,0.508,0.82,0.79,0.94,1.0,0.89,0.871,1.0,1.0,0.963,1.0,0.96,1.0,0.634,1.0,1.0,1.0,0.75,0.95,0.88,0.65,0.88,0.83,0.971,0.4,0.391,0.372,0.526,0.6,0.6,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+23.0,0.95,0.78,0.96,0.71,0.525,0.624,0.75,0.843,0.64,0.76,0.92,0.52,0.89,0.95,0.84,0.58,0.562,0.45,0.5,0.45,0.76,0.7,0.807,0.811,0.692,0.764,0.64,0.58,0.82,0.467,0.467,0.467,0.467,0.467,0.467,0.475,0.508,0.87,0.8,0.94,1.0,0.89,0.871,1.0,1.0,0.963,1.0,0.96,1.0,0.634,1.0,1.0,1.0,0.75,0.95,0.88,0.65,0.88,0.83,0.971,0.4,0.391,0.372,0.526,0.6,0.6,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+24.0,0.96,0.8,1.0,0.73,0.525,0.624,0.79,0.843,0.66,0.78,0.98,0.52,0.94,0.98,0.88,0.58,0.562,0.45,0.5,0.45,0.77,0.7,0.807,0.811,0.692,0.764,0.66,0.59,0.82,0.467,0.467,0.467,0.467,0.467,0.467,0.475,0.508,0.92,0.8,0.94,1.0,0.89,0.871,1.0,1.0,0.963,1.0,0.96,1.0,0.634,1.0,1.0,1.0,0.75,0.95,0.88,0.65,0.88,0.83,0.971,0.4,0.391,0.372,0.526,0.6,0.6,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
diff --git a/examples/data/setup_exposure_buildings/test_vulnerability_and_exposure_NSI/vulnerability/vulnerability_identifiers.csv b/examples/data/setup_exposure_buildings/test_vulnerability_and_exposure_NSI/vulnerability/vulnerability_identifiers.csv
new file mode 100644
index 00000000..b33d4845
--- /dev/null
+++ b/examples/data/setup_exposure_buildings/test_vulnerability_and_exposure_NSI/vulnerability/vulnerability_identifiers.csv
@@ -0,0 +1,77 @@
+FIAT Damage Function Name,Exposure Link,Damage Type,ID,Occupancy,Description,Source,full_identifier,Damage function name
+AGR1,AGR1,structure,616,AGR1,"Average Agriculture, structure",USACE - Galveston,"616AGR1AverageAgriculture,structureUSACE-Galveston",AGR1_structure
+COM1,COM1,structure,217,COM1,"Average Retail, Structure",USACE - Galveston,"217COM1AverageRetail,StructureUSACE-Galveston",COM1_structure
+COM10,COM10,structure,543,COM10,"Garage, structure",USACE - Galveston,"543COM10Garage,structureUSACE-Galveston",COM10_structure
+COM2,COM2,structure,341,COM2,"Average wholesale, Structure",USACE - Galveston,"341COM2Averagewholesale,StructureUSACE-Galveston",COM2_structure
+COM3,COM3,structure,32,COM3-S-MostLikely,Average Personal & Repair Services,USACE - IWR,32COM3-S-MostLikelyAveragePersonal&RepairServicesUSACE-IWR,COM3_structure
+COM4,COM4,structure,35,COM4-S-MostLikely,Average Prof/Tech Services,USACE - IWR,35COM4-S-MostLikelyAverageProf/TechServicesUSACE-IWR,COM4_structure
+COM5,COM5,structure,467,COM5,"Bank, Structure",USACE - Galveston,"467COM5Bank,StructureUSACE-Galveston",COM5_structure
+COM6,COM6,structure,38,COM6-S-MostLikely,Hospital,USACE - IWR,38COM6-S-MostLikelyHospitalUSACE-IWR,COM6_structure
+COM7,COM7,structure,475,COM7,"Average Medical Office, Structure",USACE - Galveston,"475COM7AverageMedicalOffice,StructureUSACE-Galveston",COM7_structure
+COM8,COM8,structure,493,COM8,"Average Entertainment/Recreation, Structure",USACE - Galveston,"493COM8AverageEntertainment/Recreation,StructureUSACE-Galveston",COM8_structure
+COM9,COM9,structure,532,COM9,"Average theatre, Structure",USACE - Galveston,"532COM9Averagetheatre,StructureUSACE-Galveston",COM9_structure
+EDU1,EDU1,structure,53,EDU1-S-MostLikely,Average school,USACE - IWR,53EDU1-S-MostLikelyAverageschoolUSACE-IWR,EDU1_structure
+EDU2,EDU2,structure,652,EDU2,"Average college/university, structure",USACE - Galveston,"652EDU2Averagecollege/university,structureUSACE-Galveston",EDU2_structure
+GOV1,GOV1,structure,631,GOV1,"Average government services, structure",USACE - Galveston,"631GOV1Averagegovernmentservices,structureUSACE-Galveston",GOV1_structure
+GOV2,GOV2,structure,640,GOV2,"Average emergency response, Structure",USACE - Galveston,"640GOV2Averageemergencyresponse,StructureUSACE-Galveston",GOV2_structure
+IND1,IND1,structure,545,IND1,"Average heavy industrial, Structure",USACE - Galveston,"545IND1Averageheavyindustrial,StructureUSACE-Galveston",IND1_structure
+IND2,IND2,structure,44,IND2-S-MostLikely,Average light industrial,USACE - IWR,44IND2-S-MostLikelyAveragelightindustrialUSACE-IWR,IND2_structure
+IND3,IND3,structure,575,IND3,"Average Food/Drug/Chem, Structure",USACE - Galveston,"575IND3AverageFood/Drug/Chem,StructureUSACE-Galveston",IND3_structure
+IND4,IND4,structure,586,IND4,"Average Metals/Minerals processing, structure",USACE - Galveston,"586IND4AverageMetals/Mineralsprocessing,structureUSACE-Galveston",IND4_structure
+IND5,IND5,structure,591,IND5,"Average High Technology, structure",USACE - Galveston,"591IND5AverageHighTechnology,structureUSACE-Galveston",IND5_structure
+IND6,IND6,structure,592,IND6,"Average Construction, structure",USACE - Galveston,"592IND6AverageConstruction,structureUSACE-Galveston",IND6_structure
+REL1,REL1,structure,47,REL1-S-MostLikely,Church,USACE - IWR,47REL1-S-MostLikelyChurchUSACE-IWR,REL1_structure
+RES1-1SNB,RES1-1SNB,structure,2,RES1-1SNB-S-MostLikely,"Res 1, 1 Story no Basement",USACE - IWR,"2RES1-1SNB-S-MostLikelyRes1,1StorynoBasementUSACE-IWR",RES1-1SNB_structure
+RES1-1SWB,RES1-1SWB,structure,5,RES1-1SWB-S-MostLikely,"Res 1, 1 Story w/ Basement",USACE - IWR,"5RES1-1SWB-S-MostLikelyRes1,1Storyw/BasementUSACE-IWR",RES1-1SWB_structure
+RES1-2SNB,RES1-2SNB,structure,8,RES1-2SNB-S-MostLikely,"Res 1, 2 Story no Basement",USACE - IWR,"8RES1-2SNB-S-MostLikelyRes1,2StorynoBasementUSACE-IWR",RES1-2SNB_structure
+RES1-2SWB,RES1-2SWB,structure,11,RES1-2SWB-S-MostLikely,"Res 1, 2 Story w/ Basement",USACE - IWR,"11RES1-2SWB-S-MostLikelyRes1,2Storyw/BasementUSACE-IWR",RES1-2SWB_structure
+RES1-3SNB,RES1-3SNB,structure,117,RES1,"three or more floors, no basement, Structure, V-Zone",FIA,"117RES1threeormorefloors,nobasement,Structure,V-ZoneFIA",RES1-3SNB_structure
+RES1-3SWB,RES1-3SWB,structure,127,RES1,"three or more floors, w/ basement, Structure, V-Zone",FIA,"127RES1threeormorefloors,w/basement,Structure,V-ZoneFIA",RES1-3SWB_structure
+RES2,RES2,structure,20,RES2-S-MostLikely,Mobile home,USACE - IWR,20RES2-S-MostLikelyMobilehomeUSACE-IWR,RES2_structure
+RES3A,RES3A,structure,23,RES3-S-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"23RES3-S-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR",RES3A_structure
+RES3B,RES3B,structure,23,RES3-S-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"23RES3-S-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR",RES3B_structure
+RES3C,RES3C,structure,23,RES3-S-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"23RES3-S-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR",RES3C_structure
+RES3D,RES3D,structure,23,RES3-S-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"23RES3-S-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR",RES3D_structure
+RES3E,RES3E,structure,23,RES3-S-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"23RES3-S-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR",RES3E_structure
+RES3F,RES3F,structure,23,RES3-S-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"23RES3-S-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR",RES3F_structure
+RES4,RES4,structure,26,RES4-S-MostLikely,"Average Hotel, & Motel",USACE - IWR,"26RES4-S-MostLikelyAverageHotel,&MotelUSACE-IWR",RES4_structure
+RES5,RES5,structure,50,RES5-PRO-SVC-S-MostLikely,Nursing Home,USACE - IWR,50RES5-PRO-SVC-S-MostLikelyNursingHomeUSACE-IWR,RES5_structure
+RES6,RES6,structure,215,RES6,"Nursing Home, structure",USACE - Galveston,"215RES6NursingHome,structureUSACE-Galveston",RES6_structure
+AGR1,AGR1,content,460,AGR1 ,"Average Agriculture, Contents (Equipment/Inventory)",USACE - Galveston,"460AGR1AverageAgriculture,Contents(Equipment/Inventory)USACE-Galveston",AGR1_content
+COM1,COM1,content,90,COM1 ,Average retail trade contents (Inventory/Equipment),USACE - Galveston,90COM1Averageretailtradecontents(Inventory/Equipment)USACE-Galveston,COM1_content
+COM10,COM10,content,357,COM10,"Garage, Contents (Inventory)",USACE - Galveston,"357COM10Garage,Contents(Inventory)USACE-Galveston",COM10_content
+COM2,COM2,content,195,COM2 ,"Average wholesale trade, Contents",USACE - Galveston,"195COM2Averagewholesaletrade,ContentsUSACE-Galveston",COM2_content
+COM3,COM3,content,92,COM3-C-MostLikely,Average Personal & Repair Services,USACE - IWR,92COM3-C-MostLikelyAveragePersonal&RepairServicesUSACE-IWR,COM3_content
+COM4,COM4,content,95,COM4-C-MostLikely,Average Prof/Tech Services,USACE - IWR,95COM4-C-MostLikelyAverageProf/TechServicesUSACE-IWR,COM4_content
+COM5,COM5,content,304,COM5 ,"Bank, Contents",USACE - Galveston,"304COM5Bank,ContentsUSACE-Galveston",COM5_content
+COM6,COM6,content,98,COM6-C-MostLikely,Hospital,USACE - IWR,98COM6-C-MostLikelyHospitalUSACE-IWR,COM6_content
+COM7,COM7,content,312,COM7 ,"Average Medical Offic/Clinic, Contents (Equipment/Inventory)",USACE - Galveston,"312COM7AverageMedicalOffic/Clinic,Contents(Equipment/Inventory)USACE-Galveston",COM7_content
+COM8,COM8,content,322,COM8 ,"Average Entertainment/Recreation, Contents (Equipment/Inventory)",USACE - Galveston,"322COM8AverageEntertainment/Recreation,Contents(Equipment/Inventory)USACE-Galveston",COM8_content
+COM9,COM9,content,352,COM9 ,"Theater, Contents (Equipment)",USACE - Galveston,"352COM9Theater,Contents(Equipment)USACE-Galveston",COM9_content
+EDU1,EDU1,content,113,EDU1-C-MostLikely,Average school,USACE - IWR,113EDU1-C-MostLikelyAverageschoolUSACE-IWR,EDU1_content
+EDU2,EDU2,content,485,EDU2 ,"Average College/University, Contents (Equipment/Inventory)",USACE - Galveston,"485EDU2AverageCollege/University,Contents(Equipment/Inventory)USACE-Galveston",EDU2_content
+GOV1,GOV1,content,472,GOV1 ,"Average Govt Services, contents (Equipment)",USACE - Galveston,"472GOV1AverageGovtServices,contents(Equipment)USACE-Galveston",GOV1_content
+GOV2,GOV2,content,477,GOV2 ,"Average emergency response, contents (Equipment/Inventory)",USACE - Galveston,"477GOV2Averageemergencyresponse,contents(Equipment/Inventory)USACE-Galveston",GOV2_content
+IND1,IND1,content,358,IND1 ,"Average Heavy Industrial, Contents (Equipment/Inventory)",USACE - Galveston,"358IND1AverageHeavyIndustrial,Contents(Equipment/Inventory)USACE-Galveston",IND1_content
+IND2,IND2,content,104,IND2-C-MostLikely,Average light industrial,USACE - IWR,104IND2-C-MostLikelyAveragelightindustrialUSACE-IWR,IND2_content
+IND3,IND3,content,408,IND3 ,"Average Food/Drugs/Chemicals, Contents (Equipment/Inventory)",USACE - Galveston,"408IND3AverageFood/Drugs/Chemicals,Contents(Equipment/Inventory)USACE-Galveston",IND3_content
+IND4,IND4,content,433,IND4 ,"Average Metals/Minerals Processing, Contents (Equipment/Inventory)",USACE - Galveston,"433IND4AverageMetals/MineralsProcessing,Contents(Equipment/Inventory)USACE-Galveston",IND4_content
+IND5,IND5,content,442,IND5 ,"Average High Technology, contents (Equipment/Inventory)",USACE - Galveston,"442IND5AverageHighTechnology,contents(Equipment/Inventory)USACE-Galveston",IND5_content
+IND6,IND6,content,443,IND6 ,"Average Construction, contents (Equipment/Inventory)",USACE - Galveston,"443IND6AverageConstruction,contents(Equipment/Inventory)USACE-Galveston",IND6_content
+REL1,REL1,content,107,REL1-C-MostLikely,Church,USACE - IWR,107REL1-C-MostLikelyChurchUSACE-IWR,REL1_content
+RES1-1SNB,RES1-1SNB,content,62,RES1-1SNB-C-MostLikely,"Res 1, 1 Story no Basement",USACE - IWR,"62RES1-1SNB-C-MostLikelyRes1,1StorynoBasementUSACE-IWR",RES1-1SNB_content
+RES1-1SWB,RES1-1SWB,content,65,RES1-1SWB-C-MostLikely,"Res 1, 1 Story w/ Basement",USACE - IWR,"65RES1-1SWB-C-MostLikelyRes1,1Storyw/BasementUSACE-IWR",RES1-1SWB_content
+RES1-2SNB,RES1-2SNB,content,68,RES1-2SNB-C-MostLikely,"Res 1, 2 Story no Basement",USACE - IWR,"68RES1-2SNB-C-MostLikelyRes1,2StorynoBasementUSACE-IWR",RES1-2SNB_content
+RES1-2SWB,RES1-2SWB,content,71,RES1-2SWB-C-MostLikely,"Res 1, 2 Story w/ Basement",USACE - IWR,"71RES1-2SWB-C-MostLikelyRes1,2Storyw/BasementUSACE-IWR",RES1-2SWB_content
+RES1-3SNB,RES1-3SNB,content,33,RES1 ,"three or more floors, no basement, Contents, V-Zone",FIA (MOD.),"33RES1threeormorefloors,nobasement,Contents,V-ZoneFIA(MOD.)",RES1-3SNB_content
+RES1-3SWB,RES1-3SWB,content,34,RES1 ,"three or more floors, w/ basement, Contents, V-Zone",FIA (MOD.),"34RES1threeormorefloors,w/basement,Contents,V-ZoneFIA(MOD.)",RES1-3SWB_content
+RES2,RES2,content,80,RES2-C-MostLikely,Mobile home,USACE - IWR,80RES2-C-MostLikelyMobilehomeUSACE-IWR,RES2_content
+RES3A,RES3A,content,83,RES3-C-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"83RES3-C-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR",RES3A_content
+RES3B,RES3B,content,83,RES3-C-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"83RES3-C-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR",RES3B_content
+RES3C,RES3C,content,83,RES3-C-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"83RES3-C-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR",RES3C_content
+RES3D,RES3D,content,83,RES3-C-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"83RES3-C-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR",RES3D_content
+RES3E,RES3E,content,83,RES3-C-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"83RES3-C-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR",RES3E_content
+RES3F,RES3F,content,83,RES3-C-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"83RES3-C-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR",RES3F_content
+RES4,RES4,content,86,RES4-C-MostLikely,"Average Hotel, & Motel",USACE - IWR,"86RES4-C-MostLikelyAverageHotel,&MotelUSACE-IWR",RES4_content
+RES5,RES5,content,110,RES5-PRO-SVC-C-MostLikely,Nursing Home,USACE - IWR,110RES5-PRO-SVC-C-MostLikelyNursingHomeUSACE-IWR,RES5_content
+RES6,RES6,content,89,RES6 ,"Nursing Home, contents (Equipment)",USACE - Galveston,"89RES6NursingHome,contents(Equipment)USACE-Galveston",RES6_content
diff --git a/examples/data/setup_exposure_buildings_with_dem_data/test_vulnerability_and_exposure_NSI/exposure/buildings.gpkg b/examples/data/setup_exposure_buildings_with_dem_data/test_vulnerability_and_exposure_NSI/exposure/buildings.gpkg
new file mode 100644
index 00000000..cceee861
Binary files /dev/null and b/examples/data/setup_exposure_buildings_with_dem_data/test_vulnerability_and_exposure_NSI/exposure/buildings.gpkg differ
diff --git a/examples/data/setup_exposure_buildings_with_dem_data/test_vulnerability_and_exposure_NSI/exposure/exposure.csv b/examples/data/setup_exposure_buildings_with_dem_data/test_vulnerability_and_exposure_NSI/exposure/exposure.csv
new file mode 100644
index 00000000..9d493c9e
--- /dev/null
+++ b/examples/data/setup_exposure_buildings_with_dem_data/test_vulnerability_and_exposure_NSI/exposure/exposure.csv
@@ -0,0 +1,1995 @@
+Object ID,Object Name,Primary Object Type,Secondary Object Type,Max Potential Damage: Structure,Max Potential Damage: Content,Ground Floor Height,Ground Elevation,Aggregation Label: Census Block,Extraction Method,Damage Function: Structure,Damage Function: Content
+574486973,574486973,RES,RES3A,480189.335,240094.667,0.5,7.298348064632416,450190001001006,centroid,RES3A_structure,RES3A_content
+573783417,573783417,COM,RES4,1177614.2,588807.1,8.0,8.381748561544418,450190002002010,centroid,RES4_structure,RES4_content
+573783433,573783433,COM,RES4,476216.254,238108.127,0.5,7.374183635444641,450190002002010,centroid,RES4_structure,RES4_content
+573785665,573785665,RES,RES1-2SNB,330007.42,165003.71,0.5,7.364031290178299,450190002002004,centroid,RES1-2SNB_structure,RES1-2SNB_content
+574346729,574346729,RES,RES3A,472285.148,236142.574,0.5,10.902030923233033,450190002001030,centroid,RES3A_structure,RES3A_content
+574372707,574372707,RES,RES1-3SNB,389363.808,194681.904,0.5,6.365223632082939,450190002002008,centroid,RES1-3SNB_structure,RES1-3SNB_content
+574372721,574372721,RES,RES3A,503924.421,251962.21,0.5,7.1200097102165225,450190002002008,centroid,RES3A_structure,RES3A_content
+574476728,574476728,RES,RES1-1SNB,96964.414,48482.207,0.5,5.8671571920728685,450190002002018,centroid,RES1-1SNB_structure,RES1-1SNB_content
+574476811,574476811,RES,RES1-1SNB,152049.256,76024.6281,0.5,5.8671571920728685,450190002002018,centroid,RES1-1SNB_structure,RES1-1SNB_content
+574486899,574486899,RES,RES3A,480189.335,240094.667,0.5,12.333664919576645,450190002001030,centroid,RES3A_structure,RES3A_content
+574486932,574486932,RES,RES1-1SNB,177705.21,88852.6051,0.5,6.46012056552887,450190002002005,centroid,RES1-1SNB_structure,RES1-1SNB_content
+573827729,573827729,COM,COM8,37755.4885,37755.4885,0.5,11.172797937307358,450190002001028,centroid,COM8_structure,COM8_content
+574421669,574421669,RES,RES3A,170840.757,85420.3788,8.0,11.520179606552125,450190002001029,centroid,RES3A_structure,RES3A_content
+574488781,574488781,RES,RES1-3SNB,374871.082,187435.541,0.5,5.9989820201063155,450190002001009,centroid,RES1-3SNB_structure,RES1-3SNB_content
+573467745,573467745,RES,RES3A,516183.793,258091.896,2.0,5.893531857123375,450190002002011,centroid,RES3A_structure,RES3A_content
+573779323,573779323,RES,RES1-2SWB,437207.93,218603.965,2.0,6.975060908927918,450190002002000,centroid,RES1-2SWB_structure,RES1-2SWB_content
+573779375,573779375,RES,RES1-1SNB,226202.175,113101.087,0.5,12.121235366783141,450190002002000,centroid,RES1-1SNB_structure,RES1-1SNB_content
+573779393,573779393,RES,RES3A,200255.163,100127.581,0.5,12.167719169063568,450190002002000,centroid,RES3A_structure,RES3A_content
+573779405,573779405,RES,RES1-3SNB,398598.966,199299.483,0.5,10.82060956788063,450190002002000,centroid,RES1-3SNB_structure,RES1-3SNB_content
+573782594,573782594,RES,RES3A,489787.119,244893.559,0.5,6.500304422607422,450190002002005,centroid,RES3A_structure,RES3A_content
+573782596,573782596,RES,RES3A,675402.418,337701.209,0.5,6.887255911560058,450190002002005,centroid,RES3A_structure,RES3A_content
+573782413,573782413,RES,RES3A,437002.054,218501.027,0.5,7.119138324699402,450190002002006,centroid,RES3A_structure,RES3A_content
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+574488834,574488834,RES,RES1-3SNB,384398.576,192199.288,0.5,12.906374055252074,450190002002001,centroid,RES1-3SNB_structure,RES1-3SNB_content
+574488837,574488837,RES,RES3A,480189.335,240094.667,0.5,13.259127182617187,450190002001032,centroid,RES3A_structure,RES3A_content
+574488848,574488848,RES,RES1-2SNB,322415.251,161207.625,0.5,13.14323290884018,450190002002000,centroid,RES1-2SNB_structure,RES1-2SNB_content
+574488851,574488851,RES,RES1-2SNB,222897.785,111448.892,0.5,10.150553998088837,450190002001010,centroid,RES1-2SNB_structure,RES1-2SNB_content
+574488874,574488874,RES,RES3A,480189.335,240094.667,0.5,8.90064142829895,450190002002011,centroid,RES3A_structure,RES3A_content
diff --git a/examples/data/setup_exposure_buildings_with_dem_data/test_vulnerability_and_exposure_NSI/exposure/region.gpkg b/examples/data/setup_exposure_buildings_with_dem_data/test_vulnerability_and_exposure_NSI/exposure/region.gpkg
new file mode 100644
index 00000000..e966481c
Binary files /dev/null and b/examples/data/setup_exposure_buildings_with_dem_data/test_vulnerability_and_exposure_NSI/exposure/region.gpkg differ
diff --git a/examples/data/setup_exposure_buildings_with_dem_data/test_vulnerability_and_exposure_NSI/settings.toml b/examples/data/setup_exposure_buildings_with_dem_data/test_vulnerability_and_exposure_NSI/settings.toml
new file mode 100644
index 00000000..3ae7d2ea
--- /dev/null
+++ b/examples/data/setup_exposure_buildings_with_dem_data/test_vulnerability_and_exposure_NSI/settings.toml
@@ -0,0 +1,24 @@
+[global]
+crs = "epsg:4326"
+
+[output]
+path = "output"
+
+[output.csv]
+name = "output.csv"
+
+[output.geom]
+name1 = "spatial.gpkg"
+
+[vulnerability]
+file = "./vulnerability/vulnerability_curves.csv"
+unit = "feet"
+step_size = 0.1
+
+[exposure.csv]
+file = "./exposure/exposure.csv"
+
+[exposure.geom]
+crs = "EPSG:4326"
+unit = "ft"
+file1 = "./exposure/buildings.gpkg"
diff --git a/examples/data/setup_exposure_buildings_with_dem_data/test_vulnerability_and_exposure_NSI/vulnerability/vulnerability_curves.csv b/examples/data/setup_exposure_buildings_with_dem_data/test_vulnerability_and_exposure_NSI/vulnerability/vulnerability_curves.csv
new file mode 100644
index 00000000..b7baee0f
--- /dev/null
+++ b/examples/data/setup_exposure_buildings_with_dem_data/test_vulnerability_and_exposure_NSI/vulnerability/vulnerability_curves.csv
@@ -0,0 +1,37 @@
+#UNIT=feet
+#METHOD,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean
+water depth [feet],AGR1_structure,COM1_structure,COM10_structure,COM2_structure,COM3_structure,COM4_structure,COM5_structure,COM6_structure,COM7_structure,COM8_structure,COM9_structure,EDU1_structure,EDU2_structure,GOV1_structure,GOV2_structure,IND1_structure,IND2_structure,IND3_structure,IND4_structure,IND5_structure,IND6_structure,REL1_structure,RES1-1SNB_structure,RES1-1SWB_structure,RES1-2SNB_structure,RES1-2SWB_structure,RES1-3SNB_structure,RES1-3SWB_structure,RES2_structure,RES3A_structure,RES3B_structure,RES3C_structure,RES3D_structure,RES3E_structure,RES3F_structure,RES4_structure,RES5_structure,RES6_structure,AGR1_content,COM1_content,COM10_content,COM2_content,COM3_content,COM4_content,COM5_content,COM6_content,COM7_content,COM8_content,COM9_content,EDU1_content,EDU2_content,GOV1_content,GOV2_content,IND1_content,IND2_content,IND3_content,IND4_content,IND5_content,IND6_content,REL1_content,RES1-1SNB_content,RES1-1SWB_content,RES1-2SNB_content,RES1-2SWB_content,RES1-3SNB_content,RES1-3SWB_content,RES2_content,RES3A_content,RES3B_content,RES3C_content,RES3D_content,RES3E_content,RES3F_content,RES4_content,RES5_content,RES6_content
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+9.0,0.46,0.34,0.21,0.29,0.5,0.624,0.24,0.843,0.22,0.26,0.12,0.52,0.17,0.22,0.27,0.39,0.562,0.34,0.42,0.34,0.61,0.7,0.705,0.777,0.524,0.608,0.35,0.42,0.82,0.467,0.467,0.467,0.467,0.467,0.467,0.463,0.488,0.23,0.75,0.88,0.51,0.76,0.871,1.0,1.0,0.956,0.77,0.85,0.3,0.606,0.79,1.0,1.0,0.63,0.943,0.78,0.56,0.78,0.72,0.971,0.372,0.377,0.303,0.317,0.41,0.44,0.83,0.606,0.606,0.606,0.606,0.606,0.606,0.631,0.9,1.0
+10.0,0.51,0.38,0.25,0.32,0.525,0.624,0.28,0.843,0.24,0.29,0.15,0.52,0.2,0.26,0.31,0.42,0.562,0.36,0.45,0.36,0.63,0.7,0.732,0.801,0.557,0.648,0.38,0.45,0.82,0.467,0.467,0.467,0.467,0.467,0.467,0.475,0.508,0.26,0.76,0.89,0.63,0.8,0.871,1.0,1.0,0.963,0.81,0.87,0.41,0.634,0.83,1.0,1.0,0.64,0.95,0.79,0.57,0.79,0.78,0.971,0.384,0.386,0.32,0.344,0.43,0.46,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+11.0,0.57,0.42,0.3,0.37,0.525,0.624,0.31,0.843,0.27,0.33,0.2,0.52,0.24,0.31,0.35,0.48,0.562,0.39,0.47,0.39,0.64,0.7,0.754,0.811,0.587,0.684,0.4,0.46,0.82,0.467,0.467,0.467,0.467,0.467,0.467,0.475,0.508,0.3,0.76,0.9,0.77,0.82,0.871,1.0,1.0,0.963,0.86,0.89,0.57,0.634,0.88,1.0,1.0,0.66,0.95,0.82,0.6,0.82,0.79,0.971,0.392,0.391,0.334,0.372,0.45,0.47,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+12.0,0.63,0.47,0.35,0.41,0.525,0.624,0.34,0.843,0.3,0.37,0.24,0.52,0.28,0.37,0.4,0.5,0.562,0.4,0.49,0.4,0.65,0.7,0.772,0.811,0.614,0.714,0.42,0.47,0.82,0.467,0.467,0.467,0.467,0.467,0.467,0.475,0.508,0.34,0.76,0.91,0.93,0.84,0.871,1.0,1.0,0.963,0.92,0.9,0.66,0.634,0.94,1.0,1.0,0.68,0.95,0.83,0.62,0.83,0.8,0.971,0.397,0.391,0.347,0.4,0.48,0.5,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+13.0,0.7,0.51,0.41,0.45,0.525,0.624,0.37,0.843,0.34,0.41,0.29,0.52,0.33,0.44,0.44,0.51,0.562,0.42,0.5,0.42,0.67,0.7,0.785,0.811,0.638,0.737,0.44,0.48,0.82,0.467,0.467,0.467,0.467,0.467,0.467,0.475,0.508,0.38,0.77,0.92,1.0,0.86,0.871,1.0,1.0,0.963,0.94,0.91,0.73,0.634,1.0,1.0,1.0,0.69,0.95,0.84,0.63,0.84,0.8,0.971,0.4,0.391,0.356,0.43,0.5,0.52,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+14.0,0.75,0.55,0.47,0.49,0.525,0.624,0.4,0.843,0.37,0.45,0.35,0.52,0.39,0.51,0.48,0.53,0.562,0.42,0.5,0.42,0.68,0.7,0.795,0.811,0.659,0.754,0.46,0.49,0.82,0.467,0.467,0.467,0.467,0.467,0.467,0.475,0.508,0.42,0.77,0.92,1.0,0.87,0.871,1.0,1.0,0.963,0.97,0.92,0.79,0.634,1.0,1.0,1.0,0.72,0.95,0.86,0.63,0.86,0.81,0.971,0.4,0.391,0.364,0.461,0.52,0.53,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+15.0,0.79,0.58,0.52,0.52,0.525,0.624,0.44,0.843,0.41,0.5,0.42,0.52,0.45,0.59,0.52,0.54,0.562,0.43,0.5,0.43,0.69,0.7,0.802,0.811,0.677,0.764,0.48,0.5,0.82,0.467,0.467,0.467,0.467,0.467,0.467,0.475,0.508,0.47,0.77,0.92,1.0,0.87,0.871,1.0,1.0,0.963,0.99,0.93,0.84,0.634,1.0,1.0,1.0,0.73,0.95,0.87,0.63,0.87,0.81,0.971,0.4,0.391,0.369,0.493,0.54,0.55,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+16.0,0.82,0.61,0.58,0.55,0.525,0.624,0.48,0.843,0.44,0.53,0.49,0.52,0.52,0.65,0.56,0.55,0.562,0.43,0.5,0.43,0.7,0.7,0.807,0.811,0.692,0.764,0.5,0.51,0.82,0.467,0.467,0.467,0.467,0.467,0.467,0.475,0.508,0.52,0.78,0.93,1.0,0.88,0.871,1.0,1.0,0.963,1.0,0.94,0.9,0.634,1.0,1.0,1.0,0.73,0.95,0.87,0.64,0.87,0.81,0.971,0.4,0.391,0.372,0.526,0.56,0.57,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+17.0,0.84,0.64,0.65,0.58,0.525,0.624,0.51,0.843,0.48,0.57,0.56,0.52,0.59,0.7,0.6,0.55,0.562,0.44,0.5,0.44,0.71,0.7,0.807,0.811,0.692,0.764,0.52,0.52,0.82,0.467,0.467,0.467,0.467,0.467,0.467,0.475,0.508,0.57,0.78,0.93,1.0,0.89,0.871,1.0,1.0,0.963,1.0,0.95,0.97,0.634,1.0,1.0,1.0,0.73,0.95,0.88,0.65,0.88,0.82,0.971,0.4,0.391,0.372,0.526,0.58,0.58,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+18.0,0.87,0.67,0.71,0.61,0.525,0.624,0.55,0.843,0.51,0.6,0.62,0.52,0.64,0.74,0.64,0.56,0.562,0.44,0.5,0.44,0.72,0.7,0.807,0.811,0.692,0.764,0.54,0.53,0.82,0.467,0.467,0.467,0.467,0.467,0.467,0.475,0.508,0.62,0.78,0.94,1.0,0.89,0.871,1.0,1.0,0.963,1.0,0.96,0.98,0.634,1.0,1.0,1.0,0.74,0.95,0.88,0.65,0.88,0.82,0.971,0.4,0.391,0.372,0.526,0.6,0.6,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+19.0,0.89,0.69,0.76,0.63,0.525,0.624,0.59,0.843,0.54,0.63,0.68,0.52,0.69,0.79,0.68,0.56,0.562,0.44,0.5,0.44,0.73,0.7,0.807,0.811,0.692,0.764,0.56,0.54,0.82,0.467,0.467,0.467,0.467,0.467,0.467,0.475,0.508,0.67,0.79,0.94,1.0,0.89,0.871,1.0,1.0,0.963,1.0,0.96,1.0,0.634,1.0,1.0,1.0,0.74,0.95,0.88,0.65,0.88,0.82,0.971,0.4,0.391,0.372,0.526,0.6,0.6,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+20.0,0.9,0.71,0.81,0.66,0.525,0.624,0.63,0.843,0.56,0.66,0.74,0.52,0.74,0.83,0.72,0.57,0.562,0.44,0.5,0.44,0.74,0.7,0.807,0.811,0.692,0.764,0.58,0.55,0.82,0.467,0.467,0.467,0.467,0.467,0.467,0.475,0.508,0.72,0.79,0.94,1.0,0.89,0.871,1.0,1.0,0.963,1.0,0.96,1.0,0.634,1.0,1.0,1.0,0.74,0.95,0.88,0.65,0.88,0.83,0.971,0.4,0.391,0.372,0.526,0.6,0.6,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+21.0,0.92,0.74,0.86,0.68,0.525,0.624,0.67,0.843,0.59,0.69,0.8,0.52,0.79,0.87,0.76,0.57,0.562,0.44,0.5,0.44,0.75,0.7,0.807,0.811,0.692,0.764,0.6,0.56,0.82,0.467,0.467,0.467,0.467,0.467,0.467,0.475,0.508,0.77,0.79,0.94,1.0,0.89,0.871,1.0,1.0,0.963,1.0,0.96,1.0,0.634,1.0,1.0,1.0,0.74,0.95,0.88,0.65,0.88,0.83,0.971,0.4,0.391,0.372,0.526,0.6,0.6,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+22.0,0.93,0.76,0.91,0.7,0.525,0.624,0.71,0.843,0.61,0.73,0.86,0.52,0.84,0.91,0.8,0.57,0.562,0.45,0.5,0.45,0.76,0.7,0.807,0.811,0.692,0.764,0.62,0.57,0.82,0.467,0.467,0.467,0.467,0.467,0.467,0.475,0.508,0.82,0.79,0.94,1.0,0.89,0.871,1.0,1.0,0.963,1.0,0.96,1.0,0.634,1.0,1.0,1.0,0.75,0.95,0.88,0.65,0.88,0.83,0.971,0.4,0.391,0.372,0.526,0.6,0.6,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+23.0,0.95,0.78,0.96,0.71,0.525,0.624,0.75,0.843,0.64,0.76,0.92,0.52,0.89,0.95,0.84,0.58,0.562,0.45,0.5,0.45,0.76,0.7,0.807,0.811,0.692,0.764,0.64,0.58,0.82,0.467,0.467,0.467,0.467,0.467,0.467,0.475,0.508,0.87,0.8,0.94,1.0,0.89,0.871,1.0,1.0,0.963,1.0,0.96,1.0,0.634,1.0,1.0,1.0,0.75,0.95,0.88,0.65,0.88,0.83,0.971,0.4,0.391,0.372,0.526,0.6,0.6,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+24.0,0.96,0.8,1.0,0.73,0.525,0.624,0.79,0.843,0.66,0.78,0.98,0.52,0.94,0.98,0.88,0.58,0.562,0.45,0.5,0.45,0.77,0.7,0.807,0.811,0.692,0.764,0.66,0.59,0.82,0.467,0.467,0.467,0.467,0.467,0.467,0.475,0.508,0.92,0.8,0.94,1.0,0.89,0.871,1.0,1.0,0.963,1.0,0.96,1.0,0.634,1.0,1.0,1.0,0.75,0.95,0.88,0.65,0.88,0.83,0.971,0.4,0.391,0.372,0.526,0.6,0.6,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
diff --git a/examples/data/setup_exposure_buildings_with_dem_data/test_vulnerability_and_exposure_NSI/vulnerability/vulnerability_identifiers.csv b/examples/data/setup_exposure_buildings_with_dem_data/test_vulnerability_and_exposure_NSI/vulnerability/vulnerability_identifiers.csv
new file mode 100644
index 00000000..b33d4845
--- /dev/null
+++ b/examples/data/setup_exposure_buildings_with_dem_data/test_vulnerability_and_exposure_NSI/vulnerability/vulnerability_identifiers.csv
@@ -0,0 +1,77 @@
+FIAT Damage Function Name,Exposure Link,Damage Type,ID,Occupancy,Description,Source,full_identifier,Damage function name
+AGR1,AGR1,structure,616,AGR1,"Average Agriculture, structure",USACE - Galveston,"616AGR1AverageAgriculture,structureUSACE-Galveston",AGR1_structure
+COM1,COM1,structure,217,COM1,"Average Retail, Structure",USACE - Galveston,"217COM1AverageRetail,StructureUSACE-Galveston",COM1_structure
+COM10,COM10,structure,543,COM10,"Garage, structure",USACE - Galveston,"543COM10Garage,structureUSACE-Galveston",COM10_structure
+COM2,COM2,structure,341,COM2,"Average wholesale, Structure",USACE - Galveston,"341COM2Averagewholesale,StructureUSACE-Galveston",COM2_structure
+COM3,COM3,structure,32,COM3-S-MostLikely,Average Personal & Repair Services,USACE - IWR,32COM3-S-MostLikelyAveragePersonal&RepairServicesUSACE-IWR,COM3_structure
+COM4,COM4,structure,35,COM4-S-MostLikely,Average Prof/Tech Services,USACE - IWR,35COM4-S-MostLikelyAverageProf/TechServicesUSACE-IWR,COM4_structure
+COM5,COM5,structure,467,COM5,"Bank, Structure",USACE - Galveston,"467COM5Bank,StructureUSACE-Galveston",COM5_structure
+COM6,COM6,structure,38,COM6-S-MostLikely,Hospital,USACE - IWR,38COM6-S-MostLikelyHospitalUSACE-IWR,COM6_structure
+COM7,COM7,structure,475,COM7,"Average Medical Office, Structure",USACE - Galveston,"475COM7AverageMedicalOffice,StructureUSACE-Galveston",COM7_structure
+COM8,COM8,structure,493,COM8,"Average Entertainment/Recreation, Structure",USACE - Galveston,"493COM8AverageEntertainment/Recreation,StructureUSACE-Galveston",COM8_structure
+COM9,COM9,structure,532,COM9,"Average theatre, Structure",USACE - Galveston,"532COM9Averagetheatre,StructureUSACE-Galveston",COM9_structure
+EDU1,EDU1,structure,53,EDU1-S-MostLikely,Average school,USACE - IWR,53EDU1-S-MostLikelyAverageschoolUSACE-IWR,EDU1_structure
+EDU2,EDU2,structure,652,EDU2,"Average college/university, structure",USACE - Galveston,"652EDU2Averagecollege/university,structureUSACE-Galveston",EDU2_structure
+GOV1,GOV1,structure,631,GOV1,"Average government services, structure",USACE - Galveston,"631GOV1Averagegovernmentservices,structureUSACE-Galveston",GOV1_structure
+GOV2,GOV2,structure,640,GOV2,"Average emergency response, Structure",USACE - Galveston,"640GOV2Averageemergencyresponse,StructureUSACE-Galveston",GOV2_structure
+IND1,IND1,structure,545,IND1,"Average heavy industrial, Structure",USACE - Galveston,"545IND1Averageheavyindustrial,StructureUSACE-Galveston",IND1_structure
+IND2,IND2,structure,44,IND2-S-MostLikely,Average light industrial,USACE - IWR,44IND2-S-MostLikelyAveragelightindustrialUSACE-IWR,IND2_structure
+IND3,IND3,structure,575,IND3,"Average Food/Drug/Chem, Structure",USACE - Galveston,"575IND3AverageFood/Drug/Chem,StructureUSACE-Galveston",IND3_structure
+IND4,IND4,structure,586,IND4,"Average Metals/Minerals processing, structure",USACE - Galveston,"586IND4AverageMetals/Mineralsprocessing,structureUSACE-Galveston",IND4_structure
+IND5,IND5,structure,591,IND5,"Average High Technology, structure",USACE - Galveston,"591IND5AverageHighTechnology,structureUSACE-Galveston",IND5_structure
+IND6,IND6,structure,592,IND6,"Average Construction, structure",USACE - Galveston,"592IND6AverageConstruction,structureUSACE-Galveston",IND6_structure
+REL1,REL1,structure,47,REL1-S-MostLikely,Church,USACE - IWR,47REL1-S-MostLikelyChurchUSACE-IWR,REL1_structure
+RES1-1SNB,RES1-1SNB,structure,2,RES1-1SNB-S-MostLikely,"Res 1, 1 Story no Basement",USACE - IWR,"2RES1-1SNB-S-MostLikelyRes1,1StorynoBasementUSACE-IWR",RES1-1SNB_structure
+RES1-1SWB,RES1-1SWB,structure,5,RES1-1SWB-S-MostLikely,"Res 1, 1 Story w/ Basement",USACE - IWR,"5RES1-1SWB-S-MostLikelyRes1,1Storyw/BasementUSACE-IWR",RES1-1SWB_structure
+RES1-2SNB,RES1-2SNB,structure,8,RES1-2SNB-S-MostLikely,"Res 1, 2 Story no Basement",USACE - IWR,"8RES1-2SNB-S-MostLikelyRes1,2StorynoBasementUSACE-IWR",RES1-2SNB_structure
+RES1-2SWB,RES1-2SWB,structure,11,RES1-2SWB-S-MostLikely,"Res 1, 2 Story w/ Basement",USACE - IWR,"11RES1-2SWB-S-MostLikelyRes1,2Storyw/BasementUSACE-IWR",RES1-2SWB_structure
+RES1-3SNB,RES1-3SNB,structure,117,RES1,"three or more floors, no basement, Structure, V-Zone",FIA,"117RES1threeormorefloors,nobasement,Structure,V-ZoneFIA",RES1-3SNB_structure
+RES1-3SWB,RES1-3SWB,structure,127,RES1,"three or more floors, w/ basement, Structure, V-Zone",FIA,"127RES1threeormorefloors,w/basement,Structure,V-ZoneFIA",RES1-3SWB_structure
+RES2,RES2,structure,20,RES2-S-MostLikely,Mobile home,USACE - IWR,20RES2-S-MostLikelyMobilehomeUSACE-IWR,RES2_structure
+RES3A,RES3A,structure,23,RES3-S-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"23RES3-S-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR",RES3A_structure
+RES3B,RES3B,structure,23,RES3-S-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"23RES3-S-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR",RES3B_structure
+RES3C,RES3C,structure,23,RES3-S-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"23RES3-S-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR",RES3C_structure
+RES3D,RES3D,structure,23,RES3-S-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"23RES3-S-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR",RES3D_structure
+RES3E,RES3E,structure,23,RES3-S-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"23RES3-S-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR",RES3E_structure
+RES3F,RES3F,structure,23,RES3-S-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"23RES3-S-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR",RES3F_structure
+RES4,RES4,structure,26,RES4-S-MostLikely,"Average Hotel, & Motel",USACE - IWR,"26RES4-S-MostLikelyAverageHotel,&MotelUSACE-IWR",RES4_structure
+RES5,RES5,structure,50,RES5-PRO-SVC-S-MostLikely,Nursing Home,USACE - IWR,50RES5-PRO-SVC-S-MostLikelyNursingHomeUSACE-IWR,RES5_structure
+RES6,RES6,structure,215,RES6,"Nursing Home, structure",USACE - Galveston,"215RES6NursingHome,structureUSACE-Galveston",RES6_structure
+AGR1,AGR1,content,460,AGR1 ,"Average Agriculture, Contents (Equipment/Inventory)",USACE - Galveston,"460AGR1AverageAgriculture,Contents(Equipment/Inventory)USACE-Galveston",AGR1_content
+COM1,COM1,content,90,COM1 ,Average retail trade contents (Inventory/Equipment),USACE - Galveston,90COM1Averageretailtradecontents(Inventory/Equipment)USACE-Galveston,COM1_content
+COM10,COM10,content,357,COM10,"Garage, Contents (Inventory)",USACE - Galveston,"357COM10Garage,Contents(Inventory)USACE-Galveston",COM10_content
+COM2,COM2,content,195,COM2 ,"Average wholesale trade, Contents",USACE - Galveston,"195COM2Averagewholesaletrade,ContentsUSACE-Galveston",COM2_content
+COM3,COM3,content,92,COM3-C-MostLikely,Average Personal & Repair Services,USACE - IWR,92COM3-C-MostLikelyAveragePersonal&RepairServicesUSACE-IWR,COM3_content
+COM4,COM4,content,95,COM4-C-MostLikely,Average Prof/Tech Services,USACE - IWR,95COM4-C-MostLikelyAverageProf/TechServicesUSACE-IWR,COM4_content
+COM5,COM5,content,304,COM5 ,"Bank, Contents",USACE - Galveston,"304COM5Bank,ContentsUSACE-Galveston",COM5_content
+COM6,COM6,content,98,COM6-C-MostLikely,Hospital,USACE - IWR,98COM6-C-MostLikelyHospitalUSACE-IWR,COM6_content
+COM7,COM7,content,312,COM7 ,"Average Medical Offic/Clinic, Contents (Equipment/Inventory)",USACE - Galveston,"312COM7AverageMedicalOffic/Clinic,Contents(Equipment/Inventory)USACE-Galveston",COM7_content
+COM8,COM8,content,322,COM8 ,"Average Entertainment/Recreation, Contents (Equipment/Inventory)",USACE - Galveston,"322COM8AverageEntertainment/Recreation,Contents(Equipment/Inventory)USACE-Galveston",COM8_content
+COM9,COM9,content,352,COM9 ,"Theater, Contents (Equipment)",USACE - Galveston,"352COM9Theater,Contents(Equipment)USACE-Galveston",COM9_content
+EDU1,EDU1,content,113,EDU1-C-MostLikely,Average school,USACE - IWR,113EDU1-C-MostLikelyAverageschoolUSACE-IWR,EDU1_content
+EDU2,EDU2,content,485,EDU2 ,"Average College/University, Contents (Equipment/Inventory)",USACE - Galveston,"485EDU2AverageCollege/University,Contents(Equipment/Inventory)USACE-Galveston",EDU2_content
+GOV1,GOV1,content,472,GOV1 ,"Average Govt Services, contents (Equipment)",USACE - Galveston,"472GOV1AverageGovtServices,contents(Equipment)USACE-Galveston",GOV1_content
+GOV2,GOV2,content,477,GOV2 ,"Average emergency response, contents (Equipment/Inventory)",USACE - Galveston,"477GOV2Averageemergencyresponse,contents(Equipment/Inventory)USACE-Galveston",GOV2_content
+IND1,IND1,content,358,IND1 ,"Average Heavy Industrial, Contents (Equipment/Inventory)",USACE - Galveston,"358IND1AverageHeavyIndustrial,Contents(Equipment/Inventory)USACE-Galveston",IND1_content
+IND2,IND2,content,104,IND2-C-MostLikely,Average light industrial,USACE - IWR,104IND2-C-MostLikelyAveragelightindustrialUSACE-IWR,IND2_content
+IND3,IND3,content,408,IND3 ,"Average Food/Drugs/Chemicals, Contents (Equipment/Inventory)",USACE - Galveston,"408IND3AverageFood/Drugs/Chemicals,Contents(Equipment/Inventory)USACE-Galveston",IND3_content
+IND4,IND4,content,433,IND4 ,"Average Metals/Minerals Processing, Contents (Equipment/Inventory)",USACE - Galveston,"433IND4AverageMetals/MineralsProcessing,Contents(Equipment/Inventory)USACE-Galveston",IND4_content
+IND5,IND5,content,442,IND5 ,"Average High Technology, contents (Equipment/Inventory)",USACE - Galveston,"442IND5AverageHighTechnology,contents(Equipment/Inventory)USACE-Galveston",IND5_content
+IND6,IND6,content,443,IND6 ,"Average Construction, contents (Equipment/Inventory)",USACE - Galveston,"443IND6AverageConstruction,contents(Equipment/Inventory)USACE-Galveston",IND6_content
+REL1,REL1,content,107,REL1-C-MostLikely,Church,USACE - IWR,107REL1-C-MostLikelyChurchUSACE-IWR,REL1_content
+RES1-1SNB,RES1-1SNB,content,62,RES1-1SNB-C-MostLikely,"Res 1, 1 Story no Basement",USACE - IWR,"62RES1-1SNB-C-MostLikelyRes1,1StorynoBasementUSACE-IWR",RES1-1SNB_content
+RES1-1SWB,RES1-1SWB,content,65,RES1-1SWB-C-MostLikely,"Res 1, 1 Story w/ Basement",USACE - IWR,"65RES1-1SWB-C-MostLikelyRes1,1Storyw/BasementUSACE-IWR",RES1-1SWB_content
+RES1-2SNB,RES1-2SNB,content,68,RES1-2SNB-C-MostLikely,"Res 1, 2 Story no Basement",USACE - IWR,"68RES1-2SNB-C-MostLikelyRes1,2StorynoBasementUSACE-IWR",RES1-2SNB_content
+RES1-2SWB,RES1-2SWB,content,71,RES1-2SWB-C-MostLikely,"Res 1, 2 Story w/ Basement",USACE - IWR,"71RES1-2SWB-C-MostLikelyRes1,2Storyw/BasementUSACE-IWR",RES1-2SWB_content
+RES1-3SNB,RES1-3SNB,content,33,RES1 ,"three or more floors, no basement, Contents, V-Zone",FIA (MOD.),"33RES1threeormorefloors,nobasement,Contents,V-ZoneFIA(MOD.)",RES1-3SNB_content
+RES1-3SWB,RES1-3SWB,content,34,RES1 ,"three or more floors, w/ basement, Contents, V-Zone",FIA (MOD.),"34RES1threeormorefloors,w/basement,Contents,V-ZoneFIA(MOD.)",RES1-3SWB_content
+RES2,RES2,content,80,RES2-C-MostLikely,Mobile home,USACE - IWR,80RES2-C-MostLikelyMobilehomeUSACE-IWR,RES2_content
+RES3A,RES3A,content,83,RES3-C-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"83RES3-C-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR",RES3A_content
+RES3B,RES3B,content,83,RES3-C-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"83RES3-C-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR",RES3B_content
+RES3C,RES3C,content,83,RES3-C-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"83RES3-C-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR",RES3C_content
+RES3D,RES3D,content,83,RES3-C-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"83RES3-C-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR",RES3D_content
+RES3E,RES3E,content,83,RES3-C-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"83RES3-C-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR",RES3E_content
+RES3F,RES3F,content,83,RES3-C-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"83RES3-C-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR",RES3F_content
+RES4,RES4,content,86,RES4-C-MostLikely,"Average Hotel, & Motel",USACE - IWR,"86RES4-C-MostLikelyAverageHotel,&MotelUSACE-IWR",RES4_content
+RES5,RES5,content,110,RES5-PRO-SVC-C-MostLikely,Nursing Home,USACE - IWR,110RES5-PRO-SVC-C-MostLikelyNursingHomeUSACE-IWR,RES5_content
+RES6,RES6,content,89,RES6 ,"Nursing Home, contents (Equipment)",USACE - Galveston,"89RES6NursingHome,contents(Equipment)USACE-Galveston",RES6_content
diff --git a/examples/data/setup_exposure_buildings_with_dem_data/test_vulnerability_and_exposure_NSI_with_dem_data/exposure/buildings.gpkg b/examples/data/setup_exposure_buildings_with_dem_data/test_vulnerability_and_exposure_NSI_with_dem_data/exposure/buildings.gpkg
new file mode 100644
index 00000000..3739ccbf
Binary files /dev/null and b/examples/data/setup_exposure_buildings_with_dem_data/test_vulnerability_and_exposure_NSI_with_dem_data/exposure/buildings.gpkg differ
diff --git a/examples/data/setup_exposure_buildings_with_dem_data/test_vulnerability_and_exposure_NSI_with_dem_data/exposure/exposure.csv b/examples/data/setup_exposure_buildings_with_dem_data/test_vulnerability_and_exposure_NSI_with_dem_data/exposure/exposure.csv
new file mode 100644
index 00000000..e1022c4f
--- /dev/null
+++ b/examples/data/setup_exposure_buildings_with_dem_data/test_vulnerability_and_exposure_NSI_with_dem_data/exposure/exposure.csv
@@ -0,0 +1,1995 @@
+Object ID,Object Name,Primary Object Type,Secondary Object Type,Max Potential Damage: Structure,Max Potential Damage: Content,Ground Floor Height,Ground Elevation,Aggregation Label: Census Block,Extraction Method,Damage Function: Structure,Damage Function: Content
+574486973,574486973,RES,RES3A,480189.335,240094.667,0.5,2.580267906188965,450190001001006,centroid,RES3A_structure,RES3A_content
+573783417,573783417,COM,RES4,1177614.2,588807.1,8.0,2.580267906188965,450190002002010,centroid,RES4_structure,RES4_content
+573783433,573783433,COM,RES4,476216.254,238108.127,0.5,2.0590968132019043,450190002002010,centroid,RES4_structure,RES4_content
+573785665,573785665,RES,RES1-2SNB,330007.42,165003.71,0.5,2.6374380588531494,450190002002004,centroid,RES1-2SNB_structure,RES1-2SNB_content
+574346729,574346729,RES,RES3A,472285.148,236142.574,0.5,2.0249850749969482,450190002001030,centroid,RES3A_structure,RES3A_content
+574372707,574372707,RES,RES1-3SNB,389363.808,194681.904,0.5,2.0249850749969482,450190002002008,centroid,RES1-3SNB_structure,RES1-3SNB_content
+574372721,574372721,RES,RES3A,503924.421,251962.21,0.5,2.0249850749969482,450190002002008,centroid,RES3A_structure,RES3A_content
+574476728,574476728,RES,RES1-1SNB,96964.414,48482.207,0.5,3.694910764694214,450190002002018,centroid,RES1-1SNB_structure,RES1-1SNB_content
+574476811,574476811,RES,RES1-1SNB,152049.256,76024.6281,0.5,3.694910764694214,450190002002018,centroid,RES1-1SNB_structure,RES1-1SNB_content
+574486899,574486899,RES,RES3A,480189.335,240094.667,0.5,3.694910764694214,450190002001030,centroid,RES3A_structure,RES3A_content
+574486932,574486932,RES,RES1-1SNB,177705.21,88852.6051,0.5,1.6509150266647339,450190002002005,centroid,RES1-1SNB_structure,RES1-1SNB_content
+573827729,573827729,COM,COM8,37755.4885,37755.4885,0.5,1.6509150266647339,450190002001028,centroid,COM8_structure,COM8_content
+574421669,574421669,RES,RES3A,170840.757,85420.3788,8.0,1.6509150266647339,450190002001029,centroid,RES3A_structure,RES3A_content
+574488781,574488781,RES,RES1-3SNB,374871.082,187435.541,0.5,1.6509150266647339,450190002001009,centroid,RES1-3SNB_structure,RES1-3SNB_content
+573467745,573467745,RES,RES3A,516183.793,258091.896,2.0,1.6509150266647339,450190002002011,centroid,RES3A_structure,RES3A_content
+573779323,573779323,RES,RES1-2SWB,437207.93,218603.965,2.0,3.6249663829803467,450190002002000,centroid,RES1-2SWB_structure,RES1-2SWB_content
+573779375,573779375,RES,RES1-1SNB,226202.175,113101.087,0.5,3.6249663829803467,450190002002000,centroid,RES1-1SNB_structure,RES1-1SNB_content
+573779393,573779393,RES,RES3A,200255.163,100127.581,0.5,3.7134814262390137,450190002002000,centroid,RES3A_structure,RES3A_content
+573779405,573779405,RES,RES1-3SNB,398598.966,199299.483,0.5,3.5045104026794434,450190002002000,centroid,RES1-3SNB_structure,RES1-3SNB_content
+573782594,573782594,RES,RES3A,489787.119,244893.559,0.5,1.9962352514266968,450190002002005,centroid,RES3A_structure,RES3A_content
+573782596,573782596,RES,RES3A,675402.418,337701.209,0.5,2.0721724033355713,450190002002005,centroid,RES3A_structure,RES3A_content
+573782413,573782413,RES,RES3A,437002.054,218501.027,0.5,2.1630349159240723,450190002002006,centroid,RES3A_structure,RES3A_content
+573782415,573782415,RES,RES1-3SNB,369429.359,184714.679,0.5,2.247926712036133,450190002002006,centroid,RES1-3SNB_structure,RES1-3SNB_content
+573782419,573782419,RES,RES3A,448799.6,224399.8,0.5,1.5835663080215454,450190002002006,centroid,RES3A_structure,RES3A_content
+573782453,573782453,RES,RES1-3SNB,448326.4,224163.2,0.5,1.6850732564926147,450190002002006,centroid,RES1-3SNB_structure,RES1-3SNB_content
+573782528,573782528,RES,RES3A,677628.089,338814.044,0.5,3.780707597732544,450190002001032,centroid,RES3A_structure,RES3A_content
+573783136,573783136,RES,RES1-1SNB,187137.546,93568.7731,0.5,3.3954412937164307,450190002002001,centroid,RES1-1SNB_structure,RES1-1SNB_content
+573783158,573783158,PUB,EDU1,4297828.09,4297828.09,0.5,3.3954412937164307,450190002002001,centroid,EDU1_structure,EDU1_content
+573783175,573783175,RES,RES3A,327754.263,163877.131,0.5,2.904144763946533,450190002002001,centroid,RES3A_structure,RES3A_content
+573783185,573783185,RES,RES1-2SNB,438110.47,219055.235,0.5,2.0328288078308105,450190002002001,centroid,RES1-2SNB_structure,RES1-2SNB_content
+573783419,573783419,RES,RES1-2SWB,899667.893,449833.946,2.0,2.0328288078308105,450190002002010,centroid,RES1-2SWB_structure,RES1-2SWB_content
+573783425,573783425,RES,RES1-3SWB,615853.155,307926.577,2.0,1.9762399196624756,450190002002010,centroid,RES1-3SWB_structure,RES1-3SWB_content
+573785435,573785435,COM,COM8,988173.174,988173.174,0.5,3.1606128215789795,450190002001022,centroid,COM8_structure,COM8_content
+573785827,573785827,RES,RES1-3SWB,481445.527,240722.763,2.0,3.1606128215789795,450190002001025,centroid,RES1-3SWB_structure,RES1-3SWB_content
+573785950,573785950,RES,RES1-2SNB,472439.483,236219.741,0.5,3.1718673706054688,450190002002003,centroid,RES1-2SNB_structure,RES1-2SNB_content
+573785982,573785982,RES,RES1-3SNB,544883.847,272441.923,0.5,3.1718673706054688,450190002002003,centroid,RES1-3SNB_structure,RES1-3SNB_content
+573785986,573785986,RES,RES3B,409580.853,204790.426,0.5,1.9627610445022583,450190002002003,centroid,RES3B_structure,RES3B_content
+573818743,573818743,RES,RES1-2SNB,280041.982,140020.991,0.5,3.319775342941284,450190002001004,centroid,RES1-2SNB_structure,RES1-2SNB_content
+573818747,573818747,RES,RES1-1SNB,238450.81,119225.405,0.5,3.319775342941284,450190002001004,centroid,RES1-1SNB_structure,RES1-1SNB_content
+573827725,573827725,COM,COM4,212093.854,212093.854,8.0,3.319775342941284,450190002001028,centroid,COM4_structure,COM4_content
+573827727,573827727,PUB,GOV1,1531018.22,1531018.22,8.0,3.3900787830352783,450190002001028,centroid,GOV1_structure,GOV1_content
+573827740,573827740,RES,RES3A,210487.941,105243.97,8.0,3.385042428970337,450190002001028,centroid,RES3A_structure,RES3A_content
+573827758,573827758,COM,COM4,414162.244,414162.244,8.0,3.385042428970337,450190002001028,centroid,COM4_structure,COM4_content
+573831699,573831699,RES,RES1-2SNB,195993.925,97996.9627,0.5,3.385042428970337,450190002001026,centroid,RES1-2SNB_structure,RES1-2SNB_content
+573831713,573831713,COM,COM4,221441.012,221441.012,0.5,3.3632373809814453,450190002001026,centroid,COM4_structure,COM4_content
+573831715,573831715,RES,RES1-2SNB,359315.841,179657.92,0.5,3.3632373809814453,450190002001026,centroid,RES1-2SNB_structure,RES1-2SNB_content
+574267660,574267660,COM,COM2,411264.761,411264.761,8.0,3.442486524581909,450190002001013,centroid,COM2_structure,COM2_content
+574267669,574267669,RES,RES3A,817491.32,408745.66,0.5,3.442486524581909,450190002001013,centroid,RES3A_structure,RES3A_content
+574267743,574267743,RES,RES1-3SNB,345016.887,172508.443,0.5,3.9389076232910156,450190002001016,centroid,RES1-3SNB_structure,RES1-3SNB_content
+574267753,574267753,RES,RES3A,264783.443,132391.721,0.5,4.070433616638184,450190002001016,centroid,RES3A_structure,RES3A_content
+574268223,574268223,RES,RES1-3SNB,414397.057,207198.528,0.5,2.3864593505859375,450190002002009,centroid,RES1-3SNB_structure,RES1-3SNB_content
+574268225,574268225,RES,RES3A,531400.843,265700.421,0.5,2.446042776107788,450190002002009,centroid,RES3A_structure,RES3A_content
+574268229,574268229,RES,RES1-2SNB,440077.672,220038.836,0.5,2.8119096755981445,450190002002009,centroid,RES1-2SNB_structure,RES1-2SNB_content
+574293801,574293801,RES,RES1-1SNB,265814.38,132907.19,0.5,3.941007375717163,450190002002002,centroid,RES1-1SNB_structure,RES1-1SNB_content
+574346714,574346714,RES,RES1-1SNB,269885.626,134942.813,0.5,3.941007375717163,450190002001003,centroid,RES1-1SNB_structure,RES1-1SNB_content
+574346742,574346742,RES,RES1-2SNB,569213.092,284606.546,0.5,3.941007375717163,450190002001030,centroid,RES1-2SNB_structure,RES1-2SNB_content
+574346745,574346745,RES,RES1-2SNB,329762.78,164881.39,0.5,3.941007375717163,450190002001030,centroid,RES1-2SNB_structure,RES1-2SNB_content
+574346752,574346752,RES,RES1-1SNB,310035.415,155017.707,1.5,3.328754186630249,450190002001030,centroid,RES1-1SNB_structure,RES1-1SNB_content
+574372456,574372456,COM,COM1,40694.094,40694.094,0.5,1.7508022785186768,450190002001017,centroid,COM1_structure,COM1_content
+574372466,574372466,COM,COM4,211663.463,211663.463,0.5,1.7508022785186768,450190002001017,centroid,COM4_structure,COM4_content
+574372521,574372521,RES,RES3A,141992.009,70996.0045,0.5,1.7508022785186768,450190002001031,centroid,RES3A_structure,RES3A_content
+574372563,574372563,COM,COM4,653032.622,653032.622,8.0,1.7508022785186768,450190002001031,centroid,COM4_structure,COM4_content
+574372711,574372711,RES,RES1-2SNB,312504.938,156252.469,0.5,1.7508022785186768,450190002002008,centroid,RES1-2SNB_structure,RES1-2SNB_content
+574409649,574409649,COM,COM2,421925.305,421925.305,8.0,3.8647475242614746,450190002001023,centroid,COM2_structure,COM2_content
+574421679,574421679,COM,COM4,2764995.14,2764995.14,0.5,3.8647475242614746,450190002001029,centroid,COM4_structure,COM4_content
+574437151,574437151,COM,COM1,164588.602,164588.602,0.5,3.8647475242614746,450190002001034,centroid,COM1_structure,COM1_content
+574461085,574461085,RES,RES3A,266985.928,133492.964,8.0,3.8647475242614746,450190002001029,centroid,RES3A_structure,RES3A_content
+574461093,574461093,COM,COM4,177990.619,177990.619,8.0,3.8647475242614746,450190002001017,centroid,COM4_structure,COM4_content
+574476710,574476710,RES,RES1-1SNB,201344.079,100672.039,0.5,3.514539957046509,450190002002018,centroid,RES1-1SNB_structure,RES1-1SNB_content
+574476767,574476767,RES,RES1-2SNB,214533.891,107266.945,0.5,3.514539957046509,450190002002018,centroid,RES1-2SNB_structure,RES1-2SNB_content
+574476775,574476775,RES,RES1-1SNB,206797.159,103398.579,0.5,3.514539957046509,450190002002018,centroid,RES1-1SNB_structure,RES1-1SNB_content
+574476781,574476781,RES,RES1-1SNB,108849.157,54424.5787,0.5,3.514539957046509,450190002002018,centroid,RES1-1SNB_structure,RES1-1SNB_content
+574476804,574476804,RES,RES1-1SNB,96964.414,48482.207,0.5,3.514539957046509,450190002002018,centroid,RES1-1SNB_structure,RES1-1SNB_content
+574476806,574476806,RES,RES1-2SNB,214533.891,107266.945,0.5,3.514539957046509,450190002002018,centroid,RES1-2SNB_structure,RES1-2SNB_content
+574486936,574486936,RES,RES1-2SNB,248128.867,124064.433,0.5,3.514539957046509,450190002002008,centroid,RES1-2SNB_structure,RES1-2SNB_content
+574488778,574488778,RES,RES1-2SNB,305105.785,152552.892,0.5,3.514539957046509,450190002001033,centroid,RES1-2SNB_structure,RES1-2SNB_content
+574488798,574488798,RES,RES3A,472384.782,236192.391,0.5,3.514539957046509,450190002001017,centroid,RES3A_structure,RES3A_content
+574488832,574488832,RES,RES1-3SNB,351839.338,175919.669,0.5,3.514539957046509,450190002001030,centroid,RES1-3SNB_structure,RES1-3SNB_content
+574488833,574488833,RES,RES1-2SNB,272662.957,136331.478,0.5,3.514539957046509,450190002001030,centroid,RES1-2SNB_structure,RES1-2SNB_content
+574488839,574488839,RES,RES1-2SNB,260395.912,130197.956,0.5,3.514539957046509,450190002002000,centroid,RES1-2SNB_structure,RES1-2SNB_content
+574488841,574488841,RES,RES1-3SWB,564363.457,282181.728,2.0,4.216764450073242,450190002001016,centroid,RES1-3SWB_structure,RES1-3SWB_content
+574488844,574488844,RES,RES1-2SNB,294782.511,147391.255,0.5,3.725353240966797,450190002001010,centroid,RES1-2SNB_structure,RES1-2SNB_content
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diff --git a/examples/data/setup_exposure_buildings_with_dem_data/test_vulnerability_and_exposure_NSI_with_dem_data/exposure/region.gpkg b/examples/data/setup_exposure_buildings_with_dem_data/test_vulnerability_and_exposure_NSI_with_dem_data/exposure/region.gpkg
new file mode 100644
index 00000000..c84cd753
Binary files /dev/null and b/examples/data/setup_exposure_buildings_with_dem_data/test_vulnerability_and_exposure_NSI_with_dem_data/exposure/region.gpkg differ
diff --git a/examples/data/setup_exposure_buildings_with_dem_data/test_vulnerability_and_exposure_NSI_with_dem_data/settings.toml b/examples/data/setup_exposure_buildings_with_dem_data/test_vulnerability_and_exposure_NSI_with_dem_data/settings.toml
new file mode 100644
index 00000000..3ae7d2ea
--- /dev/null
+++ b/examples/data/setup_exposure_buildings_with_dem_data/test_vulnerability_and_exposure_NSI_with_dem_data/settings.toml
@@ -0,0 +1,24 @@
+[global]
+crs = "epsg:4326"
+
+[output]
+path = "output"
+
+[output.csv]
+name = "output.csv"
+
+[output.geom]
+name1 = "spatial.gpkg"
+
+[vulnerability]
+file = "./vulnerability/vulnerability_curves.csv"
+unit = "feet"
+step_size = 0.1
+
+[exposure.csv]
+file = "./exposure/exposure.csv"
+
+[exposure.geom]
+crs = "EPSG:4326"
+unit = "ft"
+file1 = "./exposure/buildings.gpkg"
diff --git a/examples/data/setup_exposure_buildings_with_dem_data/test_vulnerability_and_exposure_NSI_with_dem_data/vulnerability/vulnerability_curves.csv b/examples/data/setup_exposure_buildings_with_dem_data/test_vulnerability_and_exposure_NSI_with_dem_data/vulnerability/vulnerability_curves.csv
new file mode 100644
index 00000000..b7baee0f
--- /dev/null
+++ b/examples/data/setup_exposure_buildings_with_dem_data/test_vulnerability_and_exposure_NSI_with_dem_data/vulnerability/vulnerability_curves.csv
@@ -0,0 +1,37 @@
+#UNIT=feet
+#METHOD,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean
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+19.0,0.89,0.69,0.76,0.63,0.525,0.624,0.59,0.843,0.54,0.63,0.68,0.52,0.69,0.79,0.68,0.56,0.562,0.44,0.5,0.44,0.73,0.7,0.807,0.811,0.692,0.764,0.56,0.54,0.82,0.467,0.467,0.467,0.467,0.467,0.467,0.475,0.508,0.67,0.79,0.94,1.0,0.89,0.871,1.0,1.0,0.963,1.0,0.96,1.0,0.634,1.0,1.0,1.0,0.74,0.95,0.88,0.65,0.88,0.82,0.971,0.4,0.391,0.372,0.526,0.6,0.6,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+20.0,0.9,0.71,0.81,0.66,0.525,0.624,0.63,0.843,0.56,0.66,0.74,0.52,0.74,0.83,0.72,0.57,0.562,0.44,0.5,0.44,0.74,0.7,0.807,0.811,0.692,0.764,0.58,0.55,0.82,0.467,0.467,0.467,0.467,0.467,0.467,0.475,0.508,0.72,0.79,0.94,1.0,0.89,0.871,1.0,1.0,0.963,1.0,0.96,1.0,0.634,1.0,1.0,1.0,0.74,0.95,0.88,0.65,0.88,0.83,0.971,0.4,0.391,0.372,0.526,0.6,0.6,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+21.0,0.92,0.74,0.86,0.68,0.525,0.624,0.67,0.843,0.59,0.69,0.8,0.52,0.79,0.87,0.76,0.57,0.562,0.44,0.5,0.44,0.75,0.7,0.807,0.811,0.692,0.764,0.6,0.56,0.82,0.467,0.467,0.467,0.467,0.467,0.467,0.475,0.508,0.77,0.79,0.94,1.0,0.89,0.871,1.0,1.0,0.963,1.0,0.96,1.0,0.634,1.0,1.0,1.0,0.74,0.95,0.88,0.65,0.88,0.83,0.971,0.4,0.391,0.372,0.526,0.6,0.6,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+22.0,0.93,0.76,0.91,0.7,0.525,0.624,0.71,0.843,0.61,0.73,0.86,0.52,0.84,0.91,0.8,0.57,0.562,0.45,0.5,0.45,0.76,0.7,0.807,0.811,0.692,0.764,0.62,0.57,0.82,0.467,0.467,0.467,0.467,0.467,0.467,0.475,0.508,0.82,0.79,0.94,1.0,0.89,0.871,1.0,1.0,0.963,1.0,0.96,1.0,0.634,1.0,1.0,1.0,0.75,0.95,0.88,0.65,0.88,0.83,0.971,0.4,0.391,0.372,0.526,0.6,0.6,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+23.0,0.95,0.78,0.96,0.71,0.525,0.624,0.75,0.843,0.64,0.76,0.92,0.52,0.89,0.95,0.84,0.58,0.562,0.45,0.5,0.45,0.76,0.7,0.807,0.811,0.692,0.764,0.64,0.58,0.82,0.467,0.467,0.467,0.467,0.467,0.467,0.475,0.508,0.87,0.8,0.94,1.0,0.89,0.871,1.0,1.0,0.963,1.0,0.96,1.0,0.634,1.0,1.0,1.0,0.75,0.95,0.88,0.65,0.88,0.83,0.971,0.4,0.391,0.372,0.526,0.6,0.6,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
+24.0,0.96,0.8,1.0,0.73,0.525,0.624,0.79,0.843,0.66,0.78,0.98,0.52,0.94,0.98,0.88,0.58,0.562,0.45,0.5,0.45,0.77,0.7,0.807,0.811,0.692,0.764,0.66,0.59,0.82,0.467,0.467,0.467,0.467,0.467,0.467,0.475,0.508,0.92,0.8,0.94,1.0,0.89,0.871,1.0,1.0,0.963,1.0,0.96,1.0,0.634,1.0,1.0,1.0,0.75,0.95,0.88,0.65,0.88,0.83,0.971,0.4,0.391,0.372,0.526,0.6,0.6,0.83,0.634,0.634,0.634,0.634,0.634,0.634,0.649,0.92,1.0
diff --git a/examples/data/setup_exposure_buildings_with_dem_data/test_vulnerability_and_exposure_NSI_with_dem_data/vulnerability/vulnerability_identifiers.csv b/examples/data/setup_exposure_buildings_with_dem_data/test_vulnerability_and_exposure_NSI_with_dem_data/vulnerability/vulnerability_identifiers.csv
new file mode 100644
index 00000000..b33d4845
--- /dev/null
+++ b/examples/data/setup_exposure_buildings_with_dem_data/test_vulnerability_and_exposure_NSI_with_dem_data/vulnerability/vulnerability_identifiers.csv
@@ -0,0 +1,77 @@
+FIAT Damage Function Name,Exposure Link,Damage Type,ID,Occupancy,Description,Source,full_identifier,Damage function name
+AGR1,AGR1,structure,616,AGR1,"Average Agriculture, structure",USACE - Galveston,"616AGR1AverageAgriculture,structureUSACE-Galveston",AGR1_structure
+COM1,COM1,structure,217,COM1,"Average Retail, Structure",USACE - Galveston,"217COM1AverageRetail,StructureUSACE-Galveston",COM1_structure
+COM10,COM10,structure,543,COM10,"Garage, structure",USACE - Galveston,"543COM10Garage,structureUSACE-Galveston",COM10_structure
+COM2,COM2,structure,341,COM2,"Average wholesale, Structure",USACE - Galveston,"341COM2Averagewholesale,StructureUSACE-Galveston",COM2_structure
+COM3,COM3,structure,32,COM3-S-MostLikely,Average Personal & Repair Services,USACE - IWR,32COM3-S-MostLikelyAveragePersonal&RepairServicesUSACE-IWR,COM3_structure
+COM4,COM4,structure,35,COM4-S-MostLikely,Average Prof/Tech Services,USACE - IWR,35COM4-S-MostLikelyAverageProf/TechServicesUSACE-IWR,COM4_structure
+COM5,COM5,structure,467,COM5,"Bank, Structure",USACE - Galveston,"467COM5Bank,StructureUSACE-Galveston",COM5_structure
+COM6,COM6,structure,38,COM6-S-MostLikely,Hospital,USACE - IWR,38COM6-S-MostLikelyHospitalUSACE-IWR,COM6_structure
+COM7,COM7,structure,475,COM7,"Average Medical Office, Structure",USACE - Galveston,"475COM7AverageMedicalOffice,StructureUSACE-Galveston",COM7_structure
+COM8,COM8,structure,493,COM8,"Average Entertainment/Recreation, Structure",USACE - Galveston,"493COM8AverageEntertainment/Recreation,StructureUSACE-Galveston",COM8_structure
+COM9,COM9,structure,532,COM9,"Average theatre, Structure",USACE - Galveston,"532COM9Averagetheatre,StructureUSACE-Galveston",COM9_structure
+EDU1,EDU1,structure,53,EDU1-S-MostLikely,Average school,USACE - IWR,53EDU1-S-MostLikelyAverageschoolUSACE-IWR,EDU1_structure
+EDU2,EDU2,structure,652,EDU2,"Average college/university, structure",USACE - Galveston,"652EDU2Averagecollege/university,structureUSACE-Galveston",EDU2_structure
+GOV1,GOV1,structure,631,GOV1,"Average government services, structure",USACE - Galveston,"631GOV1Averagegovernmentservices,structureUSACE-Galveston",GOV1_structure
+GOV2,GOV2,structure,640,GOV2,"Average emergency response, Structure",USACE - Galveston,"640GOV2Averageemergencyresponse,StructureUSACE-Galveston",GOV2_structure
+IND1,IND1,structure,545,IND1,"Average heavy industrial, Structure",USACE - Galveston,"545IND1Averageheavyindustrial,StructureUSACE-Galveston",IND1_structure
+IND2,IND2,structure,44,IND2-S-MostLikely,Average light industrial,USACE - IWR,44IND2-S-MostLikelyAveragelightindustrialUSACE-IWR,IND2_structure
+IND3,IND3,structure,575,IND3,"Average Food/Drug/Chem, Structure",USACE - Galveston,"575IND3AverageFood/Drug/Chem,StructureUSACE-Galveston",IND3_structure
+IND4,IND4,structure,586,IND4,"Average Metals/Minerals processing, structure",USACE - Galveston,"586IND4AverageMetals/Mineralsprocessing,structureUSACE-Galveston",IND4_structure
+IND5,IND5,structure,591,IND5,"Average High Technology, structure",USACE - Galveston,"591IND5AverageHighTechnology,structureUSACE-Galveston",IND5_structure
+IND6,IND6,structure,592,IND6,"Average Construction, structure",USACE - Galveston,"592IND6AverageConstruction,structureUSACE-Galveston",IND6_structure
+REL1,REL1,structure,47,REL1-S-MostLikely,Church,USACE - IWR,47REL1-S-MostLikelyChurchUSACE-IWR,REL1_structure
+RES1-1SNB,RES1-1SNB,structure,2,RES1-1SNB-S-MostLikely,"Res 1, 1 Story no Basement",USACE - IWR,"2RES1-1SNB-S-MostLikelyRes1,1StorynoBasementUSACE-IWR",RES1-1SNB_structure
+RES1-1SWB,RES1-1SWB,structure,5,RES1-1SWB-S-MostLikely,"Res 1, 1 Story w/ Basement",USACE - IWR,"5RES1-1SWB-S-MostLikelyRes1,1Storyw/BasementUSACE-IWR",RES1-1SWB_structure
+RES1-2SNB,RES1-2SNB,structure,8,RES1-2SNB-S-MostLikely,"Res 1, 2 Story no Basement",USACE - IWR,"8RES1-2SNB-S-MostLikelyRes1,2StorynoBasementUSACE-IWR",RES1-2SNB_structure
+RES1-2SWB,RES1-2SWB,structure,11,RES1-2SWB-S-MostLikely,"Res 1, 2 Story w/ Basement",USACE - IWR,"11RES1-2SWB-S-MostLikelyRes1,2Storyw/BasementUSACE-IWR",RES1-2SWB_structure
+RES1-3SNB,RES1-3SNB,structure,117,RES1,"three or more floors, no basement, Structure, V-Zone",FIA,"117RES1threeormorefloors,nobasement,Structure,V-ZoneFIA",RES1-3SNB_structure
+RES1-3SWB,RES1-3SWB,structure,127,RES1,"three or more floors, w/ basement, Structure, V-Zone",FIA,"127RES1threeormorefloors,w/basement,Structure,V-ZoneFIA",RES1-3SWB_structure
+RES2,RES2,structure,20,RES2-S-MostLikely,Mobile home,USACE - IWR,20RES2-S-MostLikelyMobilehomeUSACE-IWR,RES2_structure
+RES3A,RES3A,structure,23,RES3-S-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"23RES3-S-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR",RES3A_structure
+RES3B,RES3B,structure,23,RES3-S-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"23RES3-S-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR",RES3B_structure
+RES3C,RES3C,structure,23,RES3-S-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"23RES3-S-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR",RES3C_structure
+RES3D,RES3D,structure,23,RES3-S-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"23RES3-S-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR",RES3D_structure
+RES3E,RES3E,structure,23,RES3-S-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"23RES3-S-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR",RES3E_structure
+RES3F,RES3F,structure,23,RES3-S-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"23RES3-S-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR",RES3F_structure
+RES4,RES4,structure,26,RES4-S-MostLikely,"Average Hotel, & Motel",USACE - IWR,"26RES4-S-MostLikelyAverageHotel,&MotelUSACE-IWR",RES4_structure
+RES5,RES5,structure,50,RES5-PRO-SVC-S-MostLikely,Nursing Home,USACE - IWR,50RES5-PRO-SVC-S-MostLikelyNursingHomeUSACE-IWR,RES5_structure
+RES6,RES6,structure,215,RES6,"Nursing Home, structure",USACE - Galveston,"215RES6NursingHome,structureUSACE-Galveston",RES6_structure
+AGR1,AGR1,content,460,AGR1 ,"Average Agriculture, Contents (Equipment/Inventory)",USACE - Galveston,"460AGR1AverageAgriculture,Contents(Equipment/Inventory)USACE-Galveston",AGR1_content
+COM1,COM1,content,90,COM1 ,Average retail trade contents (Inventory/Equipment),USACE - Galveston,90COM1Averageretailtradecontents(Inventory/Equipment)USACE-Galveston,COM1_content
+COM10,COM10,content,357,COM10,"Garage, Contents (Inventory)",USACE - Galveston,"357COM10Garage,Contents(Inventory)USACE-Galveston",COM10_content
+COM2,COM2,content,195,COM2 ,"Average wholesale trade, Contents",USACE - Galveston,"195COM2Averagewholesaletrade,ContentsUSACE-Galveston",COM2_content
+COM3,COM3,content,92,COM3-C-MostLikely,Average Personal & Repair Services,USACE - IWR,92COM3-C-MostLikelyAveragePersonal&RepairServicesUSACE-IWR,COM3_content
+COM4,COM4,content,95,COM4-C-MostLikely,Average Prof/Tech Services,USACE - IWR,95COM4-C-MostLikelyAverageProf/TechServicesUSACE-IWR,COM4_content
+COM5,COM5,content,304,COM5 ,"Bank, Contents",USACE - Galveston,"304COM5Bank,ContentsUSACE-Galveston",COM5_content
+COM6,COM6,content,98,COM6-C-MostLikely,Hospital,USACE - IWR,98COM6-C-MostLikelyHospitalUSACE-IWR,COM6_content
+COM7,COM7,content,312,COM7 ,"Average Medical Offic/Clinic, Contents (Equipment/Inventory)",USACE - Galveston,"312COM7AverageMedicalOffic/Clinic,Contents(Equipment/Inventory)USACE-Galveston",COM7_content
+COM8,COM8,content,322,COM8 ,"Average Entertainment/Recreation, Contents (Equipment/Inventory)",USACE - Galveston,"322COM8AverageEntertainment/Recreation,Contents(Equipment/Inventory)USACE-Galveston",COM8_content
+COM9,COM9,content,352,COM9 ,"Theater, Contents (Equipment)",USACE - Galveston,"352COM9Theater,Contents(Equipment)USACE-Galveston",COM9_content
+EDU1,EDU1,content,113,EDU1-C-MostLikely,Average school,USACE - IWR,113EDU1-C-MostLikelyAverageschoolUSACE-IWR,EDU1_content
+EDU2,EDU2,content,485,EDU2 ,"Average College/University, Contents (Equipment/Inventory)",USACE - Galveston,"485EDU2AverageCollege/University,Contents(Equipment/Inventory)USACE-Galveston",EDU2_content
+GOV1,GOV1,content,472,GOV1 ,"Average Govt Services, contents (Equipment)",USACE - Galveston,"472GOV1AverageGovtServices,contents(Equipment)USACE-Galveston",GOV1_content
+GOV2,GOV2,content,477,GOV2 ,"Average emergency response, contents (Equipment/Inventory)",USACE - Galveston,"477GOV2Averageemergencyresponse,contents(Equipment/Inventory)USACE-Galveston",GOV2_content
+IND1,IND1,content,358,IND1 ,"Average Heavy Industrial, Contents (Equipment/Inventory)",USACE - Galveston,"358IND1AverageHeavyIndustrial,Contents(Equipment/Inventory)USACE-Galveston",IND1_content
+IND2,IND2,content,104,IND2-C-MostLikely,Average light industrial,USACE - IWR,104IND2-C-MostLikelyAveragelightindustrialUSACE-IWR,IND2_content
+IND3,IND3,content,408,IND3 ,"Average Food/Drugs/Chemicals, Contents (Equipment/Inventory)",USACE - Galveston,"408IND3AverageFood/Drugs/Chemicals,Contents(Equipment/Inventory)USACE-Galveston",IND3_content
+IND4,IND4,content,433,IND4 ,"Average Metals/Minerals Processing, Contents (Equipment/Inventory)",USACE - Galveston,"433IND4AverageMetals/MineralsProcessing,Contents(Equipment/Inventory)USACE-Galveston",IND4_content
+IND5,IND5,content,442,IND5 ,"Average High Technology, contents (Equipment/Inventory)",USACE - Galveston,"442IND5AverageHighTechnology,contents(Equipment/Inventory)USACE-Galveston",IND5_content
+IND6,IND6,content,443,IND6 ,"Average Construction, contents (Equipment/Inventory)",USACE - Galveston,"443IND6AverageConstruction,contents(Equipment/Inventory)USACE-Galveston",IND6_content
+REL1,REL1,content,107,REL1-C-MostLikely,Church,USACE - IWR,107REL1-C-MostLikelyChurchUSACE-IWR,REL1_content
+RES1-1SNB,RES1-1SNB,content,62,RES1-1SNB-C-MostLikely,"Res 1, 1 Story no Basement",USACE - IWR,"62RES1-1SNB-C-MostLikelyRes1,1StorynoBasementUSACE-IWR",RES1-1SNB_content
+RES1-1SWB,RES1-1SWB,content,65,RES1-1SWB-C-MostLikely,"Res 1, 1 Story w/ Basement",USACE - IWR,"65RES1-1SWB-C-MostLikelyRes1,1Storyw/BasementUSACE-IWR",RES1-1SWB_content
+RES1-2SNB,RES1-2SNB,content,68,RES1-2SNB-C-MostLikely,"Res 1, 2 Story no Basement",USACE - IWR,"68RES1-2SNB-C-MostLikelyRes1,2StorynoBasementUSACE-IWR",RES1-2SNB_content
+RES1-2SWB,RES1-2SWB,content,71,RES1-2SWB-C-MostLikely,"Res 1, 2 Story w/ Basement",USACE - IWR,"71RES1-2SWB-C-MostLikelyRes1,2Storyw/BasementUSACE-IWR",RES1-2SWB_content
+RES1-3SNB,RES1-3SNB,content,33,RES1 ,"three or more floors, no basement, Contents, V-Zone",FIA (MOD.),"33RES1threeormorefloors,nobasement,Contents,V-ZoneFIA(MOD.)",RES1-3SNB_content
+RES1-3SWB,RES1-3SWB,content,34,RES1 ,"three or more floors, w/ basement, Contents, V-Zone",FIA (MOD.),"34RES1threeormorefloors,w/basement,Contents,V-ZoneFIA(MOD.)",RES1-3SWB_content
+RES2,RES2,content,80,RES2-C-MostLikely,Mobile home,USACE - IWR,80RES2-C-MostLikelyMobilehomeUSACE-IWR,RES2_content
+RES3A,RES3A,content,83,RES3-C-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"83RES3-C-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR",RES3A_content
+RES3B,RES3B,content,83,RES3-C-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"83RES3-C-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR",RES3B_content
+RES3C,RES3C,content,83,RES3-C-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"83RES3-C-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR",RES3C_content
+RES3D,RES3D,content,83,RES3-C-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"83RES3-C-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR",RES3D_content
+RES3E,RES3E,content,83,RES3-C-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"83RES3-C-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR",RES3E_content
+RES3F,RES3F,content,83,RES3-C-MostLikely,"Condominium, living area on multiple floors",USACE - IWR,"83RES3-C-MostLikelyCondominium,livingareaonmultiplefloorsUSACE-IWR",RES3F_content
+RES4,RES4,content,86,RES4-C-MostLikely,"Average Hotel, & Motel",USACE - IWR,"86RES4-C-MostLikelyAverageHotel,&MotelUSACE-IWR",RES4_content
+RES5,RES5,content,110,RES5-PRO-SVC-C-MostLikely,Nursing Home,USACE - IWR,110RES5-PRO-SVC-C-MostLikelyNursingHomeUSACE-IWR,RES5_content
+RES6,RES6,content,89,RES6 ,"Nursing Home, contents (Equipment)",USACE - Galveston,"89RES6NursingHome,contents(Equipment)USACE-Galveston",RES6_content
diff --git a/examples/data/setup_new_composite_area/test_read/exposure/aggregation_areas/block_groups.gpkg b/examples/data/setup_new_composite_area/test_read/exposure/aggregation_areas/block_groups.gpkg
new file mode 100644
index 00000000..d502c14f
Binary files /dev/null and b/examples/data/setup_new_composite_area/test_read/exposure/aggregation_areas/block_groups.gpkg differ
diff --git a/examples/data/setup_new_composite_area/test_read/exposure/building_points.gpkg b/examples/data/setup_new_composite_area/test_read/exposure/building_points.gpkg
new file mode 100644
index 00000000..eb21a7ca
Binary files /dev/null and b/examples/data/setup_new_composite_area/test_read/exposure/building_points.gpkg differ
diff --git a/examples/data/setup_new_composite_area/test_read/exposure/exposure.csv b/examples/data/setup_new_composite_area/test_read/exposure/exposure.csv
new file mode 100644
index 00000000..e25d0547
--- /dev/null
+++ b/examples/data/setup_new_composite_area/test_read/exposure/exposure.csv
@@ -0,0 +1,1995 @@
+Object ID,Object Name,Primary Object Type,Secondary Object Type,Max Potential Damage: Structure,Max Potential Damage: Content,Ground Elevation,X Coordinate,Y Coordinate,Aggregation Label: Census Block,Ground Floor Height,Extraction Method,Damage Function: Structure,Damage Function: Content
+574486973,574486973,RES,RES3A,480189.335,240094.667,7.298348065,-79.93682513,32.77688289,4.5019E+14,1,centroid,RES3A,RES3A
+573783417,573783417,COM,RES4,1177614.2,588807.1,8.381748562,-79.93000008,32.77054524,4.5019E+14,1,centroid,RES4,RES4
+573783433,573783433,COM,RES4,476216.254,238108.127,7.374183635,-79.92968382,32.7712713,4.5019E+14,1,centroid,RES4,RES4
+573785665,573785665,RES,RES1-2SNB,330007.42,165003.71,7.36403129,-79.93179589,32.77336473,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+574346729,574346729,RES,RES3A,472285.148,236142.574,10.90203092,-79.93064525,32.77485433,4.5019E+14,1,centroid,RES3A,RES3A
+574372707,574372707,RES,RES1-3SNB,389363.808,194681.904,6.365223632,-79.93187273,32.77098914,4.5019E+14,1,centroid,RES1-3SNB,RES1-3SNB
+574372721,574372721,RES,RES3A,503924.421,251962.21,7.12000971,-79.93191719,32.77164194,4.5019E+14,1,centroid,RES3A,RES3A
+574476728,574476728,RES,RES1-1SNB,96964.414,48482.207,5.867157192,-79.93241085,32.76996811,4.5019E+14,1,centroid,RES1-1SNB,RES1-1SNB
+574476811,574476811,RES,RES1-1SNB,152049.256,76024.6281,5.867157192,-79.93241085,32.76996811,4.5019E+14,1,centroid,RES1-1SNB,RES1-1SNB
+574486899,574486899,RES,RES3A,480189.335,240094.667,12.33366492,-79.93027436,32.77536076,4.5019E+14,1,centroid,RES3A,RES3A
+574486932,574486932,RES,RES1-1SNB,177705.21,88852.6051,6.460120566,-79.92998998,32.77314466,4.5019E+14,1,centroid,RES1-1SNB,RES1-1SNB
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+573467726,573467726,RES,RES1-2SNB,421375.203,210687.601,7.971950971,-79.92922584,32.77068669,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+573467727,573467727,COM,RES4,682840.128,341420.064,5.998637455,-79.92868859,32.77061156,4.5019E+14,1,centroid,RES4,RES4
+573467728,573467728,COM,RES4,447990.428,223995.214,6.607965902,-79.92929336,32.77085682,4.5019E+14,1,centroid,RES4,RES4
+573467729,573467729,RES,RES3B,639434.436,319717.218,6.184400969,-79.92860345,32.77085682,4.5019E+14,1,centroid,RES3B,RES3B
+573467730,573467730,RES,RES1-3SNB,950659.269,475329.634,5.694120288,-79.928633,32.77105956,4.5019E+14,1,centroid,RES1-3SNB,RES1-3SNB
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+573467747,573467747,RES,RES1-3SNB,486027.03,243013.515,6.769053327,-79.929019,32.770324,4.5019E+14,1,centroid,RES1-3SNB,RES1-3SNB
+573779311,573779311,RES,RES1-2SNB,217879.449,108939.724,5.791196858,-79.92878222,32.77309016,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+573779319,573779319,RES,RES3A,471108.706,235554.353,7.738122412,-79.92765306,32.77323472,4.5019E+14,1,centroid,RES3A,RES3A
+573779335,573779335,RES,RES1-1SNB,256055.142,128027.571,9.352340616,-79.92764858,32.77351557,4.5019E+14,1,centroid,RES1-1SNB,RES1-1SNB
+573779357,573779357,RES,RES1-3SNB,380946.441,190473.22,10.36516436,-79.92840483,32.77411255,4.5019E+14,1,centroid,RES1-3SNB,RES1-3SNB
+573779369,573779369,RES,RES1-2SNB,374450.35,187225.175,11.93594548,-79.92751869,32.77433957,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+573779385,573779385,RES,RES3A,581713.203,290856.601,12.28355087,-79.92749568,32.77453773,4.5019E+14,1,centroid,RES3A,RES3A
+573779415,573779415,RES,RES1-3SNB,431428.087,215714.043,12.26185305,-79.92743299,32.77502193,4.5019E+14,1,centroid,RES1-3SNB,RES1-3SNB
+573782417,573782417,RES,RES1-3SNB,363979.656,181989.828,9.372612453,-79.92933255,32.77226794,4.5019E+14,1,centroid,RES1-3SNB,RES1-3SNB
+573782425,573782425,RES,RES1-2SNB,366466.821,183233.41,9.011924555,-79.92929796,32.77237201,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+573782443,573782443,RES,RES3A,543712.387,271856.193,5.378797627,-79.92831106,32.77264062,4.5019E+14,1,centroid,RES3A,RES3A
+573782449,573782449,RES,RES1-2SNB,472033.925,236016.962,5.179306272,-79.92864422,32.77279569,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+573782516,573782516,RES,RES1-3SNB,451364.729,225682.364,11.94115424,-79.92865918,32.7750572,4.5019E+14,1,centroid,RES1-3SNB,RES1-3SNB
+573782540,573782540,RES,RES1-2SNB,278796.48,139398.24,12.30676852,-79.9284404,32.77566618,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+573782554,573782554,COM,COM4,426627.449,426627.449,13.15236603,-79.92852646,32.77599619,4.5019E+14,1,centroid,COM4,COM4
+573782586,573782586,RES,RES3A,421325.574,210662.787,8.191982072,-79.93017261,32.77290957,4.5019E+14,1,centroid,RES3A,RES3A
+573783169,573783169,RES,RES3A,512267.747,256133.873,11.97290897,-79.92919073,32.77440531,4.5019E+14,1,centroid,RES3A,RES3A
+573783179,573783179,RES,RES3A,606289.125,303144.562,12.5249974,-79.92928354,32.77452729,4.5019E+14,1,centroid,RES3A,RES3A
+573783187,573783187,RES,RES1-2SNB,386160.703,193080.351,11.5436022,-79.93016895,32.77463032,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+573783189,573783189,COM,COM3,1640307.87,1640307.87,10.78047343,-79.93028267,32.7746644,4.5019E+14,1,centroid,COM3,COM3
+573783199,573783199,RES,RES1-2SNB,551102.006,275551.003,12.75948849,-79.92921208,32.77472151,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+573783443,573783443,RES,RES1-2SNB,311772.137,155886.068,10.10444409,-79.93015806,32.77191319,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+573783445,573783445,RES,RES1-2SNB,680463.744,340231.872,8.827352744,-79.92996727,32.77182933,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+573827136,573827136,COM,COM5,2936504.86,2936504.86,13.54706299,-79.92746902,32.77712292,4.5019E+14,1,centroid,COM5,COM5
+573827731,573827731,COM,COM1,194515.404,194515.404,12.23108548,-79.92959283,32.77679411,4.5019E+14,1,centroid,COM1,COM1
+573827733,573827733,COM,COM3,145886.553,145886.553,12.23108548,-79.92959283,32.77679411,4.5019E+14,1,centroid,COM3,COM3
+573827735,573827735,COM,COM4,81048.0852,81048.0852,12.23108548,-79.92959283,32.77679411,4.5019E+14,1,centroid,COM4,COM4
+573827754,573827754,COM,COM4,95724.6697,95724.6697,11.99108448,-79.93017988,32.77681959,4.5019E+14,1,centroid,COM4,COM4
+574267735,574267735,RES,RES1-1SNB,381021.716,190510.858,12.98996214,-79.92744398,32.77522114,4.5019E+14,1,centroid,RES1-1SNB,RES1-1SNB
+574267737,574267737,RES,RES1-1SNB,273325.341,136662.67,12.70973817,-79.92811401,32.77515289,4.5019E+14,1,centroid,RES1-1SNB,RES1-1SNB
+574267739,574267739,RES,RES1-2SNB,413187.234,206593.617,13.5823017,-79.92800828,32.77526518,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+574267749,574267749,RES,RES3A,491247.521,245623.76,13.51820246,-79.92788864,32.77557876,4.5019E+14,1,centroid,RES3A,RES3A
+574267976,574267976,RES,RES1-2SNB,385155.033,192577.516,13.53189744,-79.92794047,32.77603579,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+574268005,574268005,RES,RES1-1SNB,257591.82,128795.91,12.59055234,-79.92772127,32.77604828,4.5019E+14,1,centroid,RES1-1SNB,RES1-1SNB
+574346733,574346733,RES,RES1-2SNB,345561.158,172780.579,13.16675875,-79.92987341,32.77497559,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+574346737,574346737,COM,COM3,128711.316,128711.316,12.62985934,-79.92934753,32.77496936,4.5019E+14,1,centroid,COM3,COM3
+574346738,574346738,RES,RES1-3SNB,569110.405,284555.202,12.57429248,-79.92997655,32.77497829,4.5019E+14,1,centroid,RES1-3SNB,RES1-3SNB
+574346740,574346740,RES,RES1-2SNB,272662.957,136331.478,12.62266219,-79.92924438,32.77514479,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+574346744,574346744,PUB,REL1,1281715.19,1281715.19,13.0151455,-79.92929333,32.77533709,4.5019E+14,1,centroid,REL1,REL1
+574346753,574346753,RES,RES1-2SNB,311698.604,155849.302,13.77097465,-79.92981335,32.77577007,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+574372447,574372447,COM,COM1,227020.051,227020.051,12.85938964,-79.92900036,32.77610772,4.5019E+14,1,centroid,COM1,COM1
+574372448,574372448,IND,IND2,134849.922,202274.884,12.85938964,-79.92900036,32.77610772,4.5019E+14,1,centroid,IND2,IND2
+574372473,574372473,COM,COM1,424486.831,424486.831,13.03663915,-79.92881546,32.7764473,4.5019E+14,1,centroid,COM1,COM1
+574372533,574372533,COM,COM7,393804.35,590706.525,11.22623405,-79.93032633,32.7759983,4.5019E+14,1,centroid,COM7,COM7
+574372543,574372543,RES,RES3A,338190.212,169095.106,12.60003902,-79.92938805,32.77623669,4.5019E+14,1,centroid,RES3A,RES3A
+574372567,574372567,COM,COM8,284295.791,284295.791,12.21837608,-79.92958905,32.77638171,4.5019E+14,1,centroid,COM8,COM8
+574421681,574421681,COM,COM8,778149.815,778149.815,13.54457556,-79.92846361,32.77697303,4.5019E+14,1,centroid,COM8,COM8
+574421683,574421683,COM,COM1,360197.146,360197.146,13.54457556,-79.92846361,32.77697303,4.5019E+14,1,centroid,COM1,COM1
+574431298,574431298,COM,COM4,1338267.91,1338267.91,10.1165989,-79.92646291,32.77728981,4.5019E+14,1,centroid,COM4,COM4
+574461065,574461065,RES,RES3A,266985.928,133492.964,12.13528704,-79.9297417,32.77681823,4.5019E+14,1,centroid,RES3A,RES3A
+574461073,574461073,COM,COM4,177990.619,177990.619,12.27511783,-79.92950828,32.77642489,4.5019E+14,1,centroid,COM4,COM4
+574461077,574461077,RES,RES3A,273660.577,136830.288,12.4406709,-79.92903083,32.7763684,4.5019E+14,1,centroid,RES3A,RES3A
+574461079,574461079,COM,COM4,177990.619,177990.619,12.86096032,-79.92860499,32.77648743,4.5019E+14,1,centroid,COM4,COM4
+574488771,574488771,RES,RES1-1SNB,222806.066,111403.033,13.26134397,-79.92896336,32.77597596,4.5019E+14,1,centroid,RES1-1SNB,RES1-1SNB
+574488793,574488793,RES,RES1-2SNB,216903.661,108451.83,12.29002837,-79.92736548,32.77640732,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+574488804,574488804,RES,RES1-2SNB,292527.098,146263.549,13.21022478,-79.92816916,32.77627743,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+574488806,574488806,RES,RES1-3SNB,358700.469,179350.234,13.36457265,-79.92824222,32.77627542,4.5019E+14,1,centroid,RES1-3SNB,RES1-3SNB
+574488816,574488816,RES,RES1-1SNB,221789.878,110894.939,6.334944159,-79.92574958,32.77633187,4.5019E+14,1,centroid,RES1-1SNB,RES1-1SNB
+574488819,574488819,RES,RES1-3SNB,416126.362,208063.181,6.052678219,-79.9267068,32.77616474,4.5019E+14,1,centroid,RES1-3SNB,RES1-3SNB
+574488830,574488830,RES,RES1-1SNB,182232.731,91116.3657,12.69306217,-79.92898379,32.77518059,4.5019E+14,1,centroid,RES1-1SNB,RES1-1SNB
+574488834,574488834,RES,RES1-3SNB,384398.576,192199.288,12.90637406,-79.92951787,32.77470617,4.5019E+14,1,centroid,RES1-3SNB,RES1-3SNB
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+574488848,574488848,RES,RES1-2SNB,322415.251,161207.625,13.14323291,-79.92763108,32.77497895,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
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diff --git a/examples/data/setup_new_composite_area/test_read/exposure/region.gpkg b/examples/data/setup_new_composite_area/test_read/exposure/region.gpkg
new file mode 100644
index 00000000..2c06a9ef
Binary files /dev/null and b/examples/data/setup_new_composite_area/test_read/exposure/region.gpkg differ
diff --git a/examples/data/setup_new_composite_area/test_read/hazard/Current_prob_event_set_combined_doNothing_withSeaWall_RP=100_max_flood_depth.nc b/examples/data/setup_new_composite_area/test_read/hazard/Current_prob_event_set_combined_doNothing_withSeaWall_RP=100_max_flood_depth.nc
new file mode 100644
index 00000000..81dddd9f
Binary files /dev/null and b/examples/data/setup_new_composite_area/test_read/hazard/Current_prob_event_set_combined_doNothing_withSeaWall_RP=100_max_flood_depth.nc differ
diff --git a/examples/data/setup_new_composite_area/test_read/new_development_area_test.gpkg b/examples/data/setup_new_composite_area/test_read/new_development_area_test.gpkg
new file mode 100644
index 00000000..dc9519cb
Binary files /dev/null and b/examples/data/setup_new_composite_area/test_read/new_development_area_test.gpkg differ
diff --git a/hydromt_fiat/data/social_vulnerability/test_blockgroup_shp/tl_2022_45_bg.cpg b/examples/data/setup_new_composite_area/test_read/reference_groundHeight_test.cpg
similarity index 100%
rename from hydromt_fiat/data/social_vulnerability/test_blockgroup_shp/tl_2022_45_bg.cpg
rename to examples/data/setup_new_composite_area/test_read/reference_groundHeight_test.cpg
diff --git a/examples/data/setup_new_composite_area/test_read/reference_groundHeight_test.dbf b/examples/data/setup_new_composite_area/test_read/reference_groundHeight_test.dbf
new file mode 100644
index 00000000..ce5d81d9
Binary files /dev/null and b/examples/data/setup_new_composite_area/test_read/reference_groundHeight_test.dbf differ
diff --git a/examples/data/setup_new_composite_area/test_read/reference_groundHeight_test.prj b/examples/data/setup_new_composite_area/test_read/reference_groundHeight_test.prj
new file mode 100644
index 00000000..f45cbadf
--- /dev/null
+++ b/examples/data/setup_new_composite_area/test_read/reference_groundHeight_test.prj
@@ -0,0 +1 @@
+GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]]
\ No newline at end of file
diff --git a/examples/data/setup_new_composite_area/test_read/reference_groundHeight_test.qix b/examples/data/setup_new_composite_area/test_read/reference_groundHeight_test.qix
new file mode 100644
index 00000000..54c17c12
Binary files /dev/null and b/examples/data/setup_new_composite_area/test_read/reference_groundHeight_test.qix differ
diff --git a/examples/data/setup_new_composite_area/test_read/reference_groundHeight_test.shp b/examples/data/setup_new_composite_area/test_read/reference_groundHeight_test.shp
new file mode 100644
index 00000000..89f3e873
Binary files /dev/null and b/examples/data/setup_new_composite_area/test_read/reference_groundHeight_test.shp differ
diff --git a/examples/data/setup_new_composite_area/test_read/reference_groundHeight_test.shx b/examples/data/setup_new_composite_area/test_read/reference_groundHeight_test.shx
new file mode 100644
index 00000000..33153795
Binary files /dev/null and b/examples/data/setup_new_composite_area/test_read/reference_groundHeight_test.shx differ
diff --git a/examples/data/setup_new_composite_area/test_read/settings.toml b/examples/data/setup_new_composite_area/test_read/settings.toml
new file mode 100644
index 00000000..c9018a8b
--- /dev/null
+++ b/examples/data/setup_new_composite_area/test_read/settings.toml
@@ -0,0 +1,22 @@
+[global]
+crs = "epsg:4326"
+
+[output]
+path = "output"
+
+[output.csv]
+name = "output.csv"
+
+[output.geom]
+name1 = "spatial.gpkg"
+
+[vulnerability]
+file = "./vulnerability/vulnerability_curves.csv"
+scale = 0.1
+
+[exposure.csv]
+file = "./exposure/exposure.csv"
+
+[exposure.geom]
+crs = "EPSG:4326"
+file1 = "./exposure/building_points.gpkg"
diff --git a/examples/data/setup_new_composite_area/test_read/vulnerability/vulnerability_curves.csv b/examples/data/setup_new_composite_area/test_read/vulnerability/vulnerability_curves.csv
new file mode 100644
index 00000000..81d37acf
--- /dev/null
+++ b/examples/data/setup_new_composite_area/test_read/vulnerability/vulnerability_curves.csv
@@ -0,0 +1,32 @@
+#UNIT=feet
+#METHOD,max,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean
+water depth [feet],AGR1,COM1,COM10,COM2,COM3,COM4,COM5,COM6,COM7,COM8,COM9,EDU1,GOV1,GOV2,IND1,IND2,IND3,IND4,IND5,IND6,REL1,RES1-1SNB,RES1-1SWB,RES1-2SNB,RES1-2SWB,RES1-3SNB,RES1-3SWB,RES2,RES3A,RES3B,RES3C,RES3D,RES3E,RES3F,RES4,RES5,RES6
+-4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,0.0,0.04,0.0,0.03,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,0.0,0.04,0.0,0.03,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,0.0,0.08,0.0,0.06,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.11,0.0,0.14,0.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+0.0,0.0,0.01,0.0,0.0,0.0,0.02,0.0,0.0,0.02,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.18,0.17,0.11,0.19,0.05,0.12,0.11,0.12,0.12,0.12,0.12,0.12,0.12,0.0,0.0,0.0
+1.0,0.06,0.09,0.03,0.05,0.09,0.11,0.11,0.0,0.11,0.09,0.02,0.05,0.05,0.07,0.1,0.09,0.13,0.1,0.13,0.22,0.1,0.22,0.21,0.12,0.21,0.08,0.14,0.44,0.25,0.25,0.25,0.25,0.25,0.25,0.03,0.07,0.07
+2.0,0.11,0.14,0.05,0.08,0.12,0.16,0.11,0.0,0.12,0.11,0.04,0.07,0.08,0.1,0.12,0.14,0.14,0.14,0.14,0.31,0.11,0.25,0.29,0.14,0.26,0.12,0.2,0.63,0.5,0.5,0.5,0.5,0.5,0.5,0.05,0.1,0.1
+3.0,0.15,0.16,0.06,0.11,0.13,0.22,0.12,0.2,0.13,0.12,0.05,0.09,0.13,0.11,0.15,0.17,0.19,0.18,0.19,0.37,0.11,0.28,0.34,0.18,0.29,0.17,0.25,0.73,0.75,0.75,0.75,0.75,0.75,0.75,0.06,0.14,0.14
+4.0,0.19,0.18,0.07,0.13,0.16,0.28,0.13,0.25,0.14,0.14,0.05,0.09,0.14,0.12,0.19,0.22,0.22,0.22,0.22,0.43,0.12,0.3,0.38,0.2,0.34,0.19,0.31,0.78,1.0,1.0,1.0,1.0,1.0,1.0,0.07,0.15,0.15
+5.0,0.25,0.2,0.08,0.16,0.19,0.35,0.15,0.3,0.16,0.16,0.05,0.1,0.14,0.15,0.22,0.26,0.25,0.26,0.25,0.47,0.12,0.31,0.43,0.22,0.39,0.22,0.36,0.79,1.0,1.0,1.0,1.0,1.0,1.0,0.09,0.15,0.15
+6.0,0.3,0.23,0.1,0.19,0.22,0.38,0.17,0.35,0.17,0.18,0.06,0.11,0.15,0.17,0.26,0.3,0.28,0.34,0.28,0.5,0.13,0.4,0.5,0.24,0.44,0.24,0.38,0.81,1.0,1.0,1.0,1.0,1.0,1.0,0.12,0.16,0.16
+7.0,0.35,0.26,0.13,0.22,0.25,0.41,0.19,0.4,0.18,0.2,0.08,0.13,0.17,0.2,0.3,0.32,0.3,0.41,0.3,0.54,0.14,0.43,0.5,0.26,0.5,0.25,0.41,0.82,1.0,1.0,1.0,1.0,1.0,1.0,0.14,0.18,0.18
+8.0,0.41,0.3,0.17,0.25,0.28,0.44,0.22,0.43,0.2,0.22,0.1,0.15,0.19,0.23,0.35,0.35,0.33,0.42,0.33,0.57,0.14,0.43,0.54,0.3,0.55,0.3,0.44,0.83,1.0,1.0,1.0,1.0,1.0,1.0,0.18,0.2,0.2
+9.0,0.46,0.34,0.21,0.29,0.32,0.47,0.24,0.47,0.22,0.26,0.12,0.17,0.22,0.27,0.39,0.37,0.34,0.42,0.34,0.61,0.15,0.45,0.55,0.34,0.57,0.35,0.48,0.84,1.0,1.0,1.0,1.0,1.0,1.0,0.21,0.23,0.23
+10.0,0.51,0.38,0.25,0.32,0.35,0.5,0.28,0.5,0.24,0.29,0.15,0.2,0.26,0.31,0.42,0.39,0.36,0.45,0.36,0.63,0.17,0.46,0.55,0.38,0.59,0.38,0.5,0.85,1.0,1.0,1.0,1.0,1.0,1.0,0.26,0.26,0.26
+11.0,0.57,0.42,0.3,0.37,0.39,0.54,0.31,0.53,0.27,0.33,0.2,0.24,0.31,0.35,0.48,0.43,0.39,0.47,0.39,0.64,0.19,0.47,0.57,0.39,0.61,0.39,0.52,0.86,1.0,1.0,1.0,1.0,1.0,1.0,0.31,0.3,0.3
+12.0,0.63,0.47,0.35,0.41,0.43,0.57,0.34,0.55,0.3,0.37,0.24,0.28,0.37,0.4,0.5,0.46,0.4,0.49,0.4,0.65,0.24,0.47,0.58,0.4,0.63,0.4,0.54,0.88,1.0,1.0,1.0,1.0,1.0,1.0,0.36,0.34,0.34
+13.0,0.7,0.51,0.41,0.45,0.47,0.59,0.37,0.57,0.34,0.41,0.29,0.33,0.44,0.44,0.51,0.48,0.42,0.5,0.42,0.67,0.3,0.49,0.6,0.42,0.65,0.42,0.56,0.89,1.0,1.0,1.0,1.0,1.0,1.0,0.41,0.38,0.38
+14.0,0.75,0.55,0.47,0.49,0.5,0.62,0.4,0.6,0.37,0.45,0.35,0.39,0.51,0.48,0.53,0.5,0.42,0.5,0.42,0.68,0.38,0.5,0.62,0.43,0.66,0.43,0.57,0.9,1.0,1.0,1.0,1.0,1.0,1.0,0.46,0.42,0.42
+15.0,0.79,0.58,0.52,0.52,0.54,0.66,0.44,0.6,0.41,0.5,0.42,0.45,0.59,0.52,0.54,0.51,0.43,0.5,0.43,0.69,0.45,0.5,0.63,0.44,0.68,0.44,0.59,0.91,1.0,1.0,1.0,1.0,1.0,1.0,0.5,0.47,0.47
+16.0,0.82,0.61,0.58,0.55,0.57,0.68,0.48,0.6,0.44,0.53,0.49,0.52,0.65,0.56,0.55,0.54,0.43,0.5,0.43,0.7,0.52,0.5,0.65,0.45,0.69,0.45,0.61,0.92,1.0,1.0,1.0,1.0,1.0,1.0,0.54,0.52,0.52
+17.0,0.84,0.64,0.65,0.58,0.61,0.7,0.51,0.6,0.48,0.57,0.56,0.59,0.7,0.6,0.55,0.55,0.44,0.5,0.44,0.71,0.58,0.51,0.67,0.47,0.71,0.47,0.63,0.94,1.0,1.0,1.0,1.0,1.0,1.0,0.58,0.57,0.57
+18.0,0.87,0.67,0.71,0.61,0.64,0.72,0.55,0.6,0.51,0.6,0.62,0.64,0.74,0.64,0.56,0.57,0.44,0.5,0.44,0.72,0.64,0.51,0.69,0.48,0.72,0.48,0.65,0.95,1.0,1.0,1.0,1.0,1.0,1.0,0.62,0.62,0.62
+19.0,0.89,0.69,0.76,0.63,0.68,0.74,0.59,0.6,0.54,0.63,0.68,0.69,0.79,0.68,0.56,0.59,0.44,0.5,0.44,0.73,0.69,0.52,0.7,0.49,0.74,0.49,0.67,0.96,1.0,1.0,1.0,1.0,1.0,1.0,0.66,0.67,0.67
+20.0,0.9,0.71,0.81,0.66,0.71,0.76,0.63,0.6,0.56,0.66,0.74,0.74,0.83,0.72,0.57,0.6,0.44,0.5,0.44,0.74,0.74,0.52,0.72,0.5,0.75,0.5,0.69,0.97,1.0,1.0,1.0,1.0,1.0,1.0,0.7,0.72,0.72
+21.0,0.92,0.74,0.86,0.68,0.75,0.77,0.67,0.6,0.59,0.69,0.8,0.79,0.87,0.76,0.57,0.62,0.44,0.5,0.44,0.75,0.78,0.53,0.74,0.52,0.77,0.52,0.71,0.98,1.0,1.0,1.0,1.0,1.0,1.0,0.74,0.77,0.77
+22.0,0.93,0.76,0.91,0.7,0.78,0.78,0.71,0.6,0.61,0.73,0.86,0.84,0.91,0.8,0.57,0.63,0.45,0.5,0.45,0.76,0.82,0.53,0.76,0.53,0.79,0.53,0.73,0.99,1.0,1.0,1.0,1.0,1.0,1.0,0.78,0.82,0.82
+23.0,0.95,0.78,0.96,0.71,0.82,0.79,0.75,0.6,0.64,0.76,0.92,0.89,0.95,0.84,0.58,0.65,0.45,0.5,0.45,0.76,0.85,0.54,0.77,0.54,0.8,0.54,0.75,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.82,0.87,0.87
+24.0,0.96,0.8,1.0,0.73,0.85,0.8,0.79,0.6,0.66,0.78,0.98,0.94,0.98,0.88,0.58,0.66,0.45,0.5,0.45,0.77,0.88,0.54,0.79,0.56,0.82,0.56,0.77,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.86,0.92,0.92
diff --git a/examples/data/setup_new_composite_area/test_read/vulnerability/vulnerability_identifiers.csv b/examples/data/setup_new_composite_area/test_read/vulnerability/vulnerability_identifiers.csv
new file mode 100644
index 00000000..7d7fbb72
--- /dev/null
+++ b/examples/data/setup_new_composite_area/test_read/vulnerability/vulnerability_identifiers.csv
@@ -0,0 +1,38 @@
+,Name,Link,BldgDmgFnID,Occupancy,Source,Description,full_identifier
+0,AGR1,AGR1,616,AGR1 ,USACE - Galveston,"Average Agriculture, structure","616AGR1USACE-GalvestonAverageAgriculture,structure"
+1,COM1,COM1,217,COM1 ,USACE - Galveston,"Average Retail, Structure","217COM1USACE-GalvestonAverageRetail,Structure"
+2,COM10,COM10,543,COM10,USACE - Galveston,"Garage, structure","543COM10USACE-GalvestonGarage,structure"
+3,COM2,COM2,341,COM2 ,USACE - Galveston,"Average wholesale, Structure","341COM2USACE-GalvestonAveragewholesale,Structure"
+4,COM3,COM3,375,COM3 ,USACE - Galveston,"Average Personal & Repair Services, Structure","375COM3USACE-GalvestonAveragePersonal&RepairServices,Structure"
+5,COM4,COM4,431,COM4 ,USACE - Galveston,"Average Prof/Tech Services, Structure","431COM4USACE-GalvestonAverageProf/TechServices,Structure"
+6,COM5,COM5,467,COM5 ,USACE - Galveston,"Bank, Structure","467COM5USACE-GalvestonBank,Structure"
+7,COM6,COM6,474,COM6 ,USACE - Galveston,"Hospital, Structure","474COM6USACE-GalvestonHospital,Structure"
+8,COM7,COM7,475,COM7 ,USACE - Galveston,"Average Medical Office, Structure","475COM7USACE-GalvestonAverageMedicalOffice,Structure"
+9,COM8,COM8,493,COM8 ,USACE - Galveston,"Average Entertainment/Recreation, Structure","493COM8USACE-GalvestonAverageEntertainment/Recreation,Structure"
+10,COM9,COM9,532,COM9 ,USACE - Galveston,"Average theatre, Structure","532COM9USACE-GalvestonAveragetheatre,Structure"
+11,EDU1,EDU1,643,EDU1 ,USACE - Galveston,"Average school, structure","643EDU1USACE-GalvestonAverageschool,structure"
+12,GOV1,GOV1,631,GOV1 ,USACE - Galveston,"Average government services, structure","631GOV1USACE-GalvestonAveragegovernmentservices,structure"
+13,GOV2,GOV2,640,GOV2 ,USACE - Galveston,"Average emergency response, Structure","640GOV2USACE-GalvestonAverageemergencyresponse,Structure"
+14,IND1,IND1,545,IND1 ,USACE - Galveston,"Average heavy industrial, Structure","545IND1USACE-GalvestonAverageheavyindustrial,Structure"
+15,IND2,IND2,559,IND2 ,USACE - Galveston,"Average light industrial, structure","559IND2USACE-GalvestonAveragelightindustrial,structure"
+16,IND3,IND3,575,IND3 ,USACE - Galveston,"Average Food/Drug/Chem, Structure","575IND3USACE-GalvestonAverageFood/Drug/Chem,Structure"
+17,IND4,IND4,586,IND4 ,USACE - Galveston,"Average Metals/Minerals processing, structure","586IND4USACE-GalvestonAverageMetals/Mineralsprocessing,structure"
+18,IND5,IND5,591,IND5 ,USACE - Galveston,"Average High Technology, structure","591IND5USACE-GalvestonAverageHighTechnology,structure"
+19,IND6,IND6,592,IND6 ,USACE - Galveston,"Average Construction, structure","592IND6USACE-GalvestonAverageConstruction,structure"
+20,REL1,REL1,624,REL1 ,USACE - Galveston,"Church, structure","624REL1USACE-GalvestonChurch,structure"
+21,RES1-1SNB,RES1-1SNB,105,RES1 ,FIA,"one floor, no basement, Structure, A-Zone","105RES1FIAonefloor,nobasement,Structure,A-Zone"
+22,RES1-1SWB,RES1-1SWB,106,RES1 ,FIA (MOD.),"one floor, w/ basement, Structure, A-Zone","106RES1FIA(MOD.)onefloor,w/basement,Structure,A-Zone"
+23,RES1-2SNB,RES1-2SNB,107,RES1 ,FIA,"two floors, no basement, Structure, A-Zone","107RES1FIAtwofloors,nobasement,Structure,A-Zone"
+24,RES1-2SWB,RES1-2SWB,108,RES1 ,FIA (MOD.),"two floors, w/ basement, Structure, A-Zone","108RES1FIA(MOD.)twofloors,w/basement,Structure,A-Zone"
+25,RES1-3SNB,RES1-3SNB,109,RES1 ,FIA,"three or more floors, no basement, Structure, A-Zone","109RES1FIAthreeormorefloors,nobasement,Structure,A-Zone"
+26,RES1-3SWB,RES1-3SWB,110,RES1 ,FIA (MOD.),"three or more floors, w/ basement, Structure, A-Zone","110RES1FIA(MOD.)threeormorefloors,w/basement,Structure,A-Zone"
+27,RES2,RES2,189,RES2 ,FIA,"Mobile home, structure, A-Zone","189RES2FIAMobilehome,structure,A-Zone"
+28,RES3A,RES3A,659,RES3A,BCAR - Jan 2011,"1to2 Stories, slab - no basement, Coastal A or V Zone","659RES3ABCAR-Jan20111to2Stories,slab-nobasement,CoastalAorVZone"
+29,RES3B,RES3B,660,RES3B,BCAR - Jan 2011,"1to2 Stories, slab - no basement, Coastal A or V Zone","660RES3BBCAR-Jan20111to2Stories,slab-nobasement,CoastalAorVZone"
+30,RES3C,RES3C,660,RES3B,BCAR - Jan 2011,"1to2 Stories, slab - no basement, Coastal A or V Zone","660RES3BBCAR-Jan20111to2Stories,slab-nobasement,CoastalAorVZone"
+31,RES3D,RES3D,660,RES3B,BCAR - Jan 2011,"1to2 Stories, slab - no basement, Coastal A or V Zone","660RES3BBCAR-Jan20111to2Stories,slab-nobasement,CoastalAorVZone"
+32,RES3E,RES3E,660,RES3B,BCAR - Jan 2011,"1to2 Stories, slab - no basement, Coastal A or V Zone","660RES3BBCAR-Jan20111to2Stories,slab-nobasement,CoastalAorVZone"
+33,RES3F,RES3F,660,RES3B,BCAR - Jan 2011,"1to2 Stories, slab - no basement, Coastal A or V Zone","660RES3BBCAR-Jan20111to2Stories,slab-nobasement,CoastalAorVZone"
+34,RES4,RES4,209,RES4 ,USACE - Galveston,"Average Hotel & Motel, structure","209RES4USACE-GalvestonAverageHotel&Motel,structure"
+35,RES5,RES5,214,RES5 ,USACE - Galveston,"Average institutional dormitory, structure","214RES5USACE-GalvestonAverageinstitutionaldormitory,structure"
+36,RES6,RES6,215,RES6 ,USACE - Galveston,"Nursing Home, structure","215RES6USACE-GalvestonNursingHome,structure"
diff --git a/examples/data/setup_new_composite_area/test_setup_new_composite_area_datum/exposure/building_points.gpkg b/examples/data/setup_new_composite_area/test_setup_new_composite_area_datum/exposure/building_points.gpkg
new file mode 100644
index 00000000..7d434010
Binary files /dev/null and b/examples/data/setup_new_composite_area/test_setup_new_composite_area_datum/exposure/building_points.gpkg differ
diff --git a/examples/data/setup_new_composite_area/test_setup_new_composite_area_datum/exposure/exposure.csv b/examples/data/setup_new_composite_area/test_setup_new_composite_area_datum/exposure/exposure.csv
new file mode 100644
index 00000000..5ce06eaa
--- /dev/null
+++ b/examples/data/setup_new_composite_area/test_setup_new_composite_area_datum/exposure/exposure.csv
@@ -0,0 +1,1998 @@
+Object ID,Object Name,Primary Object Type,Secondary Object Type,Max Potential Damage: Structure,Max Potential Damage: Content,Ground Elevation,X Coordinate,Y Coordinate,Aggregation Label: Census Block,Ground Floor Height,Extraction Method,Damage Function: Structure,Damage Function: Content
+574486973,574486973,RES,RES3A,480189.335,240094.667,7.298348065,-79.93682513,32.77688289,450190000000000.0,1,centroid,RES3A,RES3A
+573783417,573783417,COM,RES4,1177614.2,588807.1,8.381748562,-79.93000008,32.77054524,450190000000000.0,1,centroid,RES4,RES4
+573783433,573783433,COM,RES4,476216.254,238108.127,7.374183635,-79.92968382,32.7712713,450190000000000.0,1,centroid,RES4,RES4
+573785665,573785665,RES,RES1-2SNB,330007.42,165003.71,7.36403129,-79.93179589,32.77336473,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+574346729,574346729,RES,RES3A,472285.148,236142.574,10.90203092,-79.93064525,32.77485433,450190000000000.0,1,centroid,RES3A,RES3A
+574372707,574372707,RES,RES1-3SNB,389363.808,194681.904,6.365223632,-79.93187273,32.77098914,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB
+574372721,574372721,RES,RES3A,503924.421,251962.21,7.12000971,-79.93191719,32.77164194,450190000000000.0,1,centroid,RES3A,RES3A
+574476728,574476728,RES,RES1-1SNB,96964.414,48482.207,5.867157192,-79.93241085,32.76996811,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB
+574476811,574476811,RES,RES1-1SNB,152049.256,76024.6281,5.867157192,-79.93241085,32.76996811,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB
+574486899,574486899,RES,RES3A,480189.335,240094.667,12.33366492,-79.93027436,32.77536076,450190000000000.0,1,centroid,RES3A,RES3A
+574486932,574486932,RES,RES1-1SNB,177705.21,88852.6051,6.460120566,-79.92998998,32.77314466,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB
+573827729,573827729,COM,COM8,37755.4885,37755.4885,11.17279794,-79.93080905,32.77672792,450190000000000.0,1,centroid,COM8,COM8
+574421669,574421669,RES,RES3A,170840.757,85420.3788,11.52017961,-79.92907633,32.7771126,450190000000000.0,1,centroid,RES3A,RES3A
+574488781,574488781,RES,RES1-3SNB,374871.082,187435.541,5.99898202,-79.92578441,32.77724701,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB
+573467745,573467745,RES,RES3A,516183.793,258091.896,5.893531857,-79.92827685,32.77200663,450190000000000.0,1,centroid,RES3A,RES3A
+573779323,573779323,RES,RES1-2SWB,437207.93,218603.965,6.975060909,-79.92874432,32.77330072,450190000000000.0,1,centroid,RES1-2SWB,RES1-2SWB
+573779375,573779375,RES,RES1-1SNB,226202.175,113101.087,12.12123537,-79.92780556,32.7743702,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB
+573779393,573779393,RES,RES3A,200255.163,100127.581,12.16771917,-79.92811631,32.77473003,450190000000000.0,1,centroid,RES3A,RES3A
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+573783443,573783443,RES,RES1-2SNB,311772.137,155886.068,10.10444409,-79.93015806,32.77191319,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+573783445,573783445,RES,RES1-2SNB,680463.744,340231.872,8.827352744,-79.92996727,32.77182933,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+573827136,573827136,COM,COM5,2936504.86,2936504.86,13.54706299,-79.92746902,32.77712292,450190000000000.0,1,centroid,COM5,COM5
+573827731,573827731,COM,COM1,194515.404,194515.404,12.23108548,-79.92959283,32.77679411,450190000000000.0,1,centroid,COM1,COM1
+573827733,573827733,COM,COM3,145886.553,145886.553,12.23108548,-79.92959283,32.77679411,450190000000000.0,1,centroid,COM3,COM3
+573827735,573827735,COM,COM4,81048.0852,81048.0852,12.23108548,-79.92959283,32.77679411,450190000000000.0,1,centroid,COM4,COM4
+573827754,573827754,COM,COM4,95724.6697,95724.6697,11.99108448,-79.93017988,32.77681959,450190000000000.0,1,centroid,COM4,COM4
+574267735,574267735,RES,RES1-1SNB,381021.716,190510.858,12.98996214,-79.92744398,32.77522114,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB
+574267737,574267737,RES,RES1-1SNB,273325.341,136662.67,12.70973817,-79.92811401,32.77515289,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB
+574267739,574267739,RES,RES1-2SNB,413187.234,206593.617,13.5823017,-79.92800828,32.77526518,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+574267749,574267749,RES,RES3A,491247.521,245623.76,13.51820246,-79.92788864,32.77557876,450190000000000.0,1,centroid,RES3A,RES3A
+574267976,574267976,RES,RES1-2SNB,385155.033,192577.516,13.53189744,-79.92794047,32.77603579,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+574268005,574268005,RES,RES1-1SNB,257591.82,128795.91,12.59055234,-79.92772127,32.77604828,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB
+574346733,574346733,RES,RES1-2SNB,345561.158,172780.579,13.16675875,-79.92987341,32.77497559,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+574346737,574346737,COM,COM3,128711.316,128711.316,12.62985934,-79.92934753,32.77496936,450190000000000.0,1,centroid,COM3,COM3
+574346738,574346738,RES,RES1-3SNB,569110.405,284555.202,12.57429248,-79.92997655,32.77497829,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB
+574346740,574346740,RES,RES1-2SNB,272662.957,136331.478,12.62266219,-79.92924438,32.77514479,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+574346744,574346744,PUB,REL1,1281715.19,1281715.19,13.0151455,-79.92929333,32.77533709,450190000000000.0,1,centroid,REL1,REL1
+574346753,574346753,RES,RES1-2SNB,311698.604,155849.302,13.77097465,-79.92981335,32.77577007,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+574372447,574372447,COM,COM1,227020.051,227020.051,12.85938964,-79.92900036,32.77610772,450190000000000.0,1,centroid,COM1,COM1
+574372448,574372448,IND,IND2,134849.922,202274.884,12.85938964,-79.92900036,32.77610772,450190000000000.0,1,centroid,IND2,IND2
+574372473,574372473,COM,COM1,424486.831,424486.831,13.03663915,-79.92881546,32.7764473,450190000000000.0,1,centroid,COM1,COM1
+574372533,574372533,COM,COM7,393804.35,590706.525,11.22623405,-79.93032633,32.7759983,450190000000000.0,1,centroid,COM7,COM7
+574372543,574372543,RES,RES3A,338190.212,169095.106,12.60003902,-79.92938805,32.77623669,450190000000000.0,1,centroid,RES3A,RES3A
+574372567,574372567,COM,COM8,284295.791,284295.791,12.21837608,-79.92958905,32.77638171,450190000000000.0,1,centroid,COM8,COM8
+574421681,574421681,COM,COM8,778149.815,778149.815,13.54457556,-79.92846361,32.77697303,450190000000000.0,1,centroid,COM8,COM8
+574421683,574421683,COM,COM1,360197.146,360197.146,13.54457556,-79.92846361,32.77697303,450190000000000.0,1,centroid,COM1,COM1
+574431298,574431298,COM,COM4,1338267.91,1338267.91,10.1165989,-79.92646291,32.77728981,450190000000000.0,1,centroid,COM4,COM4
+574461065,574461065,RES,RES3A,266985.928,133492.964,12.13528704,-79.9297417,32.77681823,450190000000000.0,1,centroid,RES3A,RES3A
+574461073,574461073,COM,COM4,177990.619,177990.619,12.27511783,-79.92950828,32.77642489,450190000000000.0,1,centroid,COM4,COM4
+574461077,574461077,RES,RES3A,273660.577,136830.288,12.4406709,-79.92903083,32.7763684,450190000000000.0,1,centroid,RES3A,RES3A
+574461079,574461079,COM,COM4,177990.619,177990.619,12.86096032,-79.92860499,32.77648743,450190000000000.0,1,centroid,COM4,COM4
+574488771,574488771,RES,RES1-1SNB,222806.066,111403.033,13.26134397,-79.92896336,32.77597596,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB
+574488793,574488793,RES,RES1-2SNB,216903.661,108451.83,12.29002837,-79.92736548,32.77640732,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+574488804,574488804,RES,RES1-2SNB,292527.098,146263.549,13.21022478,-79.92816916,32.77627743,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+574488806,574488806,RES,RES1-3SNB,358700.469,179350.234,13.36457265,-79.92824222,32.77627542,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB
+574488816,574488816,RES,RES1-1SNB,221789.878,110894.939,6.334944159,-79.92574958,32.77633187,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB
+574488819,574488819,RES,RES1-3SNB,416126.362,208063.181,6.052678219,-79.9267068,32.77616474,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB
+574488830,574488830,RES,RES1-1SNB,182232.731,91116.3657,12.69306217,-79.92898379,32.77518059,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB
+574488834,574488834,RES,RES1-3SNB,384398.576,192199.288,12.90637406,-79.92951787,32.77470617,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB
+574488837,574488837,RES,RES3A,480189.335,240094.667,13.25912718,-79.92891,32.77509288,450190000000000.0,1,centroid,RES3A,RES3A
+574488848,574488848,RES,RES1-2SNB,322415.251,161207.625,13.14323291,-79.92763108,32.77497895,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+574488851,574488851,RES,RES1-2SNB,222897.785,111448.892,10.150554,-79.92671547,32.7755542,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+574488874,574488874,RES,RES3A,480189.335,240094.667,8.900641428,-79.92914469,32.77176507,450190000000000.0,1,centroid,RES3A,RES3A
+574507807,New development area: 574507807,New development area,New development area,42804025.98393736,26468929.704139814,5.35474157333374,,,,2,area,new_composite_area_structure,new_composite_area_content
+574507808,New development area: 574507808,New development area,New development area,34126193.93100962,21102777.308113553,1.2411025762557983,,,,2,area,new_composite_area_structure,new_composite_area_content
+574507809,New development area: 574507809,New development area,New development area,6629114.270542782,4099277.009446545,4.435575485229492,,,,2,area,new_composite_area_structure,new_composite_area_content
diff --git a/examples/data/setup_new_composite_area/test_setup_new_composite_area_datum/settings.toml b/examples/data/setup_new_composite_area/test_setup_new_composite_area_datum/settings.toml
new file mode 100644
index 00000000..c9018a8b
--- /dev/null
+++ b/examples/data/setup_new_composite_area/test_setup_new_composite_area_datum/settings.toml
@@ -0,0 +1,22 @@
+[global]
+crs = "epsg:4326"
+
+[output]
+path = "output"
+
+[output.csv]
+name = "output.csv"
+
+[output.geom]
+name1 = "spatial.gpkg"
+
+[vulnerability]
+file = "./vulnerability/vulnerability_curves.csv"
+scale = 0.1
+
+[exposure.csv]
+file = "./exposure/exposure.csv"
+
+[exposure.geom]
+crs = "EPSG:4326"
+file1 = "./exposure/building_points.gpkg"
diff --git a/examples/data/setup_new_composite_area/test_setup_new_composite_area_datum/vulnerability/vulnerability_curves.csv b/examples/data/setup_new_composite_area/test_setup_new_composite_area_datum/vulnerability/vulnerability_curves.csv
new file mode 100644
index 00000000..8d9e73a3
--- /dev/null
+++ b/examples/data/setup_new_composite_area/test_setup_new_composite_area_datum/vulnerability/vulnerability_curves.csv
@@ -0,0 +1,32 @@
+#UNIT=feet
+#METHOD,max,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean
+water depth [feet],AGR1,COM1,COM10,COM2,COM3,COM4,COM5,COM6,COM7,COM8,COM9,EDU1,GOV1,GOV2,IND1,IND2,IND3,IND4,IND5,IND6,REL1,RES1-1SNB,RES1-1SWB,RES1-2SNB,RES1-2SWB,RES1-3SNB,RES1-3SWB,RES2,RES3A,RES3B,RES3C,RES3D,RES3E,RES3F,RES4,RES5,RES6,new_composite_area_structure,new_composite_area_content
+-4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,0.0,0.04,0.0,0.03,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0025426278836509528,0.0025426278836509528
+-3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,0.0,0.04,0.0,0.03,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0025426278836509528,0.0025426278836509528
+-2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,0.0,0.08,0.0,0.06,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.004663991975927783,0.004663991975927783
+-1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.11,0.0,0.14,0.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.007913741223671014,0.007913741223671014
+0.0,0.0,0.01,0.0,0.0,0.0,0.02,0.0,0.0,0.02,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.18,0.17,0.11,0.19,0.05,0.12,0.11,0.12,0.12,0.12,0.12,0.12,0.12,0.0,0.0,0.0,0.10625376128385156,0.10625376128385156
+1.0,0.06,0.09,0.03,0.05,0.09,0.11,0.11,0.0,0.11,0.09,0.02,0.05,0.05,0.07,0.1,0.09,0.13,0.1,0.13,0.22,0.1,0.22,0.21,0.12,0.21,0.08,0.14,0.44,0.25,0.25,0.25,0.25,0.25,0.25,0.03,0.07,0.07,0.16183049147442324,0.16183049147442324
+2.0,0.11,0.14,0.05,0.08,0.12,0.16,0.11,0.0,0.12,0.11,0.04,0.07,0.08,0.1,0.12,0.14,0.14,0.14,0.14,0.31,0.11,0.25,0.29,0.14,0.26,0.12,0.2,0.63,0.5,0.5,0.5,0.5,0.5,0.5,0.05,0.1,0.1,0.24248244734202612,0.24248244734202612
+3.0,0.15,0.16,0.06,0.11,0.13,0.22,0.12,0.2,0.13,0.12,0.05,0.09,0.13,0.11,0.15,0.17,0.19,0.18,0.19,0.37,0.11,0.28,0.34,0.18,0.29,0.17,0.25,0.73,0.75,0.75,0.75,0.75,0.75,0.75,0.06,0.14,0.14,0.32801404212637914,0.32801404212637914
+4.0,0.19,0.18,0.07,0.13,0.16,0.28,0.13,0.25,0.14,0.14,0.05,0.09,0.14,0.12,0.19,0.22,0.22,0.22,0.22,0.43,0.12,0.3,0.38,0.2,0.34,0.19,0.31,0.78,1.0,1.0,1.0,1.0,1.0,1.0,0.07,0.15,0.15,0.40343530591775323,0.40343530591775323
+5.0,0.25,0.2,0.08,0.16,0.19,0.35,0.15,0.3,0.16,0.16,0.05,0.1,0.14,0.15,0.22,0.26,0.25,0.26,0.25,0.47,0.12,0.31,0.43,0.22,0.39,0.22,0.36,0.79,1.0,1.0,1.0,1.0,1.0,1.0,0.09,0.15,0.15,0.422753259779338,0.422753259779338
+6.0,0.3,0.23,0.1,0.19,0.22,0.38,0.17,0.35,0.17,0.18,0.06,0.11,0.15,0.17,0.26,0.3,0.28,0.34,0.28,0.5,0.13,0.4,0.5,0.24,0.44,0.24,0.38,0.81,1.0,1.0,1.0,1.0,1.0,1.0,0.12,0.16,0.16,0.4530842527582749,0.4530842527582749
+7.0,0.35,0.26,0.13,0.22,0.25,0.41,0.19,0.4,0.18,0.2,0.08,0.13,0.17,0.2,0.3,0.32,0.3,0.41,0.3,0.54,0.14,0.43,0.5,0.26,0.5,0.25,0.41,0.82,1.0,1.0,1.0,1.0,1.0,1.0,0.14,0.18,0.18,0.47121364092276835,0.47121364092276835
+8.0,0.41,0.3,0.17,0.25,0.28,0.44,0.22,0.43,0.2,0.22,0.1,0.15,0.19,0.23,0.35,0.35,0.33,0.42,0.33,0.57,0.14,0.43,0.54,0.3,0.55,0.3,0.44,0.83,1.0,1.0,1.0,1.0,1.0,1.0,0.18,0.2,0.2,0.49450852557673025,0.49450852557673025
+9.0,0.46,0.34,0.21,0.29,0.32,0.47,0.24,0.47,0.22,0.26,0.12,0.17,0.22,0.27,0.39,0.37,0.34,0.42,0.34,0.61,0.15,0.45,0.55,0.34,0.57,0.35,0.48,0.84,1.0,1.0,1.0,1.0,1.0,1.0,0.21,0.23,0.23,0.5212788365095286,0.5212788365095286
+10.0,0.51,0.38,0.25,0.32,0.35,0.5,0.28,0.5,0.24,0.29,0.15,0.2,0.26,0.31,0.42,0.39,0.36,0.45,0.36,0.63,0.17,0.46,0.55,0.38,0.59,0.38,0.5,0.85,1.0,1.0,1.0,1.0,1.0,1.0,0.26,0.26,0.26,0.5430892678034104,0.5430892678034104
+11.0,0.57,0.42,0.3,0.37,0.39,0.54,0.31,0.53,0.27,0.33,0.2,0.24,0.31,0.35,0.48,0.43,0.39,0.47,0.39,0.64,0.19,0.47,0.57,0.39,0.61,0.39,0.52,0.86,1.0,1.0,1.0,1.0,1.0,1.0,0.31,0.3,0.3,0.5556469408224675,0.5556469408224675
+12.0,0.63,0.47,0.35,0.41,0.43,0.57,0.34,0.55,0.3,0.37,0.24,0.28,0.37,0.4,0.5,0.46,0.4,0.49,0.4,0.65,0.24,0.47,0.58,0.4,0.63,0.4,0.54,0.88,1.0,1.0,1.0,1.0,1.0,1.0,0.36,0.34,0.34,0.5663691073219658,0.5663691073219658
+13.0,0.7,0.51,0.41,0.45,0.47,0.59,0.37,0.57,0.34,0.41,0.29,0.33,0.44,0.44,0.51,0.48,0.42,0.5,0.42,0.67,0.3,0.49,0.6,0.42,0.65,0.42,0.56,0.89,1.0,1.0,1.0,1.0,1.0,1.0,0.41,0.38,0.38,0.5843029087261786,0.5843029087261786
+14.0,0.75,0.55,0.47,0.49,0.5,0.62,0.4,0.6,0.37,0.45,0.35,0.39,0.51,0.48,0.53,0.5,0.42,0.5,0.42,0.68,0.38,0.5,0.62,0.43,0.66,0.43,0.57,0.9,1.0,1.0,1.0,1.0,1.0,1.0,0.46,0.42,0.42,0.5964844533600802,0.5964844533600802
+15.0,0.79,0.58,0.52,0.52,0.54,0.66,0.44,0.6,0.41,0.5,0.42,0.45,0.59,0.52,0.54,0.51,0.43,0.5,0.43,0.69,0.45,0.5,0.63,0.44,0.68,0.44,0.59,0.91,1.0,1.0,1.0,1.0,1.0,1.0,0.5,0.47,0.47,0.6077281845536611,0.6077281845536611
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diff --git a/examples/data/setup_new_composite_area/test_setup_new_composite_area_elevation/exposure/building_points.gpkg b/examples/data/setup_new_composite_area/test_setup_new_composite_area_elevation/exposure/building_points.gpkg
new file mode 100644
index 00000000..0f0045de
Binary files /dev/null and b/examples/data/setup_new_composite_area/test_setup_new_composite_area_elevation/exposure/building_points.gpkg differ
diff --git a/examples/data/setup_new_composite_area/test_setup_new_composite_area_elevation/exposure/exposure.csv b/examples/data/setup_new_composite_area/test_setup_new_composite_area_elevation/exposure/exposure.csv
new file mode 100644
index 00000000..5ce06eaa
--- /dev/null
+++ b/examples/data/setup_new_composite_area/test_setup_new_composite_area_elevation/exposure/exposure.csv
@@ -0,0 +1,1998 @@
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+574486926,574486926,RES,RES1-2SNB,230007.096,115003.548,6.266001842,-79.93318093,32.770926,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+574486928,574486928,RES,RES1-1SNB,195320.206,97660.1031,6.266001842,-79.93318093,32.770926,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB
+574486930,574486930,RES,RES1-3SNB,646413.507,323206.753,6.34663942,-79.93132077,32.77092295,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB
+574486966,574486966,RES,RES1-1SNB,123142.068,61571.034,7.260584375,-79.93663915,32.77690016,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB
+574487084,574487084,RES,RES1-2SNB,279153.339,139576.669,9.454224669,-79.93460219,32.77706446,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+574487093,574487093,RES,RES1-2SNB,222885.093,111442.546,9.54534625,-79.93544376,32.7764595,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+574487094,574487094,RES,RES1-2SNB,187761.29,93880.6453,8.094838231,-79.93617751,32.7761887,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+573467723,573467723,COM,RES4,146911.004,73455.5023,7.205438344,-79.92839527,32.77175843,450190000000000.0,1,centroid,RES4,RES4
+573467726,573467726,RES,RES1-2SNB,421375.203,210687.601,7.971950971,-79.92922584,32.77068669,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+573467727,573467727,COM,RES4,682840.128,341420.064,5.998637455,-79.92868859,32.77061156,450190000000000.0,1,centroid,RES4,RES4
+573467728,573467728,COM,RES4,447990.428,223995.214,6.607965902,-79.92929336,32.77085682,450190000000000.0,1,centroid,RES4,RES4
+573467729,573467729,RES,RES3B,639434.436,319717.218,6.184400969,-79.92860345,32.77085682,450190000000000.0,1,centroid,RES3B,RES3B
+573467730,573467730,RES,RES1-3SNB,950659.269,475329.634,5.694120288,-79.928633,32.77105956,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB
+573467731,573467731,RES,RES3A,424021.723,212010.861,7.376430152,-79.92924984,32.77110611,450190000000000.0,1,centroid,RES3A,RES3A
+573467735,573467735,RES,RES1-2SNB,546238.414,273119.207,6.439359845,-79.92905401,32.77139283,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+573467737,573467737,RES,RES1-3SNB,569110.405,284555.202,6.735533926,-79.92926183,32.7714921,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB
+573467741,573467741,RES,RES1-3SNB,466979.502,233489.751,8.43485771,-79.92890022,32.77192889,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB
+573467746,573467746,COM,COM4,405574.23,405574.23,5.496637656,-79.92805491,32.77199384,450190000000000.0,1,centroid,COM4,COM4
+573467747,573467747,RES,RES1-3SNB,486027.03,243013.515,6.769053327,-79.929019,32.770324,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB
+573779311,573779311,RES,RES1-2SNB,217879.449,108939.724,5.791196858,-79.92878222,32.77309016,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+573779319,573779319,RES,RES3A,471108.706,235554.353,7.738122412,-79.92765306,32.77323472,450190000000000.0,1,centroid,RES3A,RES3A
+573779335,573779335,RES,RES1-1SNB,256055.142,128027.571,9.352340616,-79.92764858,32.77351557,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB
+573779357,573779357,RES,RES1-3SNB,380946.441,190473.22,10.36516436,-79.92840483,32.77411255,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB
+573779369,573779369,RES,RES1-2SNB,374450.35,187225.175,11.93594548,-79.92751869,32.77433957,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+573779385,573779385,RES,RES3A,581713.203,290856.601,12.28355087,-79.92749568,32.77453773,450190000000000.0,1,centroid,RES3A,RES3A
+573779415,573779415,RES,RES1-3SNB,431428.087,215714.043,12.26185305,-79.92743299,32.77502193,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB
+573782417,573782417,RES,RES1-3SNB,363979.656,181989.828,9.372612453,-79.92933255,32.77226794,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB
+573782425,573782425,RES,RES1-2SNB,366466.821,183233.41,9.011924555,-79.92929796,32.77237201,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+573782443,573782443,RES,RES3A,543712.387,271856.193,5.378797627,-79.92831106,32.77264062,450190000000000.0,1,centroid,RES3A,RES3A
+573782449,573782449,RES,RES1-2SNB,472033.925,236016.962,5.179306272,-79.92864422,32.77279569,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+573782516,573782516,RES,RES1-3SNB,451364.729,225682.364,11.94115424,-79.92865918,32.7750572,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB
+573782540,573782540,RES,RES1-2SNB,278796.48,139398.24,12.30676852,-79.9284404,32.77566618,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+573782554,573782554,COM,COM4,426627.449,426627.449,13.15236603,-79.92852646,32.77599619,450190000000000.0,1,centroid,COM4,COM4
+573782586,573782586,RES,RES3A,421325.574,210662.787,8.191982072,-79.93017261,32.77290957,450190000000000.0,1,centroid,RES3A,RES3A
+573783169,573783169,RES,RES3A,512267.747,256133.873,11.97290897,-79.92919073,32.77440531,450190000000000.0,1,centroid,RES3A,RES3A
+573783179,573783179,RES,RES3A,606289.125,303144.562,12.5249974,-79.92928354,32.77452729,450190000000000.0,1,centroid,RES3A,RES3A
+573783187,573783187,RES,RES1-2SNB,386160.703,193080.351,11.5436022,-79.93016895,32.77463032,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+573783189,573783189,COM,COM3,1640307.87,1640307.87,10.78047343,-79.93028267,32.7746644,450190000000000.0,1,centroid,COM3,COM3
+573783199,573783199,RES,RES1-2SNB,551102.006,275551.003,12.75948849,-79.92921208,32.77472151,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+573783443,573783443,RES,RES1-2SNB,311772.137,155886.068,10.10444409,-79.93015806,32.77191319,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+573783445,573783445,RES,RES1-2SNB,680463.744,340231.872,8.827352744,-79.92996727,32.77182933,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+573827136,573827136,COM,COM5,2936504.86,2936504.86,13.54706299,-79.92746902,32.77712292,450190000000000.0,1,centroid,COM5,COM5
+573827731,573827731,COM,COM1,194515.404,194515.404,12.23108548,-79.92959283,32.77679411,450190000000000.0,1,centroid,COM1,COM1
+573827733,573827733,COM,COM3,145886.553,145886.553,12.23108548,-79.92959283,32.77679411,450190000000000.0,1,centroid,COM3,COM3
+573827735,573827735,COM,COM4,81048.0852,81048.0852,12.23108548,-79.92959283,32.77679411,450190000000000.0,1,centroid,COM4,COM4
+573827754,573827754,COM,COM4,95724.6697,95724.6697,11.99108448,-79.93017988,32.77681959,450190000000000.0,1,centroid,COM4,COM4
+574267735,574267735,RES,RES1-1SNB,381021.716,190510.858,12.98996214,-79.92744398,32.77522114,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB
+574267737,574267737,RES,RES1-1SNB,273325.341,136662.67,12.70973817,-79.92811401,32.77515289,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB
+574267739,574267739,RES,RES1-2SNB,413187.234,206593.617,13.5823017,-79.92800828,32.77526518,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+574267749,574267749,RES,RES3A,491247.521,245623.76,13.51820246,-79.92788864,32.77557876,450190000000000.0,1,centroid,RES3A,RES3A
+574267976,574267976,RES,RES1-2SNB,385155.033,192577.516,13.53189744,-79.92794047,32.77603579,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+574268005,574268005,RES,RES1-1SNB,257591.82,128795.91,12.59055234,-79.92772127,32.77604828,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB
+574346733,574346733,RES,RES1-2SNB,345561.158,172780.579,13.16675875,-79.92987341,32.77497559,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+574346737,574346737,COM,COM3,128711.316,128711.316,12.62985934,-79.92934753,32.77496936,450190000000000.0,1,centroid,COM3,COM3
+574346738,574346738,RES,RES1-3SNB,569110.405,284555.202,12.57429248,-79.92997655,32.77497829,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB
+574346740,574346740,RES,RES1-2SNB,272662.957,136331.478,12.62266219,-79.92924438,32.77514479,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+574346744,574346744,PUB,REL1,1281715.19,1281715.19,13.0151455,-79.92929333,32.77533709,450190000000000.0,1,centroid,REL1,REL1
+574346753,574346753,RES,RES1-2SNB,311698.604,155849.302,13.77097465,-79.92981335,32.77577007,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+574372447,574372447,COM,COM1,227020.051,227020.051,12.85938964,-79.92900036,32.77610772,450190000000000.0,1,centroid,COM1,COM1
+574372448,574372448,IND,IND2,134849.922,202274.884,12.85938964,-79.92900036,32.77610772,450190000000000.0,1,centroid,IND2,IND2
+574372473,574372473,COM,COM1,424486.831,424486.831,13.03663915,-79.92881546,32.7764473,450190000000000.0,1,centroid,COM1,COM1
+574372533,574372533,COM,COM7,393804.35,590706.525,11.22623405,-79.93032633,32.7759983,450190000000000.0,1,centroid,COM7,COM7
+574372543,574372543,RES,RES3A,338190.212,169095.106,12.60003902,-79.92938805,32.77623669,450190000000000.0,1,centroid,RES3A,RES3A
+574372567,574372567,COM,COM8,284295.791,284295.791,12.21837608,-79.92958905,32.77638171,450190000000000.0,1,centroid,COM8,COM8
+574421681,574421681,COM,COM8,778149.815,778149.815,13.54457556,-79.92846361,32.77697303,450190000000000.0,1,centroid,COM8,COM8
+574421683,574421683,COM,COM1,360197.146,360197.146,13.54457556,-79.92846361,32.77697303,450190000000000.0,1,centroid,COM1,COM1
+574431298,574431298,COM,COM4,1338267.91,1338267.91,10.1165989,-79.92646291,32.77728981,450190000000000.0,1,centroid,COM4,COM4
+574461065,574461065,RES,RES3A,266985.928,133492.964,12.13528704,-79.9297417,32.77681823,450190000000000.0,1,centroid,RES3A,RES3A
+574461073,574461073,COM,COM4,177990.619,177990.619,12.27511783,-79.92950828,32.77642489,450190000000000.0,1,centroid,COM4,COM4
+574461077,574461077,RES,RES3A,273660.577,136830.288,12.4406709,-79.92903083,32.7763684,450190000000000.0,1,centroid,RES3A,RES3A
+574461079,574461079,COM,COM4,177990.619,177990.619,12.86096032,-79.92860499,32.77648743,450190000000000.0,1,centroid,COM4,COM4
+574488771,574488771,RES,RES1-1SNB,222806.066,111403.033,13.26134397,-79.92896336,32.77597596,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB
+574488793,574488793,RES,RES1-2SNB,216903.661,108451.83,12.29002837,-79.92736548,32.77640732,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+574488804,574488804,RES,RES1-2SNB,292527.098,146263.549,13.21022478,-79.92816916,32.77627743,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+574488806,574488806,RES,RES1-3SNB,358700.469,179350.234,13.36457265,-79.92824222,32.77627542,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB
+574488816,574488816,RES,RES1-1SNB,221789.878,110894.939,6.334944159,-79.92574958,32.77633187,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB
+574488819,574488819,RES,RES1-3SNB,416126.362,208063.181,6.052678219,-79.9267068,32.77616474,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB
+574488830,574488830,RES,RES1-1SNB,182232.731,91116.3657,12.69306217,-79.92898379,32.77518059,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB
+574488834,574488834,RES,RES1-3SNB,384398.576,192199.288,12.90637406,-79.92951787,32.77470617,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB
+574488837,574488837,RES,RES3A,480189.335,240094.667,13.25912718,-79.92891,32.77509288,450190000000000.0,1,centroid,RES3A,RES3A
+574488848,574488848,RES,RES1-2SNB,322415.251,161207.625,13.14323291,-79.92763108,32.77497895,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+574488851,574488851,RES,RES1-2SNB,222897.785,111448.892,10.150554,-79.92671547,32.7755542,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+574488874,574488874,RES,RES3A,480189.335,240094.667,8.900641428,-79.92914469,32.77176507,450190000000000.0,1,centroid,RES3A,RES3A
+574507807,New development area: 574507807,New development area,New development area,42804025.98393736,26468929.704139814,5.35474157333374,,,,2,area,new_composite_area_structure,new_composite_area_content
+574507808,New development area: 574507808,New development area,New development area,34126193.93100962,21102777.308113553,1.2411025762557983,,,,2,area,new_composite_area_structure,new_composite_area_content
+574507809,New development area: 574507809,New development area,New development area,6629114.270542782,4099277.009446545,4.435575485229492,,,,2,area,new_composite_area_structure,new_composite_area_content
diff --git a/examples/data/setup_new_composite_area/test_setup_new_composite_area_elevation/settings.toml b/examples/data/setup_new_composite_area/test_setup_new_composite_area_elevation/settings.toml
new file mode 100644
index 00000000..c9018a8b
--- /dev/null
+++ b/examples/data/setup_new_composite_area/test_setup_new_composite_area_elevation/settings.toml
@@ -0,0 +1,22 @@
+[global]
+crs = "epsg:4326"
+
+[output]
+path = "output"
+
+[output.csv]
+name = "output.csv"
+
+[output.geom]
+name1 = "spatial.gpkg"
+
+[vulnerability]
+file = "./vulnerability/vulnerability_curves.csv"
+scale = 0.1
+
+[exposure.csv]
+file = "./exposure/exposure.csv"
+
+[exposure.geom]
+crs = "EPSG:4326"
+file1 = "./exposure/building_points.gpkg"
diff --git a/examples/data/setup_new_composite_area/test_setup_new_composite_area_elevation/vulnerability/vulnerability_curves.csv b/examples/data/setup_new_composite_area/test_setup_new_composite_area_elevation/vulnerability/vulnerability_curves.csv
new file mode 100644
index 00000000..8d9e73a3
--- /dev/null
+++ b/examples/data/setup_new_composite_area/test_setup_new_composite_area_elevation/vulnerability/vulnerability_curves.csv
@@ -0,0 +1,32 @@
+#UNIT=feet
+#METHOD,max,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean
+water depth [feet],AGR1,COM1,COM10,COM2,COM3,COM4,COM5,COM6,COM7,COM8,COM9,EDU1,GOV1,GOV2,IND1,IND2,IND3,IND4,IND5,IND6,REL1,RES1-1SNB,RES1-1SWB,RES1-2SNB,RES1-2SWB,RES1-3SNB,RES1-3SWB,RES2,RES3A,RES3B,RES3C,RES3D,RES3E,RES3F,RES4,RES5,RES6,new_composite_area_structure,new_composite_area_content
+-4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,0.0,0.04,0.0,0.03,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0025426278836509528,0.0025426278836509528
+-3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,0.0,0.04,0.0,0.03,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0025426278836509528,0.0025426278836509528
+-2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,0.0,0.08,0.0,0.06,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.004663991975927783,0.004663991975927783
+-1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.11,0.0,0.14,0.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.007913741223671014,0.007913741223671014
+0.0,0.0,0.01,0.0,0.0,0.0,0.02,0.0,0.0,0.02,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.18,0.17,0.11,0.19,0.05,0.12,0.11,0.12,0.12,0.12,0.12,0.12,0.12,0.0,0.0,0.0,0.10625376128385156,0.10625376128385156
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diff --git a/examples/data/setup_new_composite_area/test_setup_new_composite_area_geom/exposure/building_points.gpkg b/examples/data/setup_new_composite_area/test_setup_new_composite_area_geom/exposure/building_points.gpkg
new file mode 100644
index 00000000..b697b458
Binary files /dev/null and b/examples/data/setup_new_composite_area/test_setup_new_composite_area_geom/exposure/building_points.gpkg differ
diff --git a/examples/data/setup_new_composite_area/test_setup_new_composite_area_geom/exposure/exposure.csv b/examples/data/setup_new_composite_area/test_setup_new_composite_area_geom/exposure/exposure.csv
new file mode 100644
index 00000000..fec56a05
--- /dev/null
+++ b/examples/data/setup_new_composite_area/test_setup_new_composite_area_geom/exposure/exposure.csv
@@ -0,0 +1,1998 @@
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+573467729,573467729,RES,RES3B,639434.436,319717.218,6.184400969,-79.92860345,32.77085682,450190000000000.0,1.0,centroid,RES3B,RES3B
+573467730,573467730,RES,RES1-3SNB,950659.269,475329.634,5.694120288,-79.928633,32.77105956,450190000000000.0,1.0,centroid,RES1-3SNB,RES1-3SNB
+573467731,573467731,RES,RES3A,424021.723,212010.861,7.376430152,-79.92924984,32.77110611,450190000000000.0,1.0,centroid,RES3A,RES3A
+573467735,573467735,RES,RES1-2SNB,546238.414,273119.207,6.439359845,-79.92905401,32.77139283,450190000000000.0,1.0,centroid,RES1-2SNB,RES1-2SNB
+573467737,573467737,RES,RES1-3SNB,569110.405,284555.202,6.735533926,-79.92926183,32.7714921,450190000000000.0,1.0,centroid,RES1-3SNB,RES1-3SNB
+573467741,573467741,RES,RES1-3SNB,466979.502,233489.751,8.43485771,-79.92890022,32.77192889,450190000000000.0,1.0,centroid,RES1-3SNB,RES1-3SNB
+573467746,573467746,COM,COM4,405574.23,405574.23,5.496637656,-79.92805491,32.77199384,450190000000000.0,1.0,centroid,COM4,COM4
+573467747,573467747,RES,RES1-3SNB,486027.03,243013.515,6.769053327,-79.929019,32.770324,450190000000000.0,1.0,centroid,RES1-3SNB,RES1-3SNB
+573779311,573779311,RES,RES1-2SNB,217879.449,108939.724,5.791196858,-79.92878222,32.77309016,450190000000000.0,1.0,centroid,RES1-2SNB,RES1-2SNB
+573779319,573779319,RES,RES3A,471108.706,235554.353,7.738122412,-79.92765306,32.77323472,450190000000000.0,1.0,centroid,RES3A,RES3A
+573779335,573779335,RES,RES1-1SNB,256055.142,128027.571,9.352340616,-79.92764858,32.77351557,450190000000000.0,1.0,centroid,RES1-1SNB,RES1-1SNB
+573779357,573779357,RES,RES1-3SNB,380946.441,190473.22,10.36516436,-79.92840483,32.77411255,450190000000000.0,1.0,centroid,RES1-3SNB,RES1-3SNB
+573779369,573779369,RES,RES1-2SNB,374450.35,187225.175,11.93594548,-79.92751869,32.77433957,450190000000000.0,1.0,centroid,RES1-2SNB,RES1-2SNB
+573779385,573779385,RES,RES3A,581713.203,290856.601,12.28355087,-79.92749568,32.77453773,450190000000000.0,1.0,centroid,RES3A,RES3A
+573779415,573779415,RES,RES1-3SNB,431428.087,215714.043,12.26185305,-79.92743299,32.77502193,450190000000000.0,1.0,centroid,RES1-3SNB,RES1-3SNB
+573782417,573782417,RES,RES1-3SNB,363979.656,181989.828,9.372612453,-79.92933255,32.77226794,450190000000000.0,1.0,centroid,RES1-3SNB,RES1-3SNB
+573782425,573782425,RES,RES1-2SNB,366466.821,183233.41,9.011924555,-79.92929796,32.77237201,450190000000000.0,1.0,centroid,RES1-2SNB,RES1-2SNB
+573782443,573782443,RES,RES3A,543712.387,271856.193,5.378797627,-79.92831106,32.77264062,450190000000000.0,1.0,centroid,RES3A,RES3A
+573782449,573782449,RES,RES1-2SNB,472033.925,236016.962,5.179306272,-79.92864422,32.77279569,450190000000000.0,1.0,centroid,RES1-2SNB,RES1-2SNB
+573782516,573782516,RES,RES1-3SNB,451364.729,225682.364,11.94115424,-79.92865918,32.7750572,450190000000000.0,1.0,centroid,RES1-3SNB,RES1-3SNB
+573782540,573782540,RES,RES1-2SNB,278796.48,139398.24,12.30676852,-79.9284404,32.77566618,450190000000000.0,1.0,centroid,RES1-2SNB,RES1-2SNB
+573782554,573782554,COM,COM4,426627.449,426627.449,13.15236603,-79.92852646,32.77599619,450190000000000.0,1.0,centroid,COM4,COM4
+573782586,573782586,RES,RES3A,421325.574,210662.787,8.191982072,-79.93017261,32.77290957,450190000000000.0,1.0,centroid,RES3A,RES3A
+573783169,573783169,RES,RES3A,512267.747,256133.873,11.97290897,-79.92919073,32.77440531,450190000000000.0,1.0,centroid,RES3A,RES3A
+573783179,573783179,RES,RES3A,606289.125,303144.562,12.5249974,-79.92928354,32.77452729,450190000000000.0,1.0,centroid,RES3A,RES3A
+573783187,573783187,RES,RES1-2SNB,386160.703,193080.351,11.5436022,-79.93016895,32.77463032,450190000000000.0,1.0,centroid,RES1-2SNB,RES1-2SNB
+573783189,573783189,COM,COM3,1640307.87,1640307.87,10.78047343,-79.93028267,32.7746644,450190000000000.0,1.0,centroid,COM3,COM3
+573783199,573783199,RES,RES1-2SNB,551102.006,275551.003,12.75948849,-79.92921208,32.77472151,450190000000000.0,1.0,centroid,RES1-2SNB,RES1-2SNB
+573783443,573783443,RES,RES1-2SNB,311772.137,155886.068,10.10444409,-79.93015806,32.77191319,450190000000000.0,1.0,centroid,RES1-2SNB,RES1-2SNB
+573783445,573783445,RES,RES1-2SNB,680463.744,340231.872,8.827352744,-79.92996727,32.77182933,450190000000000.0,1.0,centroid,RES1-2SNB,RES1-2SNB
+573827136,573827136,COM,COM5,2936504.86,2936504.86,13.54706299,-79.92746902,32.77712292,450190000000000.0,1.0,centroid,COM5,COM5
+573827731,573827731,COM,COM1,194515.404,194515.404,12.23108548,-79.92959283,32.77679411,450190000000000.0,1.0,centroid,COM1,COM1
+573827733,573827733,COM,COM3,145886.553,145886.553,12.23108548,-79.92959283,32.77679411,450190000000000.0,1.0,centroid,COM3,COM3
+573827735,573827735,COM,COM4,81048.0852,81048.0852,12.23108548,-79.92959283,32.77679411,450190000000000.0,1.0,centroid,COM4,COM4
+573827754,573827754,COM,COM4,95724.6697,95724.6697,11.99108448,-79.93017988,32.77681959,450190000000000.0,1.0,centroid,COM4,COM4
+574267735,574267735,RES,RES1-1SNB,381021.716,190510.858,12.98996214,-79.92744398,32.77522114,450190000000000.0,1.0,centroid,RES1-1SNB,RES1-1SNB
+574267737,574267737,RES,RES1-1SNB,273325.341,136662.67,12.70973817,-79.92811401,32.77515289,450190000000000.0,1.0,centroid,RES1-1SNB,RES1-1SNB
+574267739,574267739,RES,RES1-2SNB,413187.234,206593.617,13.5823017,-79.92800828,32.77526518,450190000000000.0,1.0,centroid,RES1-2SNB,RES1-2SNB
+574267749,574267749,RES,RES3A,491247.521,245623.76,13.51820246,-79.92788864,32.77557876,450190000000000.0,1.0,centroid,RES3A,RES3A
+574267976,574267976,RES,RES1-2SNB,385155.033,192577.516,13.53189744,-79.92794047,32.77603579,450190000000000.0,1.0,centroid,RES1-2SNB,RES1-2SNB
+574268005,574268005,RES,RES1-1SNB,257591.82,128795.91,12.59055234,-79.92772127,32.77604828,450190000000000.0,1.0,centroid,RES1-1SNB,RES1-1SNB
+574346733,574346733,RES,RES1-2SNB,345561.158,172780.579,13.16675875,-79.92987341,32.77497559,450190000000000.0,1.0,centroid,RES1-2SNB,RES1-2SNB
+574346737,574346737,COM,COM3,128711.316,128711.316,12.62985934,-79.92934753,32.77496936,450190000000000.0,1.0,centroid,COM3,COM3
+574346738,574346738,RES,RES1-3SNB,569110.405,284555.202,12.57429248,-79.92997655,32.77497829,450190000000000.0,1.0,centroid,RES1-3SNB,RES1-3SNB
+574346740,574346740,RES,RES1-2SNB,272662.957,136331.478,12.62266219,-79.92924438,32.77514479,450190000000000.0,1.0,centroid,RES1-2SNB,RES1-2SNB
+574346744,574346744,PUB,REL1,1281715.19,1281715.19,13.0151455,-79.92929333,32.77533709,450190000000000.0,1.0,centroid,REL1,REL1
+574346753,574346753,RES,RES1-2SNB,311698.604,155849.302,13.77097465,-79.92981335,32.77577007,450190000000000.0,1.0,centroid,RES1-2SNB,RES1-2SNB
+574372447,574372447,COM,COM1,227020.051,227020.051,12.85938964,-79.92900036,32.77610772,450190000000000.0,1.0,centroid,COM1,COM1
+574372448,574372448,IND,IND2,134849.922,202274.884,12.85938964,-79.92900036,32.77610772,450190000000000.0,1.0,centroid,IND2,IND2
+574372473,574372473,COM,COM1,424486.831,424486.831,13.03663915,-79.92881546,32.7764473,450190000000000.0,1.0,centroid,COM1,COM1
+574372533,574372533,COM,COM7,393804.35,590706.525,11.22623405,-79.93032633,32.7759983,450190000000000.0,1.0,centroid,COM7,COM7
+574372543,574372543,RES,RES3A,338190.212,169095.106,12.60003902,-79.92938805,32.77623669,450190000000000.0,1.0,centroid,RES3A,RES3A
+574372567,574372567,COM,COM8,284295.791,284295.791,12.21837608,-79.92958905,32.77638171,450190000000000.0,1.0,centroid,COM8,COM8
+574421681,574421681,COM,COM8,778149.815,778149.815,13.54457556,-79.92846361,32.77697303,450190000000000.0,1.0,centroid,COM8,COM8
+574421683,574421683,COM,COM1,360197.146,360197.146,13.54457556,-79.92846361,32.77697303,450190000000000.0,1.0,centroid,COM1,COM1
+574431298,574431298,COM,COM4,1338267.91,1338267.91,10.1165989,-79.92646291,32.77728981,450190000000000.0,1.0,centroid,COM4,COM4
+574461065,574461065,RES,RES3A,266985.928,133492.964,12.13528704,-79.9297417,32.77681823,450190000000000.0,1.0,centroid,RES3A,RES3A
+574461073,574461073,COM,COM4,177990.619,177990.619,12.27511783,-79.92950828,32.77642489,450190000000000.0,1.0,centroid,COM4,COM4
+574461077,574461077,RES,RES3A,273660.577,136830.288,12.4406709,-79.92903083,32.7763684,450190000000000.0,1.0,centroid,RES3A,RES3A
+574461079,574461079,COM,COM4,177990.619,177990.619,12.86096032,-79.92860499,32.77648743,450190000000000.0,1.0,centroid,COM4,COM4
+574488771,574488771,RES,RES1-1SNB,222806.066,111403.033,13.26134397,-79.92896336,32.77597596,450190000000000.0,1.0,centroid,RES1-1SNB,RES1-1SNB
+574488793,574488793,RES,RES1-2SNB,216903.661,108451.83,12.29002837,-79.92736548,32.77640732,450190000000000.0,1.0,centroid,RES1-2SNB,RES1-2SNB
+574488804,574488804,RES,RES1-2SNB,292527.098,146263.549,13.21022478,-79.92816916,32.77627743,450190000000000.0,1.0,centroid,RES1-2SNB,RES1-2SNB
+574488806,574488806,RES,RES1-3SNB,358700.469,179350.234,13.36457265,-79.92824222,32.77627542,450190000000000.0,1.0,centroid,RES1-3SNB,RES1-3SNB
+574488816,574488816,RES,RES1-1SNB,221789.878,110894.939,6.334944159,-79.92574958,32.77633187,450190000000000.0,1.0,centroid,RES1-1SNB,RES1-1SNB
+574488819,574488819,RES,RES1-3SNB,416126.362,208063.181,6.052678219,-79.9267068,32.77616474,450190000000000.0,1.0,centroid,RES1-3SNB,RES1-3SNB
+574488830,574488830,RES,RES1-1SNB,182232.731,91116.3657,12.69306217,-79.92898379,32.77518059,450190000000000.0,1.0,centroid,RES1-1SNB,RES1-1SNB
+574488834,574488834,RES,RES1-3SNB,384398.576,192199.288,12.90637406,-79.92951787,32.77470617,450190000000000.0,1.0,centroid,RES1-3SNB,RES1-3SNB
+574488837,574488837,RES,RES3A,480189.335,240094.667,13.25912718,-79.92891,32.77509288,450190000000000.0,1.0,centroid,RES3A,RES3A
+574488848,574488848,RES,RES1-2SNB,322415.251,161207.625,13.14323291,-79.92763108,32.77497895,450190000000000.0,1.0,centroid,RES1-2SNB,RES1-2SNB
+574488851,574488851,RES,RES1-2SNB,222897.785,111448.892,10.150554,-79.92671547,32.7755542,450190000000000.0,1.0,centroid,RES1-2SNB,RES1-2SNB
+574488874,574488874,RES,RES3A,480189.335,240094.667,8.900641428,-79.92914469,32.77176507,450190000000000.0,1.0,centroid,RES3A,RES3A
+574507807,New development area: 574507807,New development area,New development area,42804025.98393736,26468929.704139814,5.35474157333374,,,,0.0,area,new_composite_area_structure,new_composite_area_content
+574507808,New development area: 574507808,New development area,New development area,34126193.93100962,21102777.308113553,1.2411025762557983,,,,0.0,area,new_composite_area_structure,new_composite_area_content
+574507809,New development area: 574507809,New development area,New development area,6629114.270542782,4099277.009446545,4.435575485229492,,,,0.0,area,new_composite_area_structure,new_composite_area_content
diff --git a/examples/data/setup_new_composite_area/test_setup_new_composite_area_geom/settings.toml b/examples/data/setup_new_composite_area/test_setup_new_composite_area_geom/settings.toml
new file mode 100644
index 00000000..c9018a8b
--- /dev/null
+++ b/examples/data/setup_new_composite_area/test_setup_new_composite_area_geom/settings.toml
@@ -0,0 +1,22 @@
+[global]
+crs = "epsg:4326"
+
+[output]
+path = "output"
+
+[output.csv]
+name = "output.csv"
+
+[output.geom]
+name1 = "spatial.gpkg"
+
+[vulnerability]
+file = "./vulnerability/vulnerability_curves.csv"
+scale = 0.1
+
+[exposure.csv]
+file = "./exposure/exposure.csv"
+
+[exposure.geom]
+crs = "EPSG:4326"
+file1 = "./exposure/building_points.gpkg"
diff --git a/examples/data/setup_new_composite_area/test_setup_new_composite_area_geom/vulnerability/vulnerability_curves.csv b/examples/data/setup_new_composite_area/test_setup_new_composite_area_geom/vulnerability/vulnerability_curves.csv
new file mode 100644
index 00000000..8d9e73a3
--- /dev/null
+++ b/examples/data/setup_new_composite_area/test_setup_new_composite_area_geom/vulnerability/vulnerability_curves.csv
@@ -0,0 +1,32 @@
+#UNIT=feet
+#METHOD,max,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean
+water depth [feet],AGR1,COM1,COM10,COM2,COM3,COM4,COM5,COM6,COM7,COM8,COM9,EDU1,GOV1,GOV2,IND1,IND2,IND3,IND4,IND5,IND6,REL1,RES1-1SNB,RES1-1SWB,RES1-2SNB,RES1-2SWB,RES1-3SNB,RES1-3SWB,RES2,RES3A,RES3B,RES3C,RES3D,RES3E,RES3F,RES4,RES5,RES6,new_composite_area_structure,new_composite_area_content
+-4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,0.0,0.04,0.0,0.03,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0025426278836509528,0.0025426278836509528
+-3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,0.0,0.04,0.0,0.03,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0025426278836509528,0.0025426278836509528
+-2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,0.0,0.08,0.0,0.06,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.004663991975927783,0.004663991975927783
+-1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.11,0.0,0.14,0.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.007913741223671014,0.007913741223671014
+0.0,0.0,0.01,0.0,0.0,0.0,0.02,0.0,0.0,0.02,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.18,0.17,0.11,0.19,0.05,0.12,0.11,0.12,0.12,0.12,0.12,0.12,0.12,0.0,0.0,0.0,0.10625376128385156,0.10625376128385156
+1.0,0.06,0.09,0.03,0.05,0.09,0.11,0.11,0.0,0.11,0.09,0.02,0.05,0.05,0.07,0.1,0.09,0.13,0.1,0.13,0.22,0.1,0.22,0.21,0.12,0.21,0.08,0.14,0.44,0.25,0.25,0.25,0.25,0.25,0.25,0.03,0.07,0.07,0.16183049147442324,0.16183049147442324
+2.0,0.11,0.14,0.05,0.08,0.12,0.16,0.11,0.0,0.12,0.11,0.04,0.07,0.08,0.1,0.12,0.14,0.14,0.14,0.14,0.31,0.11,0.25,0.29,0.14,0.26,0.12,0.2,0.63,0.5,0.5,0.5,0.5,0.5,0.5,0.05,0.1,0.1,0.24248244734202612,0.24248244734202612
+3.0,0.15,0.16,0.06,0.11,0.13,0.22,0.12,0.2,0.13,0.12,0.05,0.09,0.13,0.11,0.15,0.17,0.19,0.18,0.19,0.37,0.11,0.28,0.34,0.18,0.29,0.17,0.25,0.73,0.75,0.75,0.75,0.75,0.75,0.75,0.06,0.14,0.14,0.32801404212637914,0.32801404212637914
+4.0,0.19,0.18,0.07,0.13,0.16,0.28,0.13,0.25,0.14,0.14,0.05,0.09,0.14,0.12,0.19,0.22,0.22,0.22,0.22,0.43,0.12,0.3,0.38,0.2,0.34,0.19,0.31,0.78,1.0,1.0,1.0,1.0,1.0,1.0,0.07,0.15,0.15,0.40343530591775323,0.40343530591775323
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diff --git a/examples/data/update_ground_elevation/fiat_model/exposure/building_points.gpkg b/examples/data/update_ground_elevation/fiat_model/exposure/building_points.gpkg
new file mode 100644
index 00000000..eb21a7ca
Binary files /dev/null and b/examples/data/update_ground_elevation/fiat_model/exposure/building_points.gpkg differ
diff --git a/examples/data/update_ground_elevation/fiat_model/exposure/exposure.csv b/examples/data/update_ground_elevation/fiat_model/exposure/exposure.csv
new file mode 100644
index 00000000..e25d0547
--- /dev/null
+++ b/examples/data/update_ground_elevation/fiat_model/exposure/exposure.csv
@@ -0,0 +1,1995 @@
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+574267749,574267749,RES,RES3A,491247.521,245623.76,13.51820246,-79.92788864,32.77557876,4.5019E+14,1,centroid,RES3A,RES3A
+574267976,574267976,RES,RES1-2SNB,385155.033,192577.516,13.53189744,-79.92794047,32.77603579,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+574268005,574268005,RES,RES1-1SNB,257591.82,128795.91,12.59055234,-79.92772127,32.77604828,4.5019E+14,1,centroid,RES1-1SNB,RES1-1SNB
+574346733,574346733,RES,RES1-2SNB,345561.158,172780.579,13.16675875,-79.92987341,32.77497559,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+574346737,574346737,COM,COM3,128711.316,128711.316,12.62985934,-79.92934753,32.77496936,4.5019E+14,1,centroid,COM3,COM3
+574346738,574346738,RES,RES1-3SNB,569110.405,284555.202,12.57429248,-79.92997655,32.77497829,4.5019E+14,1,centroid,RES1-3SNB,RES1-3SNB
+574346740,574346740,RES,RES1-2SNB,272662.957,136331.478,12.62266219,-79.92924438,32.77514479,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+574346744,574346744,PUB,REL1,1281715.19,1281715.19,13.0151455,-79.92929333,32.77533709,4.5019E+14,1,centroid,REL1,REL1
+574346753,574346753,RES,RES1-2SNB,311698.604,155849.302,13.77097465,-79.92981335,32.77577007,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+574372447,574372447,COM,COM1,227020.051,227020.051,12.85938964,-79.92900036,32.77610772,4.5019E+14,1,centroid,COM1,COM1
+574372448,574372448,IND,IND2,134849.922,202274.884,12.85938964,-79.92900036,32.77610772,4.5019E+14,1,centroid,IND2,IND2
+574372473,574372473,COM,COM1,424486.831,424486.831,13.03663915,-79.92881546,32.7764473,4.5019E+14,1,centroid,COM1,COM1
+574372533,574372533,COM,COM7,393804.35,590706.525,11.22623405,-79.93032633,32.7759983,4.5019E+14,1,centroid,COM7,COM7
+574372543,574372543,RES,RES3A,338190.212,169095.106,12.60003902,-79.92938805,32.77623669,4.5019E+14,1,centroid,RES3A,RES3A
+574372567,574372567,COM,COM8,284295.791,284295.791,12.21837608,-79.92958905,32.77638171,4.5019E+14,1,centroid,COM8,COM8
+574421681,574421681,COM,COM8,778149.815,778149.815,13.54457556,-79.92846361,32.77697303,4.5019E+14,1,centroid,COM8,COM8
+574421683,574421683,COM,COM1,360197.146,360197.146,13.54457556,-79.92846361,32.77697303,4.5019E+14,1,centroid,COM1,COM1
+574431298,574431298,COM,COM4,1338267.91,1338267.91,10.1165989,-79.92646291,32.77728981,4.5019E+14,1,centroid,COM4,COM4
+574461065,574461065,RES,RES3A,266985.928,133492.964,12.13528704,-79.9297417,32.77681823,4.5019E+14,1,centroid,RES3A,RES3A
+574461073,574461073,COM,COM4,177990.619,177990.619,12.27511783,-79.92950828,32.77642489,4.5019E+14,1,centroid,COM4,COM4
+574461077,574461077,RES,RES3A,273660.577,136830.288,12.4406709,-79.92903083,32.7763684,4.5019E+14,1,centroid,RES3A,RES3A
+574461079,574461079,COM,COM4,177990.619,177990.619,12.86096032,-79.92860499,32.77648743,4.5019E+14,1,centroid,COM4,COM4
+574488771,574488771,RES,RES1-1SNB,222806.066,111403.033,13.26134397,-79.92896336,32.77597596,4.5019E+14,1,centroid,RES1-1SNB,RES1-1SNB
+574488793,574488793,RES,RES1-2SNB,216903.661,108451.83,12.29002837,-79.92736548,32.77640732,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+574488804,574488804,RES,RES1-2SNB,292527.098,146263.549,13.21022478,-79.92816916,32.77627743,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+574488806,574488806,RES,RES1-3SNB,358700.469,179350.234,13.36457265,-79.92824222,32.77627542,4.5019E+14,1,centroid,RES1-3SNB,RES1-3SNB
+574488816,574488816,RES,RES1-1SNB,221789.878,110894.939,6.334944159,-79.92574958,32.77633187,4.5019E+14,1,centroid,RES1-1SNB,RES1-1SNB
+574488819,574488819,RES,RES1-3SNB,416126.362,208063.181,6.052678219,-79.9267068,32.77616474,4.5019E+14,1,centroid,RES1-3SNB,RES1-3SNB
+574488830,574488830,RES,RES1-1SNB,182232.731,91116.3657,12.69306217,-79.92898379,32.77518059,4.5019E+14,1,centroid,RES1-1SNB,RES1-1SNB
+574488834,574488834,RES,RES1-3SNB,384398.576,192199.288,12.90637406,-79.92951787,32.77470617,4.5019E+14,1,centroid,RES1-3SNB,RES1-3SNB
+574488837,574488837,RES,RES3A,480189.335,240094.667,13.25912718,-79.92891,32.77509288,4.5019E+14,1,centroid,RES3A,RES3A
+574488848,574488848,RES,RES1-2SNB,322415.251,161207.625,13.14323291,-79.92763108,32.77497895,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+574488851,574488851,RES,RES1-2SNB,222897.785,111448.892,10.150554,-79.92671547,32.7755542,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+574488874,574488874,RES,RES3A,480189.335,240094.667,8.900641428,-79.92914469,32.77176507,4.5019E+14,1,centroid,RES3A,RES3A
diff --git a/examples/data/update_ground_elevation/fiat_model/exposure/region.gpkg b/examples/data/update_ground_elevation/fiat_model/exposure/region.gpkg
new file mode 100644
index 00000000..2c06a9ef
Binary files /dev/null and b/examples/data/update_ground_elevation/fiat_model/exposure/region.gpkg differ
diff --git a/examples/data/update_ground_elevation/fiat_model/settings.toml b/examples/data/update_ground_elevation/fiat_model/settings.toml
new file mode 100644
index 00000000..c9018a8b
--- /dev/null
+++ b/examples/data/update_ground_elevation/fiat_model/settings.toml
@@ -0,0 +1,22 @@
+[global]
+crs = "epsg:4326"
+
+[output]
+path = "output"
+
+[output.csv]
+name = "output.csv"
+
+[output.geom]
+name1 = "spatial.gpkg"
+
+[vulnerability]
+file = "./vulnerability/vulnerability_curves.csv"
+scale = 0.1
+
+[exposure.csv]
+file = "./exposure/exposure.csv"
+
+[exposure.geom]
+crs = "EPSG:4326"
+file1 = "./exposure/building_points.gpkg"
diff --git a/examples/data/update_ground_elevation/fiat_model/vulnerability/vulnerability_curves.csv b/examples/data/update_ground_elevation/fiat_model/vulnerability/vulnerability_curves.csv
new file mode 100644
index 00000000..81d37acf
--- /dev/null
+++ b/examples/data/update_ground_elevation/fiat_model/vulnerability/vulnerability_curves.csv
@@ -0,0 +1,32 @@
+#UNIT=feet
+#METHOD,max,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean
+water depth [feet],AGR1,COM1,COM10,COM2,COM3,COM4,COM5,COM6,COM7,COM8,COM9,EDU1,GOV1,GOV2,IND1,IND2,IND3,IND4,IND5,IND6,REL1,RES1-1SNB,RES1-1SWB,RES1-2SNB,RES1-2SWB,RES1-3SNB,RES1-3SWB,RES2,RES3A,RES3B,RES3C,RES3D,RES3E,RES3F,RES4,RES5,RES6
+-4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,0.0,0.04,0.0,0.03,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,0.0,0.04,0.0,0.03,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,0.0,0.08,0.0,0.06,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.11,0.0,0.14,0.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+0.0,0.0,0.01,0.0,0.0,0.0,0.02,0.0,0.0,0.02,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.18,0.17,0.11,0.19,0.05,0.12,0.11,0.12,0.12,0.12,0.12,0.12,0.12,0.0,0.0,0.0
+1.0,0.06,0.09,0.03,0.05,0.09,0.11,0.11,0.0,0.11,0.09,0.02,0.05,0.05,0.07,0.1,0.09,0.13,0.1,0.13,0.22,0.1,0.22,0.21,0.12,0.21,0.08,0.14,0.44,0.25,0.25,0.25,0.25,0.25,0.25,0.03,0.07,0.07
+2.0,0.11,0.14,0.05,0.08,0.12,0.16,0.11,0.0,0.12,0.11,0.04,0.07,0.08,0.1,0.12,0.14,0.14,0.14,0.14,0.31,0.11,0.25,0.29,0.14,0.26,0.12,0.2,0.63,0.5,0.5,0.5,0.5,0.5,0.5,0.05,0.1,0.1
+3.0,0.15,0.16,0.06,0.11,0.13,0.22,0.12,0.2,0.13,0.12,0.05,0.09,0.13,0.11,0.15,0.17,0.19,0.18,0.19,0.37,0.11,0.28,0.34,0.18,0.29,0.17,0.25,0.73,0.75,0.75,0.75,0.75,0.75,0.75,0.06,0.14,0.14
+4.0,0.19,0.18,0.07,0.13,0.16,0.28,0.13,0.25,0.14,0.14,0.05,0.09,0.14,0.12,0.19,0.22,0.22,0.22,0.22,0.43,0.12,0.3,0.38,0.2,0.34,0.19,0.31,0.78,1.0,1.0,1.0,1.0,1.0,1.0,0.07,0.15,0.15
+5.0,0.25,0.2,0.08,0.16,0.19,0.35,0.15,0.3,0.16,0.16,0.05,0.1,0.14,0.15,0.22,0.26,0.25,0.26,0.25,0.47,0.12,0.31,0.43,0.22,0.39,0.22,0.36,0.79,1.0,1.0,1.0,1.0,1.0,1.0,0.09,0.15,0.15
+6.0,0.3,0.23,0.1,0.19,0.22,0.38,0.17,0.35,0.17,0.18,0.06,0.11,0.15,0.17,0.26,0.3,0.28,0.34,0.28,0.5,0.13,0.4,0.5,0.24,0.44,0.24,0.38,0.81,1.0,1.0,1.0,1.0,1.0,1.0,0.12,0.16,0.16
+7.0,0.35,0.26,0.13,0.22,0.25,0.41,0.19,0.4,0.18,0.2,0.08,0.13,0.17,0.2,0.3,0.32,0.3,0.41,0.3,0.54,0.14,0.43,0.5,0.26,0.5,0.25,0.41,0.82,1.0,1.0,1.0,1.0,1.0,1.0,0.14,0.18,0.18
+8.0,0.41,0.3,0.17,0.25,0.28,0.44,0.22,0.43,0.2,0.22,0.1,0.15,0.19,0.23,0.35,0.35,0.33,0.42,0.33,0.57,0.14,0.43,0.54,0.3,0.55,0.3,0.44,0.83,1.0,1.0,1.0,1.0,1.0,1.0,0.18,0.2,0.2
+9.0,0.46,0.34,0.21,0.29,0.32,0.47,0.24,0.47,0.22,0.26,0.12,0.17,0.22,0.27,0.39,0.37,0.34,0.42,0.34,0.61,0.15,0.45,0.55,0.34,0.57,0.35,0.48,0.84,1.0,1.0,1.0,1.0,1.0,1.0,0.21,0.23,0.23
+10.0,0.51,0.38,0.25,0.32,0.35,0.5,0.28,0.5,0.24,0.29,0.15,0.2,0.26,0.31,0.42,0.39,0.36,0.45,0.36,0.63,0.17,0.46,0.55,0.38,0.59,0.38,0.5,0.85,1.0,1.0,1.0,1.0,1.0,1.0,0.26,0.26,0.26
+11.0,0.57,0.42,0.3,0.37,0.39,0.54,0.31,0.53,0.27,0.33,0.2,0.24,0.31,0.35,0.48,0.43,0.39,0.47,0.39,0.64,0.19,0.47,0.57,0.39,0.61,0.39,0.52,0.86,1.0,1.0,1.0,1.0,1.0,1.0,0.31,0.3,0.3
+12.0,0.63,0.47,0.35,0.41,0.43,0.57,0.34,0.55,0.3,0.37,0.24,0.28,0.37,0.4,0.5,0.46,0.4,0.49,0.4,0.65,0.24,0.47,0.58,0.4,0.63,0.4,0.54,0.88,1.0,1.0,1.0,1.0,1.0,1.0,0.36,0.34,0.34
+13.0,0.7,0.51,0.41,0.45,0.47,0.59,0.37,0.57,0.34,0.41,0.29,0.33,0.44,0.44,0.51,0.48,0.42,0.5,0.42,0.67,0.3,0.49,0.6,0.42,0.65,0.42,0.56,0.89,1.0,1.0,1.0,1.0,1.0,1.0,0.41,0.38,0.38
+14.0,0.75,0.55,0.47,0.49,0.5,0.62,0.4,0.6,0.37,0.45,0.35,0.39,0.51,0.48,0.53,0.5,0.42,0.5,0.42,0.68,0.38,0.5,0.62,0.43,0.66,0.43,0.57,0.9,1.0,1.0,1.0,1.0,1.0,1.0,0.46,0.42,0.42
+15.0,0.79,0.58,0.52,0.52,0.54,0.66,0.44,0.6,0.41,0.5,0.42,0.45,0.59,0.52,0.54,0.51,0.43,0.5,0.43,0.69,0.45,0.5,0.63,0.44,0.68,0.44,0.59,0.91,1.0,1.0,1.0,1.0,1.0,1.0,0.5,0.47,0.47
+16.0,0.82,0.61,0.58,0.55,0.57,0.68,0.48,0.6,0.44,0.53,0.49,0.52,0.65,0.56,0.55,0.54,0.43,0.5,0.43,0.7,0.52,0.5,0.65,0.45,0.69,0.45,0.61,0.92,1.0,1.0,1.0,1.0,1.0,1.0,0.54,0.52,0.52
+17.0,0.84,0.64,0.65,0.58,0.61,0.7,0.51,0.6,0.48,0.57,0.56,0.59,0.7,0.6,0.55,0.55,0.44,0.5,0.44,0.71,0.58,0.51,0.67,0.47,0.71,0.47,0.63,0.94,1.0,1.0,1.0,1.0,1.0,1.0,0.58,0.57,0.57
+18.0,0.87,0.67,0.71,0.61,0.64,0.72,0.55,0.6,0.51,0.6,0.62,0.64,0.74,0.64,0.56,0.57,0.44,0.5,0.44,0.72,0.64,0.51,0.69,0.48,0.72,0.48,0.65,0.95,1.0,1.0,1.0,1.0,1.0,1.0,0.62,0.62,0.62
+19.0,0.89,0.69,0.76,0.63,0.68,0.74,0.59,0.6,0.54,0.63,0.68,0.69,0.79,0.68,0.56,0.59,0.44,0.5,0.44,0.73,0.69,0.52,0.7,0.49,0.74,0.49,0.67,0.96,1.0,1.0,1.0,1.0,1.0,1.0,0.66,0.67,0.67
+20.0,0.9,0.71,0.81,0.66,0.71,0.76,0.63,0.6,0.56,0.66,0.74,0.74,0.83,0.72,0.57,0.6,0.44,0.5,0.44,0.74,0.74,0.52,0.72,0.5,0.75,0.5,0.69,0.97,1.0,1.0,1.0,1.0,1.0,1.0,0.7,0.72,0.72
+21.0,0.92,0.74,0.86,0.68,0.75,0.77,0.67,0.6,0.59,0.69,0.8,0.79,0.87,0.76,0.57,0.62,0.44,0.5,0.44,0.75,0.78,0.53,0.74,0.52,0.77,0.52,0.71,0.98,1.0,1.0,1.0,1.0,1.0,1.0,0.74,0.77,0.77
+22.0,0.93,0.76,0.91,0.7,0.78,0.78,0.71,0.6,0.61,0.73,0.86,0.84,0.91,0.8,0.57,0.63,0.45,0.5,0.45,0.76,0.82,0.53,0.76,0.53,0.79,0.53,0.73,0.99,1.0,1.0,1.0,1.0,1.0,1.0,0.78,0.82,0.82
+23.0,0.95,0.78,0.96,0.71,0.82,0.79,0.75,0.6,0.64,0.76,0.92,0.89,0.95,0.84,0.58,0.65,0.45,0.5,0.45,0.76,0.85,0.54,0.77,0.54,0.8,0.54,0.75,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.82,0.87,0.87
+24.0,0.96,0.8,1.0,0.73,0.85,0.8,0.79,0.6,0.66,0.78,0.98,0.94,0.98,0.88,0.58,0.66,0.45,0.5,0.45,0.77,0.88,0.54,0.79,0.56,0.82,0.56,0.77,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.86,0.92,0.92
diff --git a/examples/data/update_ground_elevation/fiat_model/vulnerability/vulnerability_identifiers.csv b/examples/data/update_ground_elevation/fiat_model/vulnerability/vulnerability_identifiers.csv
new file mode 100644
index 00000000..7d7fbb72
--- /dev/null
+++ b/examples/data/update_ground_elevation/fiat_model/vulnerability/vulnerability_identifiers.csv
@@ -0,0 +1,38 @@
+,Name,Link,BldgDmgFnID,Occupancy,Source,Description,full_identifier
+0,AGR1,AGR1,616,AGR1 ,USACE - Galveston,"Average Agriculture, structure","616AGR1USACE-GalvestonAverageAgriculture,structure"
+1,COM1,COM1,217,COM1 ,USACE - Galveston,"Average Retail, Structure","217COM1USACE-GalvestonAverageRetail,Structure"
+2,COM10,COM10,543,COM10,USACE - Galveston,"Garage, structure","543COM10USACE-GalvestonGarage,structure"
+3,COM2,COM2,341,COM2 ,USACE - Galveston,"Average wholesale, Structure","341COM2USACE-GalvestonAveragewholesale,Structure"
+4,COM3,COM3,375,COM3 ,USACE - Galveston,"Average Personal & Repair Services, Structure","375COM3USACE-GalvestonAveragePersonal&RepairServices,Structure"
+5,COM4,COM4,431,COM4 ,USACE - Galveston,"Average Prof/Tech Services, Structure","431COM4USACE-GalvestonAverageProf/TechServices,Structure"
+6,COM5,COM5,467,COM5 ,USACE - Galveston,"Bank, Structure","467COM5USACE-GalvestonBank,Structure"
+7,COM6,COM6,474,COM6 ,USACE - Galveston,"Hospital, Structure","474COM6USACE-GalvestonHospital,Structure"
+8,COM7,COM7,475,COM7 ,USACE - Galveston,"Average Medical Office, Structure","475COM7USACE-GalvestonAverageMedicalOffice,Structure"
+9,COM8,COM8,493,COM8 ,USACE - Galveston,"Average Entertainment/Recreation, Structure","493COM8USACE-GalvestonAverageEntertainment/Recreation,Structure"
+10,COM9,COM9,532,COM9 ,USACE - Galveston,"Average theatre, Structure","532COM9USACE-GalvestonAveragetheatre,Structure"
+11,EDU1,EDU1,643,EDU1 ,USACE - Galveston,"Average school, structure","643EDU1USACE-GalvestonAverageschool,structure"
+12,GOV1,GOV1,631,GOV1 ,USACE - Galveston,"Average government services, structure","631GOV1USACE-GalvestonAveragegovernmentservices,structure"
+13,GOV2,GOV2,640,GOV2 ,USACE - Galveston,"Average emergency response, Structure","640GOV2USACE-GalvestonAverageemergencyresponse,Structure"
+14,IND1,IND1,545,IND1 ,USACE - Galveston,"Average heavy industrial, Structure","545IND1USACE-GalvestonAverageheavyindustrial,Structure"
+15,IND2,IND2,559,IND2 ,USACE - Galveston,"Average light industrial, structure","559IND2USACE-GalvestonAveragelightindustrial,structure"
+16,IND3,IND3,575,IND3 ,USACE - Galveston,"Average Food/Drug/Chem, Structure","575IND3USACE-GalvestonAverageFood/Drug/Chem,Structure"
+17,IND4,IND4,586,IND4 ,USACE - Galveston,"Average Metals/Minerals processing, structure","586IND4USACE-GalvestonAverageMetals/Mineralsprocessing,structure"
+18,IND5,IND5,591,IND5 ,USACE - Galveston,"Average High Technology, structure","591IND5USACE-GalvestonAverageHighTechnology,structure"
+19,IND6,IND6,592,IND6 ,USACE - Galveston,"Average Construction, structure","592IND6USACE-GalvestonAverageConstruction,structure"
+20,REL1,REL1,624,REL1 ,USACE - Galveston,"Church, structure","624REL1USACE-GalvestonChurch,structure"
+21,RES1-1SNB,RES1-1SNB,105,RES1 ,FIA,"one floor, no basement, Structure, A-Zone","105RES1FIAonefloor,nobasement,Structure,A-Zone"
+22,RES1-1SWB,RES1-1SWB,106,RES1 ,FIA (MOD.),"one floor, w/ basement, Structure, A-Zone","106RES1FIA(MOD.)onefloor,w/basement,Structure,A-Zone"
+23,RES1-2SNB,RES1-2SNB,107,RES1 ,FIA,"two floors, no basement, Structure, A-Zone","107RES1FIAtwofloors,nobasement,Structure,A-Zone"
+24,RES1-2SWB,RES1-2SWB,108,RES1 ,FIA (MOD.),"two floors, w/ basement, Structure, A-Zone","108RES1FIA(MOD.)twofloors,w/basement,Structure,A-Zone"
+25,RES1-3SNB,RES1-3SNB,109,RES1 ,FIA,"three or more floors, no basement, Structure, A-Zone","109RES1FIAthreeormorefloors,nobasement,Structure,A-Zone"
+26,RES1-3SWB,RES1-3SWB,110,RES1 ,FIA (MOD.),"three or more floors, w/ basement, Structure, A-Zone","110RES1FIA(MOD.)threeormorefloors,w/basement,Structure,A-Zone"
+27,RES2,RES2,189,RES2 ,FIA,"Mobile home, structure, A-Zone","189RES2FIAMobilehome,structure,A-Zone"
+28,RES3A,RES3A,659,RES3A,BCAR - Jan 2011,"1to2 Stories, slab - no basement, Coastal A or V Zone","659RES3ABCAR-Jan20111to2Stories,slab-nobasement,CoastalAorVZone"
+29,RES3B,RES3B,660,RES3B,BCAR - Jan 2011,"1to2 Stories, slab - no basement, Coastal A or V Zone","660RES3BBCAR-Jan20111to2Stories,slab-nobasement,CoastalAorVZone"
+30,RES3C,RES3C,660,RES3B,BCAR - Jan 2011,"1to2 Stories, slab - no basement, Coastal A or V Zone","660RES3BBCAR-Jan20111to2Stories,slab-nobasement,CoastalAorVZone"
+31,RES3D,RES3D,660,RES3B,BCAR - Jan 2011,"1to2 Stories, slab - no basement, Coastal A or V Zone","660RES3BBCAR-Jan20111to2Stories,slab-nobasement,CoastalAorVZone"
+32,RES3E,RES3E,660,RES3B,BCAR - Jan 2011,"1to2 Stories, slab - no basement, Coastal A or V Zone","660RES3BBCAR-Jan20111to2Stories,slab-nobasement,CoastalAorVZone"
+33,RES3F,RES3F,660,RES3B,BCAR - Jan 2011,"1to2 Stories, slab - no basement, Coastal A or V Zone","660RES3BBCAR-Jan20111to2Stories,slab-nobasement,CoastalAorVZone"
+34,RES4,RES4,209,RES4 ,USACE - Galveston,"Average Hotel & Motel, structure","209RES4USACE-GalvestonAverageHotel&Motel,structure"
+35,RES5,RES5,214,RES5 ,USACE - Galveston,"Average institutional dormitory, structure","214RES5USACE-GalvestonAverageinstitutionaldormitory,structure"
+36,RES6,RES6,215,RES6 ,USACE - Galveston,"Nursing Home, structure","215RES6USACE-GalvestonNursingHome,structure"
diff --git a/examples/data/update_ground_elevation/test_update_ground_elevation/exposure/building_points.gpkg b/examples/data/update_ground_elevation/test_update_ground_elevation/exposure/building_points.gpkg
new file mode 100644
index 00000000..fa311e5f
Binary files /dev/null and b/examples/data/update_ground_elevation/test_update_ground_elevation/exposure/building_points.gpkg differ
diff --git a/examples/data/update_ground_elevation/test_update_ground_elevation/exposure/exposure.csv b/examples/data/update_ground_elevation/test_update_ground_elevation/exposure/exposure.csv
new file mode 100644
index 00000000..8accb0e5
--- /dev/null
+++ b/examples/data/update_ground_elevation/test_update_ground_elevation/exposure/exposure.csv
@@ -0,0 +1,1995 @@
+Object ID,Object Name,Primary Object Type,Secondary Object Type,Max Potential Damage: Structure,Max Potential Damage: Content,Ground Elevation,X Coordinate,Y Coordinate,Aggregation Label: Census Block,Ground Floor Height,Extraction Method,Damage Function: Structure,Damage Function: Content
+574486973,574486973,RES,RES3A,480189.335,240094.667,2.580267906188965,-79.93682513,32.77688289,450190000000000.0,1,centroid,RES3A,RES3A
+573783417,573783417,COM,RES4,1177614.2,588807.1,2.580267906188965,-79.93000008,32.77054524,450190000000000.0,1,centroid,RES4,RES4
+573783433,573783433,COM,RES4,476216.254,238108.127,2.0590968132019043,-79.92968382,32.7712713,450190000000000.0,1,centroid,RES4,RES4
+573785665,573785665,RES,RES1-2SNB,330007.42,165003.71,2.6374380588531494,-79.93179589,32.77336473,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+574346729,574346729,RES,RES3A,472285.148,236142.574,2.0249850749969482,-79.93064525,32.77485433,450190000000000.0,1,centroid,RES3A,RES3A
+574372707,574372707,RES,RES1-3SNB,389363.808,194681.904,2.0249850749969482,-79.93187273,32.77098914,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB
+574372721,574372721,RES,RES3A,503924.421,251962.21,2.0249850749969482,-79.93191719,32.77164194,450190000000000.0,1,centroid,RES3A,RES3A
+574476728,574476728,RES,RES1-1SNB,96964.414,48482.207,3.694910764694214,-79.93241085,32.76996811,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB
+574476811,574476811,RES,RES1-1SNB,152049.256,76024.6281,3.694910764694214,-79.93241085,32.76996811,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB
+574486899,574486899,RES,RES3A,480189.335,240094.667,3.694910764694214,-79.93027436,32.77536076,450190000000000.0,1,centroid,RES3A,RES3A
+574486932,574486932,RES,RES1-1SNB,177705.21,88852.6051,1.6509150266647339,-79.92998998,32.77314466,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB
+573827729,573827729,COM,COM8,37755.4885,37755.4885,1.6509150266647339,-79.93080905,32.77672792,450190000000000.0,1,centroid,COM8,COM8
+574421669,574421669,RES,RES3A,170840.757,85420.3788,1.6509150266647339,-79.92907633,32.7771126,450190000000000.0,1,centroid,RES3A,RES3A
+574488781,574488781,RES,RES1-3SNB,374871.082,187435.541,1.6509150266647339,-79.92578441,32.77724701,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB
+573467745,573467745,RES,RES3A,516183.793,258091.896,1.6509150266647339,-79.92827685,32.77200663,450190000000000.0,1,centroid,RES3A,RES3A
+573779323,573779323,RES,RES1-2SWB,437207.93,218603.965,3.6249663829803467,-79.92874432,32.77330072,450190000000000.0,1,centroid,RES1-2SWB,RES1-2SWB
+573779375,573779375,RES,RES1-1SNB,226202.175,113101.087,3.6249663829803467,-79.92780556,32.7743702,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB
+573779393,573779393,RES,RES3A,200255.163,100127.581,3.7134814262390137,-79.92811631,32.77473003,450190000000000.0,1,centroid,RES3A,RES3A
+573779405,573779405,RES,RES1-3SNB,398598.966,199299.483,3.5045104026794434,-79.92862217,32.77481123,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB
+573782594,573782594,RES,RES3A,489787.119,244893.559,1.9962352514266968,-79.92975812,32.77311597,450190000000000.0,1,centroid,RES3A,RES3A
+573782596,573782596,RES,RES3A,675402.418,337701.209,2.0721724033355713,-79.93036119,32.77318439,450190000000000.0,1,centroid,RES3A,RES3A
+573782413,573782413,RES,RES3A,437002.054,218501.027,2.1630349159240723,-79.92871845,32.77219172,450190000000000.0,1,centroid,RES3A,RES3A
+573782415,573782415,RES,RES1-3SNB,369429.359,184714.679,2.247926712036133,-79.92887994,32.77219043,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB
+573782419,573782419,RES,RES3A,448799.6,224399.8,1.5835663080215454,-79.92834819,32.77223276,450190000000000.0,1,centroid,RES3A,RES3A
+573782453,573782453,RES,RES1-3SNB,448326.4,224163.2,1.6850732564926147,-79.928964,32.77290941,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB
+573782528,573782528,RES,RES3A,677628.089,338814.044,3.780707597732544,-79.9284514,32.77542131,450190000000000.0,1,centroid,RES3A,RES3A
+573783136,573783136,RES,RES1-1SNB,187137.546,93568.7731,3.3954412937164307,-79.92955844,32.7733371,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB
+573783158,573783158,PUB,EDU1,4297828.09,4297828.09,3.3954412937164307,-79.92936283,32.77416934,450190000000000.0,1,centroid,EDU1,EDU1
+573783175,573783175,RES,RES3A,327754.263,163877.131,2.904144763946533,-79.93046822,32.77447171,450190000000000.0,1,centroid,RES3A,RES3A
+573783185,573783185,RES,RES1-2SNB,438110.47,219055.235,2.0328288078308105,-79.93039775,32.77458916,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+573783419,573783419,RES,RES1-2SWB,899667.893,449833.946,2.0328288078308105,-79.92958184,32.77059715,450190000000000.0,1,centroid,RES1-2SWB,RES1-2SWB
+573783425,573783425,RES,RES1-3SWB,615853.155,307926.577,1.9762399196624756,-79.92967987,32.77098975,450190000000000.0,1,centroid,RES1-3SWB,RES1-3SWB
+573785435,573785435,COM,COM8,988173.174,988173.174,3.1606128215789795,-79.93155196,32.77744925,450190000000000.0,1,centroid,COM8,COM8
+573785827,573785827,RES,RES1-3SWB,481445.527,240722.763,3.1606128215789795,-79.93273414,32.77520953,450190000000000.0,1,centroid,RES1-3SWB,RES1-3SWB
+573785950,573785950,RES,RES1-2SNB,472439.483,236219.741,3.1718673706054688,-79.93278733,32.7738393,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+573785982,573785982,RES,RES1-3SNB,544883.847,272441.923,3.1718673706054688,-79.93262251,32.77438286,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB
+573785986,573785986,RES,RES3B,409580.853,204790.426,1.9627610445022583,-79.93332761,32.77233853,450190000000000.0,1,centroid,RES3B,RES3B
+573818743,573818743,RES,RES1-2SNB,280041.982,140020.991,3.319775342941284,-79.92587104,32.77601644,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+573818747,573818747,RES,RES1-1SNB,238450.81,119225.405,3.319775342941284,-79.9257632,32.77629841,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB
+573827725,573827725,COM,COM4,212093.854,212093.854,3.319775342941284,-79.929484,32.777288,450190000000000.0,1,centroid,COM4,COM4
+573827727,573827727,PUB,GOV1,1531018.22,1531018.22,3.3900787830352783,-79.93080905,32.77672792,450190000000000.0,1,centroid,GOV1,GOV1
+573827740,573827740,RES,RES3A,210487.941,105243.97,3.385042428970337,-79.92972682,32.77680666,450190000000000.0,1,centroid,RES3A,RES3A
+573827758,573827758,COM,COM4,414162.244,414162.244,3.385042428970337,-79.92947989,32.77702812,450190000000000.0,1,centroid,COM4,COM4
+573831699,573831699,RES,RES1-2SNB,195993.925,97996.9627,3.385042428970337,-79.93245507,32.77518981,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+573831713,573831713,COM,COM4,221441.012,221441.012,3.3632373809814453,-79.93193054,32.7752842,450190000000000.0,1,centroid,COM4,COM4
+573831715,573831715,RES,RES1-2SNB,359315.841,179657.92,3.3632373809814453,-79.93244164,32.77533365,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+574267660,574267660,COM,COM2,411264.761,411264.761,3.442486524581909,-79.92712901,32.77418197,450190000000000.0,1,centroid,COM2,COM2
+574267669,574267669,RES,RES3A,817491.32,408745.66,3.442486524581909,-79.9270412,32.77465035,450190000000000.0,1,centroid,RES3A,RES3A
+574267743,574267743,RES,RES1-3SNB,345016.887,172508.443,3.9389076232910156,-79.92810606,32.77541377,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB
+574267753,574267753,RES,RES3A,264783.443,132391.721,4.070433616638184,-79.92802961,32.77570753,450190000000000.0,1,centroid,RES3A,RES3A
+574268223,574268223,RES,RES1-3SNB,414397.057,207198.528,2.3864593505859375,-79.9320608,32.77205654,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB
+574268225,574268225,RES,RES3A,531400.843,265700.421,2.446042776107788,-79.93161077,32.77204931,450190000000000.0,1,centroid,RES3A,RES3A
+574268229,574268229,RES,RES1-2SNB,440077.672,220038.836,2.8119096755981445,-79.93134554,32.77208614,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+574293801,574293801,RES,RES1-1SNB,265814.38,132907.19,3.941007375717163,-79.93220346,32.77440051,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB
+574346714,574346714,RES,RES1-1SNB,269885.626,134942.813,3.941007375717163,-79.92566411,32.77661119,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB
+574346742,574346742,RES,RES1-2SNB,569213.092,284606.546,3.941007375717163,-79.93054012,32.77538017,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+574346745,574346745,RES,RES1-2SNB,329762.78,164881.39,3.941007375717163,-79.92938432,32.77552901,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+574346752,574346752,RES,RES1-1SNB,310035.415,155017.707,3.328754186630249,-79.93066427,32.77579367,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB
+574372456,574372456,COM,COM1,40694.094,40694.094,1.7508022785186768,-79.92780897,32.77626052,450190000000000.0,1,centroid,COM1,COM1
+574372466,574372466,COM,COM4,211663.463,211663.463,1.7508022785186768,-79.92834043,32.77655435,450190000000000.0,1,centroid,COM4,COM4
+574372521,574372521,RES,RES3A,141992.009,70996.0045,1.7508022785186768,-79.93005346,32.77625511,450190000000000.0,1,centroid,RES3A,RES3A
+574372563,574372563,COM,COM4,653032.622,653032.622,1.7508022785186768,-79.92989155,32.77635293,450190000000000.0,1,centroid,COM4,COM4
+574372711,574372711,RES,RES1-2SNB,312504.938,156252.469,1.7508022785186768,-79.93193579,32.77121503,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+574409649,574409649,COM,COM2,421925.305,421925.305,3.8647475242614746,-79.93305761,32.77698208,450190000000000.0,1,centroid,COM2,COM2
+574421679,574421679,COM,COM4,2764995.14,2764995.14,3.8647475242614746,-79.92846361,32.77697303,450190000000000.0,1,centroid,COM4,COM4
+574437151,574437151,COM,COM1,164588.602,164588.602,3.8647475242614746,-79.92686154,32.77684937,450190000000000.0,1,centroid,COM1,COM1
+574461085,574461085,RES,RES3A,266985.928,133492.964,3.8647475242614746,-79.92799481,32.77708356,450190000000000.0,1,centroid,RES3A,RES3A
+574461093,574461093,COM,COM4,177990.619,177990.619,3.8647475242614746,-79.92826226,32.77651931,450190000000000.0,1,centroid,COM4,COM4
+574476710,574476710,RES,RES1-1SNB,201344.079,100672.039,3.514539957046509,-79.93241085,32.76996811,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB
+574476767,574476767,RES,RES1-2SNB,214533.891,107266.945,3.514539957046509,-79.93241085,32.76996811,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+574476775,574476775,RES,RES1-1SNB,206797.159,103398.579,3.514539957046509,-79.93241085,32.76996811,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB
+574476781,574476781,RES,RES1-1SNB,108849.157,54424.5787,3.514539957046509,-79.93241085,32.76996811,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB
+574476804,574476804,RES,RES1-1SNB,96964.414,48482.207,3.514539957046509,-79.93241085,32.76996811,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB
+574476806,574476806,RES,RES1-2SNB,214533.891,107266.945,3.514539957046509,-79.93241085,32.76996811,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+574486936,574486936,RES,RES1-2SNB,248128.867,124064.433,3.514539957046509,-79.93068697,32.77123266,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
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+573779319,573779319,RES,RES3A,471108.706,235554.353,1.94492769241333,-79.92765306,32.77323472,450190000000000.0,1,centroid,RES3A,RES3A
+573779335,573779335,RES,RES1-1SNB,256055.142,128027.571,2.9407148361206055,-79.92764858,32.77351557,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB
+573779357,573779357,RES,RES1-3SNB,380946.441,190473.22,3.1821353435516357,-79.92840483,32.77411255,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB
+573779369,573779369,RES,RES1-2SNB,374450.35,187225.175,3.6251838207244873,-79.92751869,32.77433957,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+573779385,573779385,RES,RES3A,581713.203,290856.601,3.8339977264404297,-79.92749568,32.77453773,450190000000000.0,1,centroid,RES3A,RES3A
+573779415,573779415,RES,RES1-3SNB,431428.087,215714.043,3.9629554748535156,-79.92743299,32.77502193,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB
+573782417,573782417,RES,RES1-3SNB,363979.656,181989.828,2.695188283920288,-79.92933255,32.77226794,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB
+573782425,573782425,RES,RES1-2SNB,366466.821,183233.41,2.6735990047454834,-79.92929796,32.77237201,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+573782443,573782443,RES,RES3A,543712.387,271856.193,1.4465175867080688,-79.92831106,32.77264062,450190000000000.0,1,centroid,RES3A,RES3A
+573782449,573782449,RES,RES1-2SNB,472033.925,236016.962,1.6663191318511963,-79.92864422,32.77279569,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+573782516,573782516,RES,RES1-3SNB,451364.729,225682.364,3.677116870880127,-79.92865918,32.7750572,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB
+573782540,573782540,RES,RES1-2SNB,278796.48,139398.24,3.776658535003662,-79.9284404,32.77566618,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+573782554,573782554,COM,COM4,426627.449,426627.449,4.0352888107299805,-79.92852646,32.77599619,450190000000000.0,1,centroid,COM4,COM4
+573782586,573782586,RES,RES3A,421325.574,210662.787,2.4651119709014893,-79.93017261,32.77290957,450190000000000.0,1,centroid,RES3A,RES3A
+573783169,573783169,RES,RES3A,512267.747,256133.873,3.5805304050445557,-79.92919073,32.77440531,450190000000000.0,1,centroid,RES3A,RES3A
+573783179,573783179,RES,RES3A,606289.125,303144.562,3.7244884967803955,-79.92928354,32.77452729,450190000000000.0,1,centroid,RES3A,RES3A
+573783187,573783187,RES,RES1-2SNB,386160.703,193080.351,3.2870843410491943,-79.93016895,32.77463032,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+573783189,573783189,COM,COM3,1640307.87,1640307.87,3.3157706260681152,-79.93028267,32.7746644,450190000000000.0,1,centroid,COM3,COM3
+573783199,573783199,RES,RES1-2SNB,551102.006,275551.003,3.9116175174713135,-79.92921208,32.77472151,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+573783443,573783443,RES,RES1-2SNB,311772.137,155886.068,2.8042871952056885,-79.93015806,32.77191319,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+573783445,573783445,RES,RES1-2SNB,680463.744,340231.872,3.1074347496032715,-79.92996727,32.77182933,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+573827136,573827136,COM,COM5,2936504.86,2936504.86,4.212172031402588,-79.92746902,32.77712292,450190000000000.0,1,centroid,COM5,COM5
+573827731,573827731,COM,COM1,194515.404,194515.404,3.586665630340576,-79.92959283,32.77679411,450190000000000.0,1,centroid,COM1,COM1
+573827733,573827733,COM,COM3,145886.553,145886.553,3.586665630340576,-79.92959283,32.77679411,450190000000000.0,1,centroid,COM3,COM3
+573827735,573827735,COM,COM4,81048.0852,81048.0852,3.586665630340576,-79.92959283,32.77679411,450190000000000.0,1,centroid,COM4,COM4
+573827754,573827754,COM,COM4,95724.6697,95724.6697,3.541720390319824,-79.93017988,32.77681959,450190000000000.0,1,centroid,COM4,COM4
+574267735,574267735,RES,RES1-1SNB,381021.716,190510.858,3.9229440689086914,-79.92744398,32.77522114,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB
+574267737,574267737,RES,RES1-1SNB,273325.341,136662.67,3.8988091945648193,-79.92811401,32.77515289,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB
+574267739,574267739,RES,RES1-2SNB,413187.234,206593.617,4.1990251541137695,-79.92800828,32.77526518,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+574267749,574267749,RES,RES3A,491247.521,245623.76,4.229095935821533,-79.92788864,32.77557876,450190000000000.0,1,centroid,RES3A,RES3A
+574267976,574267976,RES,RES1-2SNB,385155.033,192577.516,4.496554851531982,-79.92794047,32.77603579,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+574268005,574268005,RES,RES1-1SNB,257591.82,128795.91,4.123398780822754,-79.92772127,32.77604828,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB
+574346733,574346733,RES,RES1-2SNB,345561.158,172780.579,4.085295677185059,-79.92987341,32.77497559,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+574346737,574346737,COM,COM3,128711.316,128711.316,4.025929927825928,-79.92934753,32.77496936,450190000000000.0,1,centroid,COM3,COM3
+574346738,574346738,RES,RES1-3SNB,569110.405,284555.202,3.6682686805725098,-79.92997655,32.77497829,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB
+574346740,574346740,RES,RES1-2SNB,272662.957,136331.478,3.8636796474456787,-79.92924438,32.77514479,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+574346744,574346744,PUB,REL1,1281715.19,1281715.19,3.897091865539551,-79.92929333,32.77533709,450190000000000.0,1,centroid,REL1,REL1
+574346753,574346753,RES,RES1-2SNB,311698.604,155849.302,4.008721828460693,-79.92981335,32.77577007,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+574372447,574372447,COM,COM1,227020.051,227020.051,3.952767848968506,-79.92900036,32.77610772,450190000000000.0,1,centroid,COM1,COM1
+574372448,574372448,IND,IND2,134849.922,202274.884,3.952767848968506,-79.92900036,32.77610772,450190000000000.0,1,centroid,IND2,IND2
+574372473,574372473,COM,COM1,424486.831,424486.831,3.7199630737304688,-79.92881546,32.7764473,450190000000000.0,1,centroid,COM1,COM1
+574372533,574372533,COM,COM7,393804.35,590706.525,3.269709587097168,-79.93032633,32.7759983,450190000000000.0,1,centroid,COM7,COM7
+574372543,574372543,RES,RES3A,338190.212,169095.106,3.756402015686035,-79.92938805,32.77623669,450190000000000.0,1,centroid,RES3A,RES3A
+574372567,574372567,COM,COM8,284295.791,284295.791,4.172937870025635,-79.92958905,32.77638171,450190000000000.0,1,centroid,COM8,COM8
+574421681,574421681,COM,COM8,778149.815,778149.815,4.172937870025635,-79.92846361,32.77697303,450190000000000.0,1,centroid,COM8,COM8
+574421683,574421683,COM,COM1,360197.146,360197.146,4.172937870025635,-79.92846361,32.77697303,450190000000000.0,1,centroid,COM1,COM1
+574431298,574431298,COM,COM4,1338267.91,1338267.91,2.944330930709839,-79.92646291,32.77728981,450190000000000.0,1,centroid,COM4,COM4
+574461065,574461065,RES,RES3A,266985.928,133492.964,3.8383705615997314,-79.9297417,32.77681823,450190000000000.0,1,centroid,RES3A,RES3A
+574461073,574461073,COM,COM4,177990.619,177990.619,3.720332622528076,-79.92950828,32.77642489,450190000000000.0,1,centroid,COM4,COM4
+574461077,574461077,RES,RES3A,273660.577,136830.288,3.6558895111083984,-79.92903083,32.7763684,450190000000000.0,1,centroid,RES3A,RES3A
+574461079,574461079,COM,COM4,177990.619,177990.619,3.8224174976348877,-79.92860499,32.77648743,450190000000000.0,1,centroid,COM4,COM4
+574488771,574488771,RES,RES1-1SNB,222806.066,111403.033,3.8611080646514893,-79.92896336,32.77597596,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB
+574488793,574488793,RES,RES1-2SNB,216903.661,108451.83,3.414585590362549,-79.92736548,32.77640732,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+574488804,574488804,RES,RES1-2SNB,292527.098,146263.549,4.070784091949463,-79.92816916,32.77627743,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+574488806,574488806,RES,RES1-3SNB,358700.469,179350.234,4.070784091949463,-79.92824222,32.77627542,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB
+574488816,574488816,RES,RES1-1SNB,221789.878,110894.939,1.8417584896087646,-79.92574958,32.77633187,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB
+574488819,574488819,RES,RES1-3SNB,416126.362,208063.181,1.7718870639801025,-79.9267068,32.77616474,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB
+574488830,574488830,RES,RES1-1SNB,182232.731,91116.3657,3.8453402519226074,-79.92898379,32.77518059,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB
+574488834,574488834,RES,RES1-3SNB,384398.576,192199.288,3.8642210960388184,-79.92951787,32.77470617,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB
+574488837,574488837,RES,RES3A,480189.335,240094.667,3.8105275630950928,-79.92891,32.77509288,450190000000000.0,1,centroid,RES3A,RES3A
+574488848,574488848,RES,RES1-2SNB,322415.251,161207.625,3.953150987625122,-79.92763108,32.77497895,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+574488851,574488851,RES,RES1-2SNB,222897.785,111448.892,3.0513789653778076,-79.92671547,32.7755542,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB
+574488874,574488874,RES,RES3A,480189.335,240094.667,2.6741490364074707,-79.92914469,32.77176507,450190000000000.0,1,centroid,RES3A,RES3A
diff --git a/examples/data/update_ground_elevation/test_update_ground_elevation/settings.toml b/examples/data/update_ground_elevation/test_update_ground_elevation/settings.toml
new file mode 100644
index 00000000..c9018a8b
--- /dev/null
+++ b/examples/data/update_ground_elevation/test_update_ground_elevation/settings.toml
@@ -0,0 +1,22 @@
+[global]
+crs = "epsg:4326"
+
+[output]
+path = "output"
+
+[output.csv]
+name = "output.csv"
+
+[output.geom]
+name1 = "spatial.gpkg"
+
+[vulnerability]
+file = "./vulnerability/vulnerability_curves.csv"
+scale = 0.1
+
+[exposure.csv]
+file = "./exposure/exposure.csv"
+
+[exposure.geom]
+crs = "EPSG:4326"
+file1 = "./exposure/building_points.gpkg"
diff --git a/examples/data/update_ground_elevation/test_update_ground_elevation/vulnerability/vulnerability_curves.csv b/examples/data/update_ground_elevation/test_update_ground_elevation/vulnerability/vulnerability_curves.csv
new file mode 100644
index 00000000..81d37acf
--- /dev/null
+++ b/examples/data/update_ground_elevation/test_update_ground_elevation/vulnerability/vulnerability_curves.csv
@@ -0,0 +1,32 @@
+#UNIT=feet
+#METHOD,max,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean
+water depth [feet],AGR1,COM1,COM10,COM2,COM3,COM4,COM5,COM6,COM7,COM8,COM9,EDU1,GOV1,GOV2,IND1,IND2,IND3,IND4,IND5,IND6,REL1,RES1-1SNB,RES1-1SWB,RES1-2SNB,RES1-2SWB,RES1-3SNB,RES1-3SWB,RES2,RES3A,RES3B,RES3C,RES3D,RES3E,RES3F,RES4,RES5,RES6
+-4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,0.0,0.04,0.0,0.03,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,0.0,0.04,0.0,0.03,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,0.0,0.08,0.0,0.06,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.11,0.0,0.14,0.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+0.0,0.0,0.01,0.0,0.0,0.0,0.02,0.0,0.0,0.02,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.18,0.17,0.11,0.19,0.05,0.12,0.11,0.12,0.12,0.12,0.12,0.12,0.12,0.0,0.0,0.0
+1.0,0.06,0.09,0.03,0.05,0.09,0.11,0.11,0.0,0.11,0.09,0.02,0.05,0.05,0.07,0.1,0.09,0.13,0.1,0.13,0.22,0.1,0.22,0.21,0.12,0.21,0.08,0.14,0.44,0.25,0.25,0.25,0.25,0.25,0.25,0.03,0.07,0.07
+2.0,0.11,0.14,0.05,0.08,0.12,0.16,0.11,0.0,0.12,0.11,0.04,0.07,0.08,0.1,0.12,0.14,0.14,0.14,0.14,0.31,0.11,0.25,0.29,0.14,0.26,0.12,0.2,0.63,0.5,0.5,0.5,0.5,0.5,0.5,0.05,0.1,0.1
+3.0,0.15,0.16,0.06,0.11,0.13,0.22,0.12,0.2,0.13,0.12,0.05,0.09,0.13,0.11,0.15,0.17,0.19,0.18,0.19,0.37,0.11,0.28,0.34,0.18,0.29,0.17,0.25,0.73,0.75,0.75,0.75,0.75,0.75,0.75,0.06,0.14,0.14
+4.0,0.19,0.18,0.07,0.13,0.16,0.28,0.13,0.25,0.14,0.14,0.05,0.09,0.14,0.12,0.19,0.22,0.22,0.22,0.22,0.43,0.12,0.3,0.38,0.2,0.34,0.19,0.31,0.78,1.0,1.0,1.0,1.0,1.0,1.0,0.07,0.15,0.15
+5.0,0.25,0.2,0.08,0.16,0.19,0.35,0.15,0.3,0.16,0.16,0.05,0.1,0.14,0.15,0.22,0.26,0.25,0.26,0.25,0.47,0.12,0.31,0.43,0.22,0.39,0.22,0.36,0.79,1.0,1.0,1.0,1.0,1.0,1.0,0.09,0.15,0.15
+6.0,0.3,0.23,0.1,0.19,0.22,0.38,0.17,0.35,0.17,0.18,0.06,0.11,0.15,0.17,0.26,0.3,0.28,0.34,0.28,0.5,0.13,0.4,0.5,0.24,0.44,0.24,0.38,0.81,1.0,1.0,1.0,1.0,1.0,1.0,0.12,0.16,0.16
+7.0,0.35,0.26,0.13,0.22,0.25,0.41,0.19,0.4,0.18,0.2,0.08,0.13,0.17,0.2,0.3,0.32,0.3,0.41,0.3,0.54,0.14,0.43,0.5,0.26,0.5,0.25,0.41,0.82,1.0,1.0,1.0,1.0,1.0,1.0,0.14,0.18,0.18
+8.0,0.41,0.3,0.17,0.25,0.28,0.44,0.22,0.43,0.2,0.22,0.1,0.15,0.19,0.23,0.35,0.35,0.33,0.42,0.33,0.57,0.14,0.43,0.54,0.3,0.55,0.3,0.44,0.83,1.0,1.0,1.0,1.0,1.0,1.0,0.18,0.2,0.2
+9.0,0.46,0.34,0.21,0.29,0.32,0.47,0.24,0.47,0.22,0.26,0.12,0.17,0.22,0.27,0.39,0.37,0.34,0.42,0.34,0.61,0.15,0.45,0.55,0.34,0.57,0.35,0.48,0.84,1.0,1.0,1.0,1.0,1.0,1.0,0.21,0.23,0.23
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diff --git a/examples/data/update_ground_floor_height/fake_elevation_certificates.gpkg b/examples/data/update_ground_floor_height/fake_elevation_certificates.gpkg
new file mode 100644
index 00000000..0315d43c
Binary files /dev/null and b/examples/data/update_ground_floor_height/fake_elevation_certificates.gpkg differ
diff --git a/examples/data/update_ground_floor_height/fake_update_ground_floor_height.gpkg b/examples/data/update_ground_floor_height/fake_update_ground_floor_height.gpkg
new file mode 100644
index 00000000..3bdab74e
Binary files /dev/null and b/examples/data/update_ground_floor_height/fake_update_ground_floor_height.gpkg differ
diff --git a/examples/data/update_ground_floor_height/fiat_model/exposure/building_points.gpkg b/examples/data/update_ground_floor_height/fiat_model/exposure/building_points.gpkg
new file mode 100644
index 00000000..eb21a7ca
Binary files /dev/null and b/examples/data/update_ground_floor_height/fiat_model/exposure/building_points.gpkg differ
diff --git a/examples/data/update_ground_floor_height/fiat_model/exposure/exposure.csv b/examples/data/update_ground_floor_height/fiat_model/exposure/exposure.csv
new file mode 100644
index 00000000..e25d0547
--- /dev/null
+++ b/examples/data/update_ground_floor_height/fiat_model/exposure/exposure.csv
@@ -0,0 +1,1995 @@
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+574421683,574421683,COM,COM1,360197.146,360197.146,13.54457556,-79.92846361,32.77697303,4.5019E+14,1,centroid,COM1,COM1
+574431298,574431298,COM,COM4,1338267.91,1338267.91,10.1165989,-79.92646291,32.77728981,4.5019E+14,1,centroid,COM4,COM4
+574461065,574461065,RES,RES3A,266985.928,133492.964,12.13528704,-79.9297417,32.77681823,4.5019E+14,1,centroid,RES3A,RES3A
+574461073,574461073,COM,COM4,177990.619,177990.619,12.27511783,-79.92950828,32.77642489,4.5019E+14,1,centroid,COM4,COM4
+574461077,574461077,RES,RES3A,273660.577,136830.288,12.4406709,-79.92903083,32.7763684,4.5019E+14,1,centroid,RES3A,RES3A
+574461079,574461079,COM,COM4,177990.619,177990.619,12.86096032,-79.92860499,32.77648743,4.5019E+14,1,centroid,COM4,COM4
+574488771,574488771,RES,RES1-1SNB,222806.066,111403.033,13.26134397,-79.92896336,32.77597596,4.5019E+14,1,centroid,RES1-1SNB,RES1-1SNB
+574488793,574488793,RES,RES1-2SNB,216903.661,108451.83,12.29002837,-79.92736548,32.77640732,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+574488804,574488804,RES,RES1-2SNB,292527.098,146263.549,13.21022478,-79.92816916,32.77627743,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+574488806,574488806,RES,RES1-3SNB,358700.469,179350.234,13.36457265,-79.92824222,32.77627542,4.5019E+14,1,centroid,RES1-3SNB,RES1-3SNB
+574488816,574488816,RES,RES1-1SNB,221789.878,110894.939,6.334944159,-79.92574958,32.77633187,4.5019E+14,1,centroid,RES1-1SNB,RES1-1SNB
+574488819,574488819,RES,RES1-3SNB,416126.362,208063.181,6.052678219,-79.9267068,32.77616474,4.5019E+14,1,centroid,RES1-3SNB,RES1-3SNB
+574488830,574488830,RES,RES1-1SNB,182232.731,91116.3657,12.69306217,-79.92898379,32.77518059,4.5019E+14,1,centroid,RES1-1SNB,RES1-1SNB
+574488834,574488834,RES,RES1-3SNB,384398.576,192199.288,12.90637406,-79.92951787,32.77470617,4.5019E+14,1,centroid,RES1-3SNB,RES1-3SNB
+574488837,574488837,RES,RES3A,480189.335,240094.667,13.25912718,-79.92891,32.77509288,4.5019E+14,1,centroid,RES3A,RES3A
+574488848,574488848,RES,RES1-2SNB,322415.251,161207.625,13.14323291,-79.92763108,32.77497895,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+574488851,574488851,RES,RES1-2SNB,222897.785,111448.892,10.150554,-79.92671547,32.7755542,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+574488874,574488874,RES,RES3A,480189.335,240094.667,8.900641428,-79.92914469,32.77176507,4.5019E+14,1,centroid,RES3A,RES3A
diff --git a/examples/data/update_ground_floor_height/fiat_model/exposure/region.gpkg b/examples/data/update_ground_floor_height/fiat_model/exposure/region.gpkg
new file mode 100644
index 00000000..2c06a9ef
Binary files /dev/null and b/examples/data/update_ground_floor_height/fiat_model/exposure/region.gpkg differ
diff --git a/examples/data/update_ground_floor_height/fiat_model/settings.toml b/examples/data/update_ground_floor_height/fiat_model/settings.toml
new file mode 100644
index 00000000..c9018a8b
--- /dev/null
+++ b/examples/data/update_ground_floor_height/fiat_model/settings.toml
@@ -0,0 +1,22 @@
+[global]
+crs = "epsg:4326"
+
+[output]
+path = "output"
+
+[output.csv]
+name = "output.csv"
+
+[output.geom]
+name1 = "spatial.gpkg"
+
+[vulnerability]
+file = "./vulnerability/vulnerability_curves.csv"
+scale = 0.1
+
+[exposure.csv]
+file = "./exposure/exposure.csv"
+
+[exposure.geom]
+crs = "EPSG:4326"
+file1 = "./exposure/building_points.gpkg"
diff --git a/examples/data/update_ground_floor_height/fiat_model/vulnerability/vulnerability_curves.csv b/examples/data/update_ground_floor_height/fiat_model/vulnerability/vulnerability_curves.csv
new file mode 100644
index 00000000..81d37acf
--- /dev/null
+++ b/examples/data/update_ground_floor_height/fiat_model/vulnerability/vulnerability_curves.csv
@@ -0,0 +1,32 @@
+#UNIT=feet
+#METHOD,max,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean
+water depth [feet],AGR1,COM1,COM10,COM2,COM3,COM4,COM5,COM6,COM7,COM8,COM9,EDU1,GOV1,GOV2,IND1,IND2,IND3,IND4,IND5,IND6,REL1,RES1-1SNB,RES1-1SWB,RES1-2SNB,RES1-2SWB,RES1-3SNB,RES1-3SWB,RES2,RES3A,RES3B,RES3C,RES3D,RES3E,RES3F,RES4,RES5,RES6
+-4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,0.0,0.04,0.0,0.03,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,0.0,0.04,0.0,0.03,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,0.0,0.08,0.0,0.06,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.11,0.0,0.14,0.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+0.0,0.0,0.01,0.0,0.0,0.0,0.02,0.0,0.0,0.02,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.18,0.17,0.11,0.19,0.05,0.12,0.11,0.12,0.12,0.12,0.12,0.12,0.12,0.0,0.0,0.0
+1.0,0.06,0.09,0.03,0.05,0.09,0.11,0.11,0.0,0.11,0.09,0.02,0.05,0.05,0.07,0.1,0.09,0.13,0.1,0.13,0.22,0.1,0.22,0.21,0.12,0.21,0.08,0.14,0.44,0.25,0.25,0.25,0.25,0.25,0.25,0.03,0.07,0.07
+2.0,0.11,0.14,0.05,0.08,0.12,0.16,0.11,0.0,0.12,0.11,0.04,0.07,0.08,0.1,0.12,0.14,0.14,0.14,0.14,0.31,0.11,0.25,0.29,0.14,0.26,0.12,0.2,0.63,0.5,0.5,0.5,0.5,0.5,0.5,0.05,0.1,0.1
+3.0,0.15,0.16,0.06,0.11,0.13,0.22,0.12,0.2,0.13,0.12,0.05,0.09,0.13,0.11,0.15,0.17,0.19,0.18,0.19,0.37,0.11,0.28,0.34,0.18,0.29,0.17,0.25,0.73,0.75,0.75,0.75,0.75,0.75,0.75,0.06,0.14,0.14
+4.0,0.19,0.18,0.07,0.13,0.16,0.28,0.13,0.25,0.14,0.14,0.05,0.09,0.14,0.12,0.19,0.22,0.22,0.22,0.22,0.43,0.12,0.3,0.38,0.2,0.34,0.19,0.31,0.78,1.0,1.0,1.0,1.0,1.0,1.0,0.07,0.15,0.15
+5.0,0.25,0.2,0.08,0.16,0.19,0.35,0.15,0.3,0.16,0.16,0.05,0.1,0.14,0.15,0.22,0.26,0.25,0.26,0.25,0.47,0.12,0.31,0.43,0.22,0.39,0.22,0.36,0.79,1.0,1.0,1.0,1.0,1.0,1.0,0.09,0.15,0.15
+6.0,0.3,0.23,0.1,0.19,0.22,0.38,0.17,0.35,0.17,0.18,0.06,0.11,0.15,0.17,0.26,0.3,0.28,0.34,0.28,0.5,0.13,0.4,0.5,0.24,0.44,0.24,0.38,0.81,1.0,1.0,1.0,1.0,1.0,1.0,0.12,0.16,0.16
+7.0,0.35,0.26,0.13,0.22,0.25,0.41,0.19,0.4,0.18,0.2,0.08,0.13,0.17,0.2,0.3,0.32,0.3,0.41,0.3,0.54,0.14,0.43,0.5,0.26,0.5,0.25,0.41,0.82,1.0,1.0,1.0,1.0,1.0,1.0,0.14,0.18,0.18
+8.0,0.41,0.3,0.17,0.25,0.28,0.44,0.22,0.43,0.2,0.22,0.1,0.15,0.19,0.23,0.35,0.35,0.33,0.42,0.33,0.57,0.14,0.43,0.54,0.3,0.55,0.3,0.44,0.83,1.0,1.0,1.0,1.0,1.0,1.0,0.18,0.2,0.2
+9.0,0.46,0.34,0.21,0.29,0.32,0.47,0.24,0.47,0.22,0.26,0.12,0.17,0.22,0.27,0.39,0.37,0.34,0.42,0.34,0.61,0.15,0.45,0.55,0.34,0.57,0.35,0.48,0.84,1.0,1.0,1.0,1.0,1.0,1.0,0.21,0.23,0.23
+10.0,0.51,0.38,0.25,0.32,0.35,0.5,0.28,0.5,0.24,0.29,0.15,0.2,0.26,0.31,0.42,0.39,0.36,0.45,0.36,0.63,0.17,0.46,0.55,0.38,0.59,0.38,0.5,0.85,1.0,1.0,1.0,1.0,1.0,1.0,0.26,0.26,0.26
+11.0,0.57,0.42,0.3,0.37,0.39,0.54,0.31,0.53,0.27,0.33,0.2,0.24,0.31,0.35,0.48,0.43,0.39,0.47,0.39,0.64,0.19,0.47,0.57,0.39,0.61,0.39,0.52,0.86,1.0,1.0,1.0,1.0,1.0,1.0,0.31,0.3,0.3
+12.0,0.63,0.47,0.35,0.41,0.43,0.57,0.34,0.55,0.3,0.37,0.24,0.28,0.37,0.4,0.5,0.46,0.4,0.49,0.4,0.65,0.24,0.47,0.58,0.4,0.63,0.4,0.54,0.88,1.0,1.0,1.0,1.0,1.0,1.0,0.36,0.34,0.34
+13.0,0.7,0.51,0.41,0.45,0.47,0.59,0.37,0.57,0.34,0.41,0.29,0.33,0.44,0.44,0.51,0.48,0.42,0.5,0.42,0.67,0.3,0.49,0.6,0.42,0.65,0.42,0.56,0.89,1.0,1.0,1.0,1.0,1.0,1.0,0.41,0.38,0.38
+14.0,0.75,0.55,0.47,0.49,0.5,0.62,0.4,0.6,0.37,0.45,0.35,0.39,0.51,0.48,0.53,0.5,0.42,0.5,0.42,0.68,0.38,0.5,0.62,0.43,0.66,0.43,0.57,0.9,1.0,1.0,1.0,1.0,1.0,1.0,0.46,0.42,0.42
+15.0,0.79,0.58,0.52,0.52,0.54,0.66,0.44,0.6,0.41,0.5,0.42,0.45,0.59,0.52,0.54,0.51,0.43,0.5,0.43,0.69,0.45,0.5,0.63,0.44,0.68,0.44,0.59,0.91,1.0,1.0,1.0,1.0,1.0,1.0,0.5,0.47,0.47
+16.0,0.82,0.61,0.58,0.55,0.57,0.68,0.48,0.6,0.44,0.53,0.49,0.52,0.65,0.56,0.55,0.54,0.43,0.5,0.43,0.7,0.52,0.5,0.65,0.45,0.69,0.45,0.61,0.92,1.0,1.0,1.0,1.0,1.0,1.0,0.54,0.52,0.52
+17.0,0.84,0.64,0.65,0.58,0.61,0.7,0.51,0.6,0.48,0.57,0.56,0.59,0.7,0.6,0.55,0.55,0.44,0.5,0.44,0.71,0.58,0.51,0.67,0.47,0.71,0.47,0.63,0.94,1.0,1.0,1.0,1.0,1.0,1.0,0.58,0.57,0.57
+18.0,0.87,0.67,0.71,0.61,0.64,0.72,0.55,0.6,0.51,0.6,0.62,0.64,0.74,0.64,0.56,0.57,0.44,0.5,0.44,0.72,0.64,0.51,0.69,0.48,0.72,0.48,0.65,0.95,1.0,1.0,1.0,1.0,1.0,1.0,0.62,0.62,0.62
+19.0,0.89,0.69,0.76,0.63,0.68,0.74,0.59,0.6,0.54,0.63,0.68,0.69,0.79,0.68,0.56,0.59,0.44,0.5,0.44,0.73,0.69,0.52,0.7,0.49,0.74,0.49,0.67,0.96,1.0,1.0,1.0,1.0,1.0,1.0,0.66,0.67,0.67
+20.0,0.9,0.71,0.81,0.66,0.71,0.76,0.63,0.6,0.56,0.66,0.74,0.74,0.83,0.72,0.57,0.6,0.44,0.5,0.44,0.74,0.74,0.52,0.72,0.5,0.75,0.5,0.69,0.97,1.0,1.0,1.0,1.0,1.0,1.0,0.7,0.72,0.72
+21.0,0.92,0.74,0.86,0.68,0.75,0.77,0.67,0.6,0.59,0.69,0.8,0.79,0.87,0.76,0.57,0.62,0.44,0.5,0.44,0.75,0.78,0.53,0.74,0.52,0.77,0.52,0.71,0.98,1.0,1.0,1.0,1.0,1.0,1.0,0.74,0.77,0.77
+22.0,0.93,0.76,0.91,0.7,0.78,0.78,0.71,0.6,0.61,0.73,0.86,0.84,0.91,0.8,0.57,0.63,0.45,0.5,0.45,0.76,0.82,0.53,0.76,0.53,0.79,0.53,0.73,0.99,1.0,1.0,1.0,1.0,1.0,1.0,0.78,0.82,0.82
+23.0,0.95,0.78,0.96,0.71,0.82,0.79,0.75,0.6,0.64,0.76,0.92,0.89,0.95,0.84,0.58,0.65,0.45,0.5,0.45,0.76,0.85,0.54,0.77,0.54,0.8,0.54,0.75,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.82,0.87,0.87
+24.0,0.96,0.8,1.0,0.73,0.85,0.8,0.79,0.6,0.66,0.78,0.98,0.94,0.98,0.88,0.58,0.66,0.45,0.5,0.45,0.77,0.88,0.54,0.79,0.56,0.82,0.56,0.77,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.86,0.92,0.92
diff --git a/examples/data/update_ground_floor_height/fiat_model/vulnerability/vulnerability_identifiers.csv b/examples/data/update_ground_floor_height/fiat_model/vulnerability/vulnerability_identifiers.csv
new file mode 100644
index 00000000..7d7fbb72
--- /dev/null
+++ b/examples/data/update_ground_floor_height/fiat_model/vulnerability/vulnerability_identifiers.csv
@@ -0,0 +1,38 @@
+,Name,Link,BldgDmgFnID,Occupancy,Source,Description,full_identifier
+0,AGR1,AGR1,616,AGR1 ,USACE - Galveston,"Average Agriculture, structure","616AGR1USACE-GalvestonAverageAgriculture,structure"
+1,COM1,COM1,217,COM1 ,USACE - Galveston,"Average Retail, Structure","217COM1USACE-GalvestonAverageRetail,Structure"
+2,COM10,COM10,543,COM10,USACE - Galveston,"Garage, structure","543COM10USACE-GalvestonGarage,structure"
+3,COM2,COM2,341,COM2 ,USACE - Galveston,"Average wholesale, Structure","341COM2USACE-GalvestonAveragewholesale,Structure"
+4,COM3,COM3,375,COM3 ,USACE - Galveston,"Average Personal & Repair Services, Structure","375COM3USACE-GalvestonAveragePersonal&RepairServices,Structure"
+5,COM4,COM4,431,COM4 ,USACE - Galveston,"Average Prof/Tech Services, Structure","431COM4USACE-GalvestonAverageProf/TechServices,Structure"
+6,COM5,COM5,467,COM5 ,USACE - Galveston,"Bank, Structure","467COM5USACE-GalvestonBank,Structure"
+7,COM6,COM6,474,COM6 ,USACE - Galveston,"Hospital, Structure","474COM6USACE-GalvestonHospital,Structure"
+8,COM7,COM7,475,COM7 ,USACE - Galveston,"Average Medical Office, Structure","475COM7USACE-GalvestonAverageMedicalOffice,Structure"
+9,COM8,COM8,493,COM8 ,USACE - Galveston,"Average Entertainment/Recreation, Structure","493COM8USACE-GalvestonAverageEntertainment/Recreation,Structure"
+10,COM9,COM9,532,COM9 ,USACE - Galveston,"Average theatre, Structure","532COM9USACE-GalvestonAveragetheatre,Structure"
+11,EDU1,EDU1,643,EDU1 ,USACE - Galveston,"Average school, structure","643EDU1USACE-GalvestonAverageschool,structure"
+12,GOV1,GOV1,631,GOV1 ,USACE - Galveston,"Average government services, structure","631GOV1USACE-GalvestonAveragegovernmentservices,structure"
+13,GOV2,GOV2,640,GOV2 ,USACE - Galveston,"Average emergency response, Structure","640GOV2USACE-GalvestonAverageemergencyresponse,Structure"
+14,IND1,IND1,545,IND1 ,USACE - Galveston,"Average heavy industrial, Structure","545IND1USACE-GalvestonAverageheavyindustrial,Structure"
+15,IND2,IND2,559,IND2 ,USACE - Galveston,"Average light industrial, structure","559IND2USACE-GalvestonAveragelightindustrial,structure"
+16,IND3,IND3,575,IND3 ,USACE - Galveston,"Average Food/Drug/Chem, Structure","575IND3USACE-GalvestonAverageFood/Drug/Chem,Structure"
+17,IND4,IND4,586,IND4 ,USACE - Galveston,"Average Metals/Minerals processing, structure","586IND4USACE-GalvestonAverageMetals/Mineralsprocessing,structure"
+18,IND5,IND5,591,IND5 ,USACE - Galveston,"Average High Technology, structure","591IND5USACE-GalvestonAverageHighTechnology,structure"
+19,IND6,IND6,592,IND6 ,USACE - Galveston,"Average Construction, structure","592IND6USACE-GalvestonAverageConstruction,structure"
+20,REL1,REL1,624,REL1 ,USACE - Galveston,"Church, structure","624REL1USACE-GalvestonChurch,structure"
+21,RES1-1SNB,RES1-1SNB,105,RES1 ,FIA,"one floor, no basement, Structure, A-Zone","105RES1FIAonefloor,nobasement,Structure,A-Zone"
+22,RES1-1SWB,RES1-1SWB,106,RES1 ,FIA (MOD.),"one floor, w/ basement, Structure, A-Zone","106RES1FIA(MOD.)onefloor,w/basement,Structure,A-Zone"
+23,RES1-2SNB,RES1-2SNB,107,RES1 ,FIA,"two floors, no basement, Structure, A-Zone","107RES1FIAtwofloors,nobasement,Structure,A-Zone"
+24,RES1-2SWB,RES1-2SWB,108,RES1 ,FIA (MOD.),"two floors, w/ basement, Structure, A-Zone","108RES1FIA(MOD.)twofloors,w/basement,Structure,A-Zone"
+25,RES1-3SNB,RES1-3SNB,109,RES1 ,FIA,"three or more floors, no basement, Structure, A-Zone","109RES1FIAthreeormorefloors,nobasement,Structure,A-Zone"
+26,RES1-3SWB,RES1-3SWB,110,RES1 ,FIA (MOD.),"three or more floors, w/ basement, Structure, A-Zone","110RES1FIA(MOD.)threeormorefloors,w/basement,Structure,A-Zone"
+27,RES2,RES2,189,RES2 ,FIA,"Mobile home, structure, A-Zone","189RES2FIAMobilehome,structure,A-Zone"
+28,RES3A,RES3A,659,RES3A,BCAR - Jan 2011,"1to2 Stories, slab - no basement, Coastal A or V Zone","659RES3ABCAR-Jan20111to2Stories,slab-nobasement,CoastalAorVZone"
+29,RES3B,RES3B,660,RES3B,BCAR - Jan 2011,"1to2 Stories, slab - no basement, Coastal A or V Zone","660RES3BBCAR-Jan20111to2Stories,slab-nobasement,CoastalAorVZone"
+30,RES3C,RES3C,660,RES3B,BCAR - Jan 2011,"1to2 Stories, slab - no basement, Coastal A or V Zone","660RES3BBCAR-Jan20111to2Stories,slab-nobasement,CoastalAorVZone"
+31,RES3D,RES3D,660,RES3B,BCAR - Jan 2011,"1to2 Stories, slab - no basement, Coastal A or V Zone","660RES3BBCAR-Jan20111to2Stories,slab-nobasement,CoastalAorVZone"
+32,RES3E,RES3E,660,RES3B,BCAR - Jan 2011,"1to2 Stories, slab - no basement, Coastal A or V Zone","660RES3BBCAR-Jan20111to2Stories,slab-nobasement,CoastalAorVZone"
+33,RES3F,RES3F,660,RES3B,BCAR - Jan 2011,"1to2 Stories, slab - no basement, Coastal A or V Zone","660RES3BBCAR-Jan20111to2Stories,slab-nobasement,CoastalAorVZone"
+34,RES4,RES4,209,RES4 ,USACE - Galveston,"Average Hotel & Motel, structure","209RES4USACE-GalvestonAverageHotel&Motel,structure"
+35,RES5,RES5,214,RES5 ,USACE - Galveston,"Average institutional dormitory, structure","214RES5USACE-GalvestonAverageinstitutionaldormitory,structure"
+36,RES6,RES6,215,RES6 ,USACE - Galveston,"Nursing Home, structure","215RES6USACE-GalvestonNursingHome,structure"
diff --git a/examples/data/update_ground_floor_height/test_update_ground_floor_height_points/exposure/building_points.gpkg b/examples/data/update_ground_floor_height/test_update_ground_floor_height_points/exposure/building_points.gpkg
new file mode 100644
index 00000000..b31d5c2a
Binary files /dev/null and b/examples/data/update_ground_floor_height/test_update_ground_floor_height_points/exposure/building_points.gpkg differ
diff --git a/examples/data/update_ground_floor_height/test_update_ground_floor_height_points/exposure/exposure.csv b/examples/data/update_ground_floor_height/test_update_ground_floor_height_points/exposure/exposure.csv
new file mode 100644
index 00000000..4858ab9b
--- /dev/null
+++ b/examples/data/update_ground_floor_height/test_update_ground_floor_height_points/exposure/exposure.csv
@@ -0,0 +1,1995 @@
+Object ID,Object Name,Primary Object Type,Secondary Object Type,Max Potential Damage: Structure,Max Potential Damage: Content,Ground Elevation,X Coordinate,Y Coordinate,Aggregation Label: Census Block,Ground Floor Height,Extraction Method,Damage Function: Structure,Damage Function: Content,geometry
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+573827735,573827735,COM,COM4,81048.0852,81048.0852,12.23108548,-79.92959283,32.77679411,450190000000000.0,1,centroid,COM4,COM4,POINT (-79.92959283 32.776794111)
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+574461079,574461079,COM,COM4,177990.619,177990.619,12.86096032,-79.92860499,32.77648743,450190000000000.0,1,centroid,COM4,COM4,POINT (-79.92860499 32.776487427)
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+574488874,574488874,RES,RES3A,480189.335,240094.667,8.900641428,-79.92914469,32.77176507,450190000000000.0,1,centroid,RES3A,RES3A,POINT (-79.92914469 32.77176507)
diff --git a/examples/data/update_ground_floor_height/test_update_ground_floor_height_points/settings.toml b/examples/data/update_ground_floor_height/test_update_ground_floor_height_points/settings.toml
new file mode 100644
index 00000000..c9018a8b
--- /dev/null
+++ b/examples/data/update_ground_floor_height/test_update_ground_floor_height_points/settings.toml
@@ -0,0 +1,22 @@
+[global]
+crs = "epsg:4326"
+
+[output]
+path = "output"
+
+[output.csv]
+name = "output.csv"
+
+[output.geom]
+name1 = "spatial.gpkg"
+
+[vulnerability]
+file = "./vulnerability/vulnerability_curves.csv"
+scale = 0.1
+
+[exposure.csv]
+file = "./exposure/exposure.csv"
+
+[exposure.geom]
+crs = "EPSG:4326"
+file1 = "./exposure/building_points.gpkg"
diff --git a/examples/data/update_ground_floor_height/test_update_ground_floor_height_points/vulnerability/vulnerability_curves.csv b/examples/data/update_ground_floor_height/test_update_ground_floor_height_points/vulnerability/vulnerability_curves.csv
new file mode 100644
index 00000000..81d37acf
--- /dev/null
+++ b/examples/data/update_ground_floor_height/test_update_ground_floor_height_points/vulnerability/vulnerability_curves.csv
@@ -0,0 +1,32 @@
+#UNIT=feet
+#METHOD,max,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean
+water depth [feet],AGR1,COM1,COM10,COM2,COM3,COM4,COM5,COM6,COM7,COM8,COM9,EDU1,GOV1,GOV2,IND1,IND2,IND3,IND4,IND5,IND6,REL1,RES1-1SNB,RES1-1SWB,RES1-2SNB,RES1-2SWB,RES1-3SNB,RES1-3SWB,RES2,RES3A,RES3B,RES3C,RES3D,RES3E,RES3F,RES4,RES5,RES6
+-4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,0.0,0.04,0.0,0.03,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,0.0,0.04,0.0,0.03,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,0.0,0.08,0.0,0.06,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.11,0.0,0.14,0.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+0.0,0.0,0.01,0.0,0.0,0.0,0.02,0.0,0.0,0.02,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.18,0.17,0.11,0.19,0.05,0.12,0.11,0.12,0.12,0.12,0.12,0.12,0.12,0.0,0.0,0.0
+1.0,0.06,0.09,0.03,0.05,0.09,0.11,0.11,0.0,0.11,0.09,0.02,0.05,0.05,0.07,0.1,0.09,0.13,0.1,0.13,0.22,0.1,0.22,0.21,0.12,0.21,0.08,0.14,0.44,0.25,0.25,0.25,0.25,0.25,0.25,0.03,0.07,0.07
+2.0,0.11,0.14,0.05,0.08,0.12,0.16,0.11,0.0,0.12,0.11,0.04,0.07,0.08,0.1,0.12,0.14,0.14,0.14,0.14,0.31,0.11,0.25,0.29,0.14,0.26,0.12,0.2,0.63,0.5,0.5,0.5,0.5,0.5,0.5,0.05,0.1,0.1
+3.0,0.15,0.16,0.06,0.11,0.13,0.22,0.12,0.2,0.13,0.12,0.05,0.09,0.13,0.11,0.15,0.17,0.19,0.18,0.19,0.37,0.11,0.28,0.34,0.18,0.29,0.17,0.25,0.73,0.75,0.75,0.75,0.75,0.75,0.75,0.06,0.14,0.14
+4.0,0.19,0.18,0.07,0.13,0.16,0.28,0.13,0.25,0.14,0.14,0.05,0.09,0.14,0.12,0.19,0.22,0.22,0.22,0.22,0.43,0.12,0.3,0.38,0.2,0.34,0.19,0.31,0.78,1.0,1.0,1.0,1.0,1.0,1.0,0.07,0.15,0.15
+5.0,0.25,0.2,0.08,0.16,0.19,0.35,0.15,0.3,0.16,0.16,0.05,0.1,0.14,0.15,0.22,0.26,0.25,0.26,0.25,0.47,0.12,0.31,0.43,0.22,0.39,0.22,0.36,0.79,1.0,1.0,1.0,1.0,1.0,1.0,0.09,0.15,0.15
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diff --git a/examples/data/update_ground_floor_height/test_update_ground_floor_height_polygons/exposure/building_points.gpkg b/examples/data/update_ground_floor_height/test_update_ground_floor_height_polygons/exposure/building_points.gpkg
new file mode 100644
index 00000000..a5209f76
Binary files /dev/null and b/examples/data/update_ground_floor_height/test_update_ground_floor_height_polygons/exposure/building_points.gpkg differ
diff --git a/examples/data/update_ground_floor_height/test_update_ground_floor_height_polygons/exposure/exposure.csv b/examples/data/update_ground_floor_height/test_update_ground_floor_height_polygons/exposure/exposure.csv
new file mode 100644
index 00000000..2e2ae644
--- /dev/null
+++ b/examples/data/update_ground_floor_height/test_update_ground_floor_height_polygons/exposure/exposure.csv
@@ -0,0 +1,1995 @@
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diff --git a/examples/data/update_ground_floor_height/test_update_ground_floor_height_polygons/settings.toml b/examples/data/update_ground_floor_height/test_update_ground_floor_height_polygons/settings.toml
new file mode 100644
index 00000000..c9018a8b
--- /dev/null
+++ b/examples/data/update_ground_floor_height/test_update_ground_floor_height_polygons/settings.toml
@@ -0,0 +1,22 @@
+[global]
+crs = "epsg:4326"
+
+[output]
+path = "output"
+
+[output.csv]
+name = "output.csv"
+
+[output.geom]
+name1 = "spatial.gpkg"
+
+[vulnerability]
+file = "./vulnerability/vulnerability_curves.csv"
+scale = 0.1
+
+[exposure.csv]
+file = "./exposure/exposure.csv"
+
+[exposure.geom]
+crs = "EPSG:4326"
+file1 = "./exposure/building_points.gpkg"
diff --git a/examples/data/update_ground_floor_height/test_update_ground_floor_height_polygons/vulnerability/vulnerability_curves.csv b/examples/data/update_ground_floor_height/test_update_ground_floor_height_polygons/vulnerability/vulnerability_curves.csv
new file mode 100644
index 00000000..81d37acf
--- /dev/null
+++ b/examples/data/update_ground_floor_height/test_update_ground_floor_height_polygons/vulnerability/vulnerability_curves.csv
@@ -0,0 +1,32 @@
+#UNIT=feet
+#METHOD,max,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean
+water depth [feet],AGR1,COM1,COM10,COM2,COM3,COM4,COM5,COM6,COM7,COM8,COM9,EDU1,GOV1,GOV2,IND1,IND2,IND3,IND4,IND5,IND6,REL1,RES1-1SNB,RES1-1SWB,RES1-2SNB,RES1-2SWB,RES1-3SNB,RES1-3SWB,RES2,RES3A,RES3B,RES3C,RES3D,RES3E,RES3F,RES4,RES5,RES6
+-4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,0.0,0.04,0.0,0.03,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,0.0,0.04,0.0,0.03,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,0.0,0.08,0.0,0.06,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.11,0.0,0.14,0.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+0.0,0.0,0.01,0.0,0.0,0.0,0.02,0.0,0.0,0.02,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.18,0.17,0.11,0.19,0.05,0.12,0.11,0.12,0.12,0.12,0.12,0.12,0.12,0.0,0.0,0.0
+1.0,0.06,0.09,0.03,0.05,0.09,0.11,0.11,0.0,0.11,0.09,0.02,0.05,0.05,0.07,0.1,0.09,0.13,0.1,0.13,0.22,0.1,0.22,0.21,0.12,0.21,0.08,0.14,0.44,0.25,0.25,0.25,0.25,0.25,0.25,0.03,0.07,0.07
+2.0,0.11,0.14,0.05,0.08,0.12,0.16,0.11,0.0,0.12,0.11,0.04,0.07,0.08,0.1,0.12,0.14,0.14,0.14,0.14,0.31,0.11,0.25,0.29,0.14,0.26,0.12,0.2,0.63,0.5,0.5,0.5,0.5,0.5,0.5,0.05,0.1,0.1
+3.0,0.15,0.16,0.06,0.11,0.13,0.22,0.12,0.2,0.13,0.12,0.05,0.09,0.13,0.11,0.15,0.17,0.19,0.18,0.19,0.37,0.11,0.28,0.34,0.18,0.29,0.17,0.25,0.73,0.75,0.75,0.75,0.75,0.75,0.75,0.06,0.14,0.14
+4.0,0.19,0.18,0.07,0.13,0.16,0.28,0.13,0.25,0.14,0.14,0.05,0.09,0.14,0.12,0.19,0.22,0.22,0.22,0.22,0.43,0.12,0.3,0.38,0.2,0.34,0.19,0.31,0.78,1.0,1.0,1.0,1.0,1.0,1.0,0.07,0.15,0.15
+5.0,0.25,0.2,0.08,0.16,0.19,0.35,0.15,0.3,0.16,0.16,0.05,0.1,0.14,0.15,0.22,0.26,0.25,0.26,0.25,0.47,0.12,0.31,0.43,0.22,0.39,0.22,0.36,0.79,1.0,1.0,1.0,1.0,1.0,1.0,0.09,0.15,0.15
+6.0,0.3,0.23,0.1,0.19,0.22,0.38,0.17,0.35,0.17,0.18,0.06,0.11,0.15,0.17,0.26,0.3,0.28,0.34,0.28,0.5,0.13,0.4,0.5,0.24,0.44,0.24,0.38,0.81,1.0,1.0,1.0,1.0,1.0,1.0,0.12,0.16,0.16
+7.0,0.35,0.26,0.13,0.22,0.25,0.41,0.19,0.4,0.18,0.2,0.08,0.13,0.17,0.2,0.3,0.32,0.3,0.41,0.3,0.54,0.14,0.43,0.5,0.26,0.5,0.25,0.41,0.82,1.0,1.0,1.0,1.0,1.0,1.0,0.14,0.18,0.18
+8.0,0.41,0.3,0.17,0.25,0.28,0.44,0.22,0.43,0.2,0.22,0.1,0.15,0.19,0.23,0.35,0.35,0.33,0.42,0.33,0.57,0.14,0.43,0.54,0.3,0.55,0.3,0.44,0.83,1.0,1.0,1.0,1.0,1.0,1.0,0.18,0.2,0.2
+9.0,0.46,0.34,0.21,0.29,0.32,0.47,0.24,0.47,0.22,0.26,0.12,0.17,0.22,0.27,0.39,0.37,0.34,0.42,0.34,0.61,0.15,0.45,0.55,0.34,0.57,0.35,0.48,0.84,1.0,1.0,1.0,1.0,1.0,1.0,0.21,0.23,0.23
+10.0,0.51,0.38,0.25,0.32,0.35,0.5,0.28,0.5,0.24,0.29,0.15,0.2,0.26,0.31,0.42,0.39,0.36,0.45,0.36,0.63,0.17,0.46,0.55,0.38,0.59,0.38,0.5,0.85,1.0,1.0,1.0,1.0,1.0,1.0,0.26,0.26,0.26
+11.0,0.57,0.42,0.3,0.37,0.39,0.54,0.31,0.53,0.27,0.33,0.2,0.24,0.31,0.35,0.48,0.43,0.39,0.47,0.39,0.64,0.19,0.47,0.57,0.39,0.61,0.39,0.52,0.86,1.0,1.0,1.0,1.0,1.0,1.0,0.31,0.3,0.3
+12.0,0.63,0.47,0.35,0.41,0.43,0.57,0.34,0.55,0.3,0.37,0.24,0.28,0.37,0.4,0.5,0.46,0.4,0.49,0.4,0.65,0.24,0.47,0.58,0.4,0.63,0.4,0.54,0.88,1.0,1.0,1.0,1.0,1.0,1.0,0.36,0.34,0.34
+13.0,0.7,0.51,0.41,0.45,0.47,0.59,0.37,0.57,0.34,0.41,0.29,0.33,0.44,0.44,0.51,0.48,0.42,0.5,0.42,0.67,0.3,0.49,0.6,0.42,0.65,0.42,0.56,0.89,1.0,1.0,1.0,1.0,1.0,1.0,0.41,0.38,0.38
+14.0,0.75,0.55,0.47,0.49,0.5,0.62,0.4,0.6,0.37,0.45,0.35,0.39,0.51,0.48,0.53,0.5,0.42,0.5,0.42,0.68,0.38,0.5,0.62,0.43,0.66,0.43,0.57,0.9,1.0,1.0,1.0,1.0,1.0,1.0,0.46,0.42,0.42
+15.0,0.79,0.58,0.52,0.52,0.54,0.66,0.44,0.6,0.41,0.5,0.42,0.45,0.59,0.52,0.54,0.51,0.43,0.5,0.43,0.69,0.45,0.5,0.63,0.44,0.68,0.44,0.59,0.91,1.0,1.0,1.0,1.0,1.0,1.0,0.5,0.47,0.47
+16.0,0.82,0.61,0.58,0.55,0.57,0.68,0.48,0.6,0.44,0.53,0.49,0.52,0.65,0.56,0.55,0.54,0.43,0.5,0.43,0.7,0.52,0.5,0.65,0.45,0.69,0.45,0.61,0.92,1.0,1.0,1.0,1.0,1.0,1.0,0.54,0.52,0.52
+17.0,0.84,0.64,0.65,0.58,0.61,0.7,0.51,0.6,0.48,0.57,0.56,0.59,0.7,0.6,0.55,0.55,0.44,0.5,0.44,0.71,0.58,0.51,0.67,0.47,0.71,0.47,0.63,0.94,1.0,1.0,1.0,1.0,1.0,1.0,0.58,0.57,0.57
+18.0,0.87,0.67,0.71,0.61,0.64,0.72,0.55,0.6,0.51,0.6,0.62,0.64,0.74,0.64,0.56,0.57,0.44,0.5,0.44,0.72,0.64,0.51,0.69,0.48,0.72,0.48,0.65,0.95,1.0,1.0,1.0,1.0,1.0,1.0,0.62,0.62,0.62
+19.0,0.89,0.69,0.76,0.63,0.68,0.74,0.59,0.6,0.54,0.63,0.68,0.69,0.79,0.68,0.56,0.59,0.44,0.5,0.44,0.73,0.69,0.52,0.7,0.49,0.74,0.49,0.67,0.96,1.0,1.0,1.0,1.0,1.0,1.0,0.66,0.67,0.67
+20.0,0.9,0.71,0.81,0.66,0.71,0.76,0.63,0.6,0.56,0.66,0.74,0.74,0.83,0.72,0.57,0.6,0.44,0.5,0.44,0.74,0.74,0.52,0.72,0.5,0.75,0.5,0.69,0.97,1.0,1.0,1.0,1.0,1.0,1.0,0.7,0.72,0.72
+21.0,0.92,0.74,0.86,0.68,0.75,0.77,0.67,0.6,0.59,0.69,0.8,0.79,0.87,0.76,0.57,0.62,0.44,0.5,0.44,0.75,0.78,0.53,0.74,0.52,0.77,0.52,0.71,0.98,1.0,1.0,1.0,1.0,1.0,1.0,0.74,0.77,0.77
+22.0,0.93,0.76,0.91,0.7,0.78,0.78,0.71,0.6,0.61,0.73,0.86,0.84,0.91,0.8,0.57,0.63,0.45,0.5,0.45,0.76,0.82,0.53,0.76,0.53,0.79,0.53,0.73,0.99,1.0,1.0,1.0,1.0,1.0,1.0,0.78,0.82,0.82
+23.0,0.95,0.78,0.96,0.71,0.82,0.79,0.75,0.6,0.64,0.76,0.92,0.89,0.95,0.84,0.58,0.65,0.45,0.5,0.45,0.76,0.85,0.54,0.77,0.54,0.8,0.54,0.75,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.82,0.87,0.87
+24.0,0.96,0.8,1.0,0.73,0.85,0.8,0.79,0.6,0.66,0.78,0.98,0.94,0.98,0.88,0.58,0.66,0.45,0.5,0.45,0.77,0.88,0.54,0.79,0.56,0.82,0.56,0.77,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.86,0.92,0.92
diff --git a/examples/data/update_max_potential_damage/fake_max_potential_damage_points.gpkg b/examples/data/update_max_potential_damage/fake_max_potential_damage_points.gpkg
new file mode 100644
index 00000000..2b30bf0c
Binary files /dev/null and b/examples/data/update_max_potential_damage/fake_max_potential_damage_points.gpkg differ
diff --git a/examples/data/update_max_potential_damage/fake_max_potential_damage_polygons.gpkg b/examples/data/update_max_potential_damage/fake_max_potential_damage_polygons.gpkg
new file mode 100644
index 00000000..8fe41472
Binary files /dev/null and b/examples/data/update_max_potential_damage/fake_max_potential_damage_polygons.gpkg differ
diff --git a/examples/data/update_max_potential_damage/fiat_model/exposure/building_points.gpkg b/examples/data/update_max_potential_damage/fiat_model/exposure/building_points.gpkg
new file mode 100644
index 00000000..eb21a7ca
Binary files /dev/null and b/examples/data/update_max_potential_damage/fiat_model/exposure/building_points.gpkg differ
diff --git a/examples/data/update_max_potential_damage/fiat_model/exposure/exposure.csv b/examples/data/update_max_potential_damage/fiat_model/exposure/exposure.csv
new file mode 100644
index 00000000..e25d0547
--- /dev/null
+++ b/examples/data/update_max_potential_damage/fiat_model/exposure/exposure.csv
@@ -0,0 +1,1995 @@
+Object ID,Object Name,Primary Object Type,Secondary Object Type,Max Potential Damage: Structure,Max Potential Damage: Content,Ground Elevation,X Coordinate,Y Coordinate,Aggregation Label: Census Block,Ground Floor Height,Extraction Method,Damage Function: Structure,Damage Function: Content
+574486973,574486973,RES,RES3A,480189.335,240094.667,7.298348065,-79.93682513,32.77688289,4.5019E+14,1,centroid,RES3A,RES3A
+573783417,573783417,COM,RES4,1177614.2,588807.1,8.381748562,-79.93000008,32.77054524,4.5019E+14,1,centroid,RES4,RES4
+573783433,573783433,COM,RES4,476216.254,238108.127,7.374183635,-79.92968382,32.7712713,4.5019E+14,1,centroid,RES4,RES4
+573785665,573785665,RES,RES1-2SNB,330007.42,165003.71,7.36403129,-79.93179589,32.77336473,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+574346729,574346729,RES,RES3A,472285.148,236142.574,10.90203092,-79.93064525,32.77485433,4.5019E+14,1,centroid,RES3A,RES3A
+574372707,574372707,RES,RES1-3SNB,389363.808,194681.904,6.365223632,-79.93187273,32.77098914,4.5019E+14,1,centroid,RES1-3SNB,RES1-3SNB
+574372721,574372721,RES,RES3A,503924.421,251962.21,7.12000971,-79.93191719,32.77164194,4.5019E+14,1,centroid,RES3A,RES3A
+574476728,574476728,RES,RES1-1SNB,96964.414,48482.207,5.867157192,-79.93241085,32.76996811,4.5019E+14,1,centroid,RES1-1SNB,RES1-1SNB
+574476811,574476811,RES,RES1-1SNB,152049.256,76024.6281,5.867157192,-79.93241085,32.76996811,4.5019E+14,1,centroid,RES1-1SNB,RES1-1SNB
+574486899,574486899,RES,RES3A,480189.335,240094.667,12.33366492,-79.93027436,32.77536076,4.5019E+14,1,centroid,RES3A,RES3A
+574486932,574486932,RES,RES1-1SNB,177705.21,88852.6051,6.460120566,-79.92998998,32.77314466,4.5019E+14,1,centroid,RES1-1SNB,RES1-1SNB
+573827729,573827729,COM,COM8,37755.4885,37755.4885,11.17279794,-79.93080905,32.77672792,4.5019E+14,1,centroid,COM8,COM8
+574421669,574421669,RES,RES3A,170840.757,85420.3788,11.52017961,-79.92907633,32.7771126,4.5019E+14,1,centroid,RES3A,RES3A
+574488781,574488781,RES,RES1-3SNB,374871.082,187435.541,5.99898202,-79.92578441,32.77724701,4.5019E+14,1,centroid,RES1-3SNB,RES1-3SNB
+573467745,573467745,RES,RES3A,516183.793,258091.896,5.893531857,-79.92827685,32.77200663,4.5019E+14,1,centroid,RES3A,RES3A
+573779323,573779323,RES,RES1-2SWB,437207.93,218603.965,6.975060909,-79.92874432,32.77330072,4.5019E+14,1,centroid,RES1-2SWB,RES1-2SWB
+573779375,573779375,RES,RES1-1SNB,226202.175,113101.087,12.12123537,-79.92780556,32.7743702,4.5019E+14,1,centroid,RES1-1SNB,RES1-1SNB
+573779393,573779393,RES,RES3A,200255.163,100127.581,12.16771917,-79.92811631,32.77473003,4.5019E+14,1,centroid,RES3A,RES3A
+573779405,573779405,RES,RES1-3SNB,398598.966,199299.483,10.82060957,-79.92862217,32.77481123,4.5019E+14,1,centroid,RES1-3SNB,RES1-3SNB
+573782594,573782594,RES,RES3A,489787.119,244893.559,6.500304423,-79.92975812,32.77311597,4.5019E+14,1,centroid,RES3A,RES3A
+573782596,573782596,RES,RES3A,675402.418,337701.209,6.887255912,-79.93036119,32.77318439,4.5019E+14,1,centroid,RES3A,RES3A
+573782413,573782413,RES,RES3A,437002.054,218501.027,7.119138325,-79.92871845,32.77219172,4.5019E+14,1,centroid,RES3A,RES3A
+573782415,573782415,RES,RES1-3SNB,369429.359,184714.679,7.403951541,-79.92887994,32.77219043,4.5019E+14,1,centroid,RES1-3SNB,RES1-3SNB
+573782419,573782419,RES,RES3A,448799.6,224399.8,5.554710338,-79.92834819,32.77223276,4.5019E+14,1,centroid,RES3A,RES3A
+573782453,573782453,RES,RES1-3SNB,448326.4,224163.2,5.318573467,-79.928964,32.77290941,4.5019E+14,1,centroid,RES1-3SNB,RES1-3SNB
+573782528,573782528,RES,RES3A,677628.089,338814.044,12.29409979,-79.9284514,32.77542131,4.5019E+14,1,centroid,RES3A,RES3A
+573783136,573783136,RES,RES1-1SNB,187137.546,93568.7731,6.356549279,-79.92955844,32.7733371,4.5019E+14,1,centroid,RES1-1SNB,RES1-1SNB
+573783158,573783158,PUB,EDU1,4297828.09,4297828.09,11.21253906,-79.92936283,32.77416934,4.5019E+14,1,centroid,EDU1,EDU1
+573783175,573783175,RES,RES3A,327754.263,163877.131,9.728631321,-79.93046822,32.77447171,4.5019E+14,1,centroid,RES3A,RES3A
+573783185,573783185,RES,RES1-2SNB,438110.47,219055.235,10.55727514,-79.93039775,32.77458916,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+573783419,573783419,RES,RES1-2SWB,899667.893,449833.946,7.065387762,-79.92958184,32.77059715,4.5019E+14,1,centroid,RES1-2SWB,RES1-2SWB
+573783425,573783425,RES,RES1-3SWB,615853.155,307926.577,6.638645869,-79.92967987,32.77098975,4.5019E+14,1,centroid,RES1-3SWB,RES1-3SWB
+573785435,573785435,COM,COM8,988173.174,988173.174,11.90498236,-79.93155196,32.77744925,4.5019E+14,1,centroid,COM8,COM8
+573785827,573785827,RES,RES1-3SWB,481445.527,240722.763,10.28449237,-79.93273414,32.77520953,4.5019E+14,1,centroid,RES1-3SWB,RES1-3SWB
+573785950,573785950,RES,RES1-2SNB,472439.483,236219.741,9.125092034,-79.93278733,32.7738393,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
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+573782540,573782540,RES,RES1-2SNB,278796.48,139398.24,12.30676852,-79.9284404,32.77566618,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+573782554,573782554,COM,COM4,426627.449,426627.449,13.15236603,-79.92852646,32.77599619,4.5019E+14,1,centroid,COM4,COM4
+573782586,573782586,RES,RES3A,421325.574,210662.787,8.191982072,-79.93017261,32.77290957,4.5019E+14,1,centroid,RES3A,RES3A
+573783169,573783169,RES,RES3A,512267.747,256133.873,11.97290897,-79.92919073,32.77440531,4.5019E+14,1,centroid,RES3A,RES3A
+573783179,573783179,RES,RES3A,606289.125,303144.562,12.5249974,-79.92928354,32.77452729,4.5019E+14,1,centroid,RES3A,RES3A
+573783187,573783187,RES,RES1-2SNB,386160.703,193080.351,11.5436022,-79.93016895,32.77463032,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+573783189,573783189,COM,COM3,1640307.87,1640307.87,10.78047343,-79.93028267,32.7746644,4.5019E+14,1,centroid,COM3,COM3
+573783199,573783199,RES,RES1-2SNB,551102.006,275551.003,12.75948849,-79.92921208,32.77472151,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+573783443,573783443,RES,RES1-2SNB,311772.137,155886.068,10.10444409,-79.93015806,32.77191319,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+573783445,573783445,RES,RES1-2SNB,680463.744,340231.872,8.827352744,-79.92996727,32.77182933,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+573827136,573827136,COM,COM5,2936504.86,2936504.86,13.54706299,-79.92746902,32.77712292,4.5019E+14,1,centroid,COM5,COM5
+573827731,573827731,COM,COM1,194515.404,194515.404,12.23108548,-79.92959283,32.77679411,4.5019E+14,1,centroid,COM1,COM1
+573827733,573827733,COM,COM3,145886.553,145886.553,12.23108548,-79.92959283,32.77679411,4.5019E+14,1,centroid,COM3,COM3
+573827735,573827735,COM,COM4,81048.0852,81048.0852,12.23108548,-79.92959283,32.77679411,4.5019E+14,1,centroid,COM4,COM4
+573827754,573827754,COM,COM4,95724.6697,95724.6697,11.99108448,-79.93017988,32.77681959,4.5019E+14,1,centroid,COM4,COM4
+574267735,574267735,RES,RES1-1SNB,381021.716,190510.858,12.98996214,-79.92744398,32.77522114,4.5019E+14,1,centroid,RES1-1SNB,RES1-1SNB
+574267737,574267737,RES,RES1-1SNB,273325.341,136662.67,12.70973817,-79.92811401,32.77515289,4.5019E+14,1,centroid,RES1-1SNB,RES1-1SNB
+574267739,574267739,RES,RES1-2SNB,413187.234,206593.617,13.5823017,-79.92800828,32.77526518,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+574267749,574267749,RES,RES3A,491247.521,245623.76,13.51820246,-79.92788864,32.77557876,4.5019E+14,1,centroid,RES3A,RES3A
+574267976,574267976,RES,RES1-2SNB,385155.033,192577.516,13.53189744,-79.92794047,32.77603579,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+574268005,574268005,RES,RES1-1SNB,257591.82,128795.91,12.59055234,-79.92772127,32.77604828,4.5019E+14,1,centroid,RES1-1SNB,RES1-1SNB
+574346733,574346733,RES,RES1-2SNB,345561.158,172780.579,13.16675875,-79.92987341,32.77497559,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+574346737,574346737,COM,COM3,128711.316,128711.316,12.62985934,-79.92934753,32.77496936,4.5019E+14,1,centroid,COM3,COM3
+574346738,574346738,RES,RES1-3SNB,569110.405,284555.202,12.57429248,-79.92997655,32.77497829,4.5019E+14,1,centroid,RES1-3SNB,RES1-3SNB
+574346740,574346740,RES,RES1-2SNB,272662.957,136331.478,12.62266219,-79.92924438,32.77514479,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+574346744,574346744,PUB,REL1,1281715.19,1281715.19,13.0151455,-79.92929333,32.77533709,4.5019E+14,1,centroid,REL1,REL1
+574346753,574346753,RES,RES1-2SNB,311698.604,155849.302,13.77097465,-79.92981335,32.77577007,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+574372447,574372447,COM,COM1,227020.051,227020.051,12.85938964,-79.92900036,32.77610772,4.5019E+14,1,centroid,COM1,COM1
+574372448,574372448,IND,IND2,134849.922,202274.884,12.85938964,-79.92900036,32.77610772,4.5019E+14,1,centroid,IND2,IND2
+574372473,574372473,COM,COM1,424486.831,424486.831,13.03663915,-79.92881546,32.7764473,4.5019E+14,1,centroid,COM1,COM1
+574372533,574372533,COM,COM7,393804.35,590706.525,11.22623405,-79.93032633,32.7759983,4.5019E+14,1,centroid,COM7,COM7
+574372543,574372543,RES,RES3A,338190.212,169095.106,12.60003902,-79.92938805,32.77623669,4.5019E+14,1,centroid,RES3A,RES3A
+574372567,574372567,COM,COM8,284295.791,284295.791,12.21837608,-79.92958905,32.77638171,4.5019E+14,1,centroid,COM8,COM8
+574421681,574421681,COM,COM8,778149.815,778149.815,13.54457556,-79.92846361,32.77697303,4.5019E+14,1,centroid,COM8,COM8
+574421683,574421683,COM,COM1,360197.146,360197.146,13.54457556,-79.92846361,32.77697303,4.5019E+14,1,centroid,COM1,COM1
+574431298,574431298,COM,COM4,1338267.91,1338267.91,10.1165989,-79.92646291,32.77728981,4.5019E+14,1,centroid,COM4,COM4
+574461065,574461065,RES,RES3A,266985.928,133492.964,12.13528704,-79.9297417,32.77681823,4.5019E+14,1,centroid,RES3A,RES3A
+574461073,574461073,COM,COM4,177990.619,177990.619,12.27511783,-79.92950828,32.77642489,4.5019E+14,1,centroid,COM4,COM4
+574461077,574461077,RES,RES3A,273660.577,136830.288,12.4406709,-79.92903083,32.7763684,4.5019E+14,1,centroid,RES3A,RES3A
+574461079,574461079,COM,COM4,177990.619,177990.619,12.86096032,-79.92860499,32.77648743,4.5019E+14,1,centroid,COM4,COM4
+574488771,574488771,RES,RES1-1SNB,222806.066,111403.033,13.26134397,-79.92896336,32.77597596,4.5019E+14,1,centroid,RES1-1SNB,RES1-1SNB
+574488793,574488793,RES,RES1-2SNB,216903.661,108451.83,12.29002837,-79.92736548,32.77640732,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+574488804,574488804,RES,RES1-2SNB,292527.098,146263.549,13.21022478,-79.92816916,32.77627743,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+574488806,574488806,RES,RES1-3SNB,358700.469,179350.234,13.36457265,-79.92824222,32.77627542,4.5019E+14,1,centroid,RES1-3SNB,RES1-3SNB
+574488816,574488816,RES,RES1-1SNB,221789.878,110894.939,6.334944159,-79.92574958,32.77633187,4.5019E+14,1,centroid,RES1-1SNB,RES1-1SNB
+574488819,574488819,RES,RES1-3SNB,416126.362,208063.181,6.052678219,-79.9267068,32.77616474,4.5019E+14,1,centroid,RES1-3SNB,RES1-3SNB
+574488830,574488830,RES,RES1-1SNB,182232.731,91116.3657,12.69306217,-79.92898379,32.77518059,4.5019E+14,1,centroid,RES1-1SNB,RES1-1SNB
+574488834,574488834,RES,RES1-3SNB,384398.576,192199.288,12.90637406,-79.92951787,32.77470617,4.5019E+14,1,centroid,RES1-3SNB,RES1-3SNB
+574488837,574488837,RES,RES3A,480189.335,240094.667,13.25912718,-79.92891,32.77509288,4.5019E+14,1,centroid,RES3A,RES3A
+574488848,574488848,RES,RES1-2SNB,322415.251,161207.625,13.14323291,-79.92763108,32.77497895,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+574488851,574488851,RES,RES1-2SNB,222897.785,111448.892,10.150554,-79.92671547,32.7755542,4.5019E+14,1,centroid,RES1-2SNB,RES1-2SNB
+574488874,574488874,RES,RES3A,480189.335,240094.667,8.900641428,-79.92914469,32.77176507,4.5019E+14,1,centroid,RES3A,RES3A
diff --git a/examples/data/update_max_potential_damage/fiat_model/exposure/region.gpkg b/examples/data/update_max_potential_damage/fiat_model/exposure/region.gpkg
new file mode 100644
index 00000000..2c06a9ef
Binary files /dev/null and b/examples/data/update_max_potential_damage/fiat_model/exposure/region.gpkg differ
diff --git a/examples/data/update_max_potential_damage/fiat_model/settings.toml b/examples/data/update_max_potential_damage/fiat_model/settings.toml
new file mode 100644
index 00000000..c9018a8b
--- /dev/null
+++ b/examples/data/update_max_potential_damage/fiat_model/settings.toml
@@ -0,0 +1,22 @@
+[global]
+crs = "epsg:4326"
+
+[output]
+path = "output"
+
+[output.csv]
+name = "output.csv"
+
+[output.geom]
+name1 = "spatial.gpkg"
+
+[vulnerability]
+file = "./vulnerability/vulnerability_curves.csv"
+scale = 0.1
+
+[exposure.csv]
+file = "./exposure/exposure.csv"
+
+[exposure.geom]
+crs = "EPSG:4326"
+file1 = "./exposure/building_points.gpkg"
diff --git a/examples/data/update_max_potential_damage/fiat_model/vulnerability/vulnerability_curves.csv b/examples/data/update_max_potential_damage/fiat_model/vulnerability/vulnerability_curves.csv
new file mode 100644
index 00000000..81d37acf
--- /dev/null
+++ b/examples/data/update_max_potential_damage/fiat_model/vulnerability/vulnerability_curves.csv
@@ -0,0 +1,32 @@
+#UNIT=feet
+#METHOD,max,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean
+water depth [feet],AGR1,COM1,COM10,COM2,COM3,COM4,COM5,COM6,COM7,COM8,COM9,EDU1,GOV1,GOV2,IND1,IND2,IND3,IND4,IND5,IND6,REL1,RES1-1SNB,RES1-1SWB,RES1-2SNB,RES1-2SWB,RES1-3SNB,RES1-3SWB,RES2,RES3A,RES3B,RES3C,RES3D,RES3E,RES3F,RES4,RES5,RES6
+-4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,0.0,0.04,0.0,0.03,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,0.0,0.04,0.0,0.03,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,0.0,0.08,0.0,0.06,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.11,0.0,0.14,0.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+0.0,0.0,0.01,0.0,0.0,0.0,0.02,0.0,0.0,0.02,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.18,0.17,0.11,0.19,0.05,0.12,0.11,0.12,0.12,0.12,0.12,0.12,0.12,0.0,0.0,0.0
+1.0,0.06,0.09,0.03,0.05,0.09,0.11,0.11,0.0,0.11,0.09,0.02,0.05,0.05,0.07,0.1,0.09,0.13,0.1,0.13,0.22,0.1,0.22,0.21,0.12,0.21,0.08,0.14,0.44,0.25,0.25,0.25,0.25,0.25,0.25,0.03,0.07,0.07
+2.0,0.11,0.14,0.05,0.08,0.12,0.16,0.11,0.0,0.12,0.11,0.04,0.07,0.08,0.1,0.12,0.14,0.14,0.14,0.14,0.31,0.11,0.25,0.29,0.14,0.26,0.12,0.2,0.63,0.5,0.5,0.5,0.5,0.5,0.5,0.05,0.1,0.1
+3.0,0.15,0.16,0.06,0.11,0.13,0.22,0.12,0.2,0.13,0.12,0.05,0.09,0.13,0.11,0.15,0.17,0.19,0.18,0.19,0.37,0.11,0.28,0.34,0.18,0.29,0.17,0.25,0.73,0.75,0.75,0.75,0.75,0.75,0.75,0.06,0.14,0.14
+4.0,0.19,0.18,0.07,0.13,0.16,0.28,0.13,0.25,0.14,0.14,0.05,0.09,0.14,0.12,0.19,0.22,0.22,0.22,0.22,0.43,0.12,0.3,0.38,0.2,0.34,0.19,0.31,0.78,1.0,1.0,1.0,1.0,1.0,1.0,0.07,0.15,0.15
+5.0,0.25,0.2,0.08,0.16,0.19,0.35,0.15,0.3,0.16,0.16,0.05,0.1,0.14,0.15,0.22,0.26,0.25,0.26,0.25,0.47,0.12,0.31,0.43,0.22,0.39,0.22,0.36,0.79,1.0,1.0,1.0,1.0,1.0,1.0,0.09,0.15,0.15
+6.0,0.3,0.23,0.1,0.19,0.22,0.38,0.17,0.35,0.17,0.18,0.06,0.11,0.15,0.17,0.26,0.3,0.28,0.34,0.28,0.5,0.13,0.4,0.5,0.24,0.44,0.24,0.38,0.81,1.0,1.0,1.0,1.0,1.0,1.0,0.12,0.16,0.16
+7.0,0.35,0.26,0.13,0.22,0.25,0.41,0.19,0.4,0.18,0.2,0.08,0.13,0.17,0.2,0.3,0.32,0.3,0.41,0.3,0.54,0.14,0.43,0.5,0.26,0.5,0.25,0.41,0.82,1.0,1.0,1.0,1.0,1.0,1.0,0.14,0.18,0.18
+8.0,0.41,0.3,0.17,0.25,0.28,0.44,0.22,0.43,0.2,0.22,0.1,0.15,0.19,0.23,0.35,0.35,0.33,0.42,0.33,0.57,0.14,0.43,0.54,0.3,0.55,0.3,0.44,0.83,1.0,1.0,1.0,1.0,1.0,1.0,0.18,0.2,0.2
+9.0,0.46,0.34,0.21,0.29,0.32,0.47,0.24,0.47,0.22,0.26,0.12,0.17,0.22,0.27,0.39,0.37,0.34,0.42,0.34,0.61,0.15,0.45,0.55,0.34,0.57,0.35,0.48,0.84,1.0,1.0,1.0,1.0,1.0,1.0,0.21,0.23,0.23
+10.0,0.51,0.38,0.25,0.32,0.35,0.5,0.28,0.5,0.24,0.29,0.15,0.2,0.26,0.31,0.42,0.39,0.36,0.45,0.36,0.63,0.17,0.46,0.55,0.38,0.59,0.38,0.5,0.85,1.0,1.0,1.0,1.0,1.0,1.0,0.26,0.26,0.26
+11.0,0.57,0.42,0.3,0.37,0.39,0.54,0.31,0.53,0.27,0.33,0.2,0.24,0.31,0.35,0.48,0.43,0.39,0.47,0.39,0.64,0.19,0.47,0.57,0.39,0.61,0.39,0.52,0.86,1.0,1.0,1.0,1.0,1.0,1.0,0.31,0.3,0.3
+12.0,0.63,0.47,0.35,0.41,0.43,0.57,0.34,0.55,0.3,0.37,0.24,0.28,0.37,0.4,0.5,0.46,0.4,0.49,0.4,0.65,0.24,0.47,0.58,0.4,0.63,0.4,0.54,0.88,1.0,1.0,1.0,1.0,1.0,1.0,0.36,0.34,0.34
+13.0,0.7,0.51,0.41,0.45,0.47,0.59,0.37,0.57,0.34,0.41,0.29,0.33,0.44,0.44,0.51,0.48,0.42,0.5,0.42,0.67,0.3,0.49,0.6,0.42,0.65,0.42,0.56,0.89,1.0,1.0,1.0,1.0,1.0,1.0,0.41,0.38,0.38
+14.0,0.75,0.55,0.47,0.49,0.5,0.62,0.4,0.6,0.37,0.45,0.35,0.39,0.51,0.48,0.53,0.5,0.42,0.5,0.42,0.68,0.38,0.5,0.62,0.43,0.66,0.43,0.57,0.9,1.0,1.0,1.0,1.0,1.0,1.0,0.46,0.42,0.42
+15.0,0.79,0.58,0.52,0.52,0.54,0.66,0.44,0.6,0.41,0.5,0.42,0.45,0.59,0.52,0.54,0.51,0.43,0.5,0.43,0.69,0.45,0.5,0.63,0.44,0.68,0.44,0.59,0.91,1.0,1.0,1.0,1.0,1.0,1.0,0.5,0.47,0.47
+16.0,0.82,0.61,0.58,0.55,0.57,0.68,0.48,0.6,0.44,0.53,0.49,0.52,0.65,0.56,0.55,0.54,0.43,0.5,0.43,0.7,0.52,0.5,0.65,0.45,0.69,0.45,0.61,0.92,1.0,1.0,1.0,1.0,1.0,1.0,0.54,0.52,0.52
+17.0,0.84,0.64,0.65,0.58,0.61,0.7,0.51,0.6,0.48,0.57,0.56,0.59,0.7,0.6,0.55,0.55,0.44,0.5,0.44,0.71,0.58,0.51,0.67,0.47,0.71,0.47,0.63,0.94,1.0,1.0,1.0,1.0,1.0,1.0,0.58,0.57,0.57
+18.0,0.87,0.67,0.71,0.61,0.64,0.72,0.55,0.6,0.51,0.6,0.62,0.64,0.74,0.64,0.56,0.57,0.44,0.5,0.44,0.72,0.64,0.51,0.69,0.48,0.72,0.48,0.65,0.95,1.0,1.0,1.0,1.0,1.0,1.0,0.62,0.62,0.62
+19.0,0.89,0.69,0.76,0.63,0.68,0.74,0.59,0.6,0.54,0.63,0.68,0.69,0.79,0.68,0.56,0.59,0.44,0.5,0.44,0.73,0.69,0.52,0.7,0.49,0.74,0.49,0.67,0.96,1.0,1.0,1.0,1.0,1.0,1.0,0.66,0.67,0.67
+20.0,0.9,0.71,0.81,0.66,0.71,0.76,0.63,0.6,0.56,0.66,0.74,0.74,0.83,0.72,0.57,0.6,0.44,0.5,0.44,0.74,0.74,0.52,0.72,0.5,0.75,0.5,0.69,0.97,1.0,1.0,1.0,1.0,1.0,1.0,0.7,0.72,0.72
+21.0,0.92,0.74,0.86,0.68,0.75,0.77,0.67,0.6,0.59,0.69,0.8,0.79,0.87,0.76,0.57,0.62,0.44,0.5,0.44,0.75,0.78,0.53,0.74,0.52,0.77,0.52,0.71,0.98,1.0,1.0,1.0,1.0,1.0,1.0,0.74,0.77,0.77
+22.0,0.93,0.76,0.91,0.7,0.78,0.78,0.71,0.6,0.61,0.73,0.86,0.84,0.91,0.8,0.57,0.63,0.45,0.5,0.45,0.76,0.82,0.53,0.76,0.53,0.79,0.53,0.73,0.99,1.0,1.0,1.0,1.0,1.0,1.0,0.78,0.82,0.82
+23.0,0.95,0.78,0.96,0.71,0.82,0.79,0.75,0.6,0.64,0.76,0.92,0.89,0.95,0.84,0.58,0.65,0.45,0.5,0.45,0.76,0.85,0.54,0.77,0.54,0.8,0.54,0.75,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.82,0.87,0.87
+24.0,0.96,0.8,1.0,0.73,0.85,0.8,0.79,0.6,0.66,0.78,0.98,0.94,0.98,0.88,0.58,0.66,0.45,0.5,0.45,0.77,0.88,0.54,0.79,0.56,0.82,0.56,0.77,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.86,0.92,0.92
diff --git a/examples/data/update_max_potential_damage/fiat_model/vulnerability/vulnerability_identifiers.csv b/examples/data/update_max_potential_damage/fiat_model/vulnerability/vulnerability_identifiers.csv
new file mode 100644
index 00000000..7d7fbb72
--- /dev/null
+++ b/examples/data/update_max_potential_damage/fiat_model/vulnerability/vulnerability_identifiers.csv
@@ -0,0 +1,38 @@
+,Name,Link,BldgDmgFnID,Occupancy,Source,Description,full_identifier
+0,AGR1,AGR1,616,AGR1 ,USACE - Galveston,"Average Agriculture, structure","616AGR1USACE-GalvestonAverageAgriculture,structure"
+1,COM1,COM1,217,COM1 ,USACE - Galveston,"Average Retail, Structure","217COM1USACE-GalvestonAverageRetail,Structure"
+2,COM10,COM10,543,COM10,USACE - Galveston,"Garage, structure","543COM10USACE-GalvestonGarage,structure"
+3,COM2,COM2,341,COM2 ,USACE - Galveston,"Average wholesale, Structure","341COM2USACE-GalvestonAveragewholesale,Structure"
+4,COM3,COM3,375,COM3 ,USACE - Galveston,"Average Personal & Repair Services, Structure","375COM3USACE-GalvestonAveragePersonal&RepairServices,Structure"
+5,COM4,COM4,431,COM4 ,USACE - Galveston,"Average Prof/Tech Services, Structure","431COM4USACE-GalvestonAverageProf/TechServices,Structure"
+6,COM5,COM5,467,COM5 ,USACE - Galveston,"Bank, Structure","467COM5USACE-GalvestonBank,Structure"
+7,COM6,COM6,474,COM6 ,USACE - Galveston,"Hospital, Structure","474COM6USACE-GalvestonHospital,Structure"
+8,COM7,COM7,475,COM7 ,USACE - Galveston,"Average Medical Office, Structure","475COM7USACE-GalvestonAverageMedicalOffice,Structure"
+9,COM8,COM8,493,COM8 ,USACE - Galveston,"Average Entertainment/Recreation, Structure","493COM8USACE-GalvestonAverageEntertainment/Recreation,Structure"
+10,COM9,COM9,532,COM9 ,USACE - Galveston,"Average theatre, Structure","532COM9USACE-GalvestonAveragetheatre,Structure"
+11,EDU1,EDU1,643,EDU1 ,USACE - Galveston,"Average school, structure","643EDU1USACE-GalvestonAverageschool,structure"
+12,GOV1,GOV1,631,GOV1 ,USACE - Galveston,"Average government services, structure","631GOV1USACE-GalvestonAveragegovernmentservices,structure"
+13,GOV2,GOV2,640,GOV2 ,USACE - Galveston,"Average emergency response, Structure","640GOV2USACE-GalvestonAverageemergencyresponse,Structure"
+14,IND1,IND1,545,IND1 ,USACE - Galveston,"Average heavy industrial, Structure","545IND1USACE-GalvestonAverageheavyindustrial,Structure"
+15,IND2,IND2,559,IND2 ,USACE - Galveston,"Average light industrial, structure","559IND2USACE-GalvestonAveragelightindustrial,structure"
+16,IND3,IND3,575,IND3 ,USACE - Galveston,"Average Food/Drug/Chem, Structure","575IND3USACE-GalvestonAverageFood/Drug/Chem,Structure"
+17,IND4,IND4,586,IND4 ,USACE - Galveston,"Average Metals/Minerals processing, structure","586IND4USACE-GalvestonAverageMetals/Mineralsprocessing,structure"
+18,IND5,IND5,591,IND5 ,USACE - Galveston,"Average High Technology, structure","591IND5USACE-GalvestonAverageHighTechnology,structure"
+19,IND6,IND6,592,IND6 ,USACE - Galveston,"Average Construction, structure","592IND6USACE-GalvestonAverageConstruction,structure"
+20,REL1,REL1,624,REL1 ,USACE - Galveston,"Church, structure","624REL1USACE-GalvestonChurch,structure"
+21,RES1-1SNB,RES1-1SNB,105,RES1 ,FIA,"one floor, no basement, Structure, A-Zone","105RES1FIAonefloor,nobasement,Structure,A-Zone"
+22,RES1-1SWB,RES1-1SWB,106,RES1 ,FIA (MOD.),"one floor, w/ basement, Structure, A-Zone","106RES1FIA(MOD.)onefloor,w/basement,Structure,A-Zone"
+23,RES1-2SNB,RES1-2SNB,107,RES1 ,FIA,"two floors, no basement, Structure, A-Zone","107RES1FIAtwofloors,nobasement,Structure,A-Zone"
+24,RES1-2SWB,RES1-2SWB,108,RES1 ,FIA (MOD.),"two floors, w/ basement, Structure, A-Zone","108RES1FIA(MOD.)twofloors,w/basement,Structure,A-Zone"
+25,RES1-3SNB,RES1-3SNB,109,RES1 ,FIA,"three or more floors, no basement, Structure, A-Zone","109RES1FIAthreeormorefloors,nobasement,Structure,A-Zone"
+26,RES1-3SWB,RES1-3SWB,110,RES1 ,FIA (MOD.),"three or more floors, w/ basement, Structure, A-Zone","110RES1FIA(MOD.)threeormorefloors,w/basement,Structure,A-Zone"
+27,RES2,RES2,189,RES2 ,FIA,"Mobile home, structure, A-Zone","189RES2FIAMobilehome,structure,A-Zone"
+28,RES3A,RES3A,659,RES3A,BCAR - Jan 2011,"1to2 Stories, slab - no basement, Coastal A or V Zone","659RES3ABCAR-Jan20111to2Stories,slab-nobasement,CoastalAorVZone"
+29,RES3B,RES3B,660,RES3B,BCAR - Jan 2011,"1to2 Stories, slab - no basement, Coastal A or V Zone","660RES3BBCAR-Jan20111to2Stories,slab-nobasement,CoastalAorVZone"
+30,RES3C,RES3C,660,RES3B,BCAR - Jan 2011,"1to2 Stories, slab - no basement, Coastal A or V Zone","660RES3BBCAR-Jan20111to2Stories,slab-nobasement,CoastalAorVZone"
+31,RES3D,RES3D,660,RES3B,BCAR - Jan 2011,"1to2 Stories, slab - no basement, Coastal A or V Zone","660RES3BBCAR-Jan20111to2Stories,slab-nobasement,CoastalAorVZone"
+32,RES3E,RES3E,660,RES3B,BCAR - Jan 2011,"1to2 Stories, slab - no basement, Coastal A or V Zone","660RES3BBCAR-Jan20111to2Stories,slab-nobasement,CoastalAorVZone"
+33,RES3F,RES3F,660,RES3B,BCAR - Jan 2011,"1to2 Stories, slab - no basement, Coastal A or V Zone","660RES3BBCAR-Jan20111to2Stories,slab-nobasement,CoastalAorVZone"
+34,RES4,RES4,209,RES4 ,USACE - Galveston,"Average Hotel & Motel, structure","209RES4USACE-GalvestonAverageHotel&Motel,structure"
+35,RES5,RES5,214,RES5 ,USACE - Galveston,"Average institutional dormitory, structure","214RES5USACE-GalvestonAverageinstitutionaldormitory,structure"
+36,RES6,RES6,215,RES6 ,USACE - Galveston,"Nursing Home, structure","215RES6USACE-GalvestonNursingHome,structure"
diff --git a/examples/data/update_max_potential_damage/test_update_max_potential_damage_points/exposure/building_points.gpkg b/examples/data/update_max_potential_damage/test_update_max_potential_damage_points/exposure/building_points.gpkg
new file mode 100644
index 00000000..ac926375
Binary files /dev/null and b/examples/data/update_max_potential_damage/test_update_max_potential_damage_points/exposure/building_points.gpkg differ
diff --git a/examples/data/update_max_potential_damage/test_update_max_potential_damage_points/exposure/exposure.csv b/examples/data/update_max_potential_damage/test_update_max_potential_damage_points/exposure/exposure.csv
new file mode 100644
index 00000000..923521c5
--- /dev/null
+++ b/examples/data/update_max_potential_damage/test_update_max_potential_damage_points/exposure/exposure.csv
@@ -0,0 +1,1995 @@
+Object ID,Object Name,Primary Object Type,Secondary Object Type,Max Potential Damage: Structure,Max Potential Damage: Content,Ground Elevation,X Coordinate,Y Coordinate,Aggregation Label: Census Block,Ground Floor Height,Extraction Method,Damage Function: Structure,Damage Function: Content,geometry
+574486973,574486973,RES,RES3A,480189.335,240094.667,7.298348065,-79.93682513,32.77688289,450190000000000.0,1,centroid,RES3A,RES3A,POINT (-79.93682513 32.776882893)
+573783417,573783417,COM,RES4,1177614.2,588807.1,8.381748562,-79.93000008,32.77054524,450190000000000.0,1,centroid,RES4,RES4,POINT (-79.93000008 32.770545239)
+573783433,573783433,COM,RES4,476216.254,238108.127,7.374183635,-79.92968382,32.7712713,450190000000000.0,1,centroid,RES4,RES4,POINT (-79.92968382 32.771271301)
+573785665,573785665,RES,RES1-2SNB,330007.42,165003.71,7.36403129,-79.93179589,32.77336473,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB,POINT (-79.93179589 32.773364727)
+574346729,574346729,RES,RES3A,472285.148,236142.574,10.90203092,-79.93064525,32.77485433,450190000000000.0,1,centroid,RES3A,RES3A,POINT (-79.93064525 32.774854333)
+574372707,574372707,RES,RES1-3SNB,389363.808,194681.904,6.365223632,-79.93187273,32.77098914,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB,POINT (-79.93187273 32.770989143)
+574372721,574372721,RES,RES3A,503924.421,251962.21,7.12000971,-79.93191719,32.77164194,450190000000000.0,1,centroid,RES3A,RES3A,POINT (-79.93191719 32.771641938)
+574476728,574476728,RES,RES1-1SNB,96964.414,48482.207,5.867157192,-79.93241085,32.76996811,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB,POINT (-79.93241085 32.769968112)
+574476811,574476811,RES,RES1-1SNB,152049.256,76024.6281,5.867157192,-79.93241085,32.76996811,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB,POINT (-79.93241085 32.769968112)
+574486899,574486899,RES,RES3A,480189.335,240094.667,12.33366492,-79.93027436,32.77536076,450190000000000.0,1,centroid,RES3A,RES3A,POINT (-79.93027436 32.775360757)
+574486932,574486932,RES,RES1-1SNB,177705.21,88852.6051,6.460120566,-79.92998998,32.77314466,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB,POINT (-79.92998998 32.773144664)
+573827729,573827729,COM,COM8,37755.4885,37755.4885,11.17279794,-79.93080905,32.77672792,450190000000000.0,1,centroid,COM8,COM8,POINT (-79.93080905 32.776727923)
+574421669,574421669,RES,RES3A,170840.757,85420.3788,11.52017961,-79.92907633,32.7771126,450190000000000.0,1,centroid,RES3A,RES3A,POINT (-79.92907633 32.777112595)
+574488781,574488781,RES,RES1-3SNB,374871.082,187435.541,5.99898202,-79.92578441,32.77724701,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB,POINT (-79.92578441 32.77724701)
+573467745,573467745,RES,RES3A,516183.793,258091.896,5.893531857,-79.92827685,32.77200663,450190000000000.0,1,centroid,RES3A,RES3A,POINT (-79.92827685 32.772006634)
+573779323,573779323,RES,RES1-2SWB,437207.93,218603.965,6.975060909,-79.92874432,32.77330072,450190000000000.0,1,centroid,RES1-2SWB,RES1-2SWB,POINT (-79.92874432 32.77330072)
+573779375,573779375,RES,RES1-1SNB,226202.175,113101.087,12.12123537,-79.92780556,32.7743702,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB,POINT (-79.92780556 32.7743702)
+573779393,573779393,RES,RES3A,200255.163,100127.581,12.16771917,-79.92811631,32.77473003,450190000000000.0,1,centroid,RES3A,RES3A,POINT (-79.92811631 32.774730033)
+573779405,573779405,RES,RES1-3SNB,398598.966,199299.483,10.82060957,-79.92862217,32.77481123,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB,POINT (-79.92862217 32.774811231)
+573782594,573782594,RES,RES3A,489787.119,244893.559,6.500304423,-79.92975812,32.77311597,450190000000000.0,1,centroid,RES3A,RES3A,POINT (-79.92975812 32.773115965)
+573782596,573782596,RES,RES3A,675402.418,337701.209,6.887255912,-79.93036119,32.77318439,450190000000000.0,1,centroid,RES3A,RES3A,POINT (-79.93036119 32.773184386)
+573782413,573782413,RES,RES3A,437002.054,218501.027,7.119138325,-79.92871845,32.77219172,450190000000000.0,1,centroid,RES3A,RES3A,POINT (-79.92871845 32.772191724)
+573782415,573782415,RES,RES1-3SNB,369429.359,184714.679,7.403951541,-79.92887994,32.77219043,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB,POINT (-79.92887994 32.772190428)
+573782419,573782419,RES,RES3A,448799.6,224399.8,5.554710338,-79.92834819,32.77223276,450190000000000.0,1,centroid,RES3A,RES3A,POINT (-79.92834819 32.772232759)
+573782453,573782453,RES,RES1-3SNB,448326.4,224163.2,5.318573467,-79.928964,32.77290941,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB,POINT (-79.928964 32.772909407)
+573782528,573782528,RES,RES3A,677628.089,338814.044,12.29409979,-79.9284514,32.77542131,450190000000000.0,1,centroid,RES3A,RES3A,POINT (-79.9284514 32.775421312)
+573783136,573783136,RES,RES1-1SNB,187137.546,93568.7731,6.356549279,-79.92955844,32.7733371,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB,POINT (-79.92955844 32.773337104)
+573783158,573783158,PUB,EDU1,4297828.09,4297828.09,11.21253906,-79.92936283,32.77416934,450190000000000.0,1,centroid,EDU1,EDU1,POINT (-79.92936283 32.774169335)
+573783175,573783175,RES,RES3A,327754.263,163877.131,9.728631321,-79.93046822,32.77447171,450190000000000.0,1,centroid,RES3A,RES3A,POINT (-79.93046822 32.774471714)
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+573783419,573783419,RES,RES1-2SWB,899667.893,449833.946,7.065387762,-79.92958184,32.77059715,450190000000000.0,1,centroid,RES1-2SWB,RES1-2SWB,POINT (-79.92958184 32.77059715)
+573783425,573783425,RES,RES1-3SWB,615853.155,307926.577,6.638645869,-79.92967987,32.77098975,450190000000000.0,1,centroid,RES1-3SWB,RES1-3SWB,POINT (-79.92967987 32.770989749)
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+573785950,573785950,RES,RES1-2SNB,472439.483,236219.741,9.125092034,-79.93278733,32.7738393,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB,POINT (-79.93278733 32.773839298)
+573785982,573785982,RES,RES1-3SNB,544883.847,272441.923,10.69242829,-79.93262251,32.77438286,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB,POINT (-79.93262251 32.77438286)
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+573827727,573827727,PUB,GOV1,1531018.22,1531018.22,11.17279794,-79.93080905,32.77672792,450190000000000.0,1,centroid,GOV1,GOV1,POINT (-79.93080905 32.776727923)
+573827740,573827740,RES,RES3A,210487.941,105243.97,12.13528704,-79.92972682,32.77680666,450190000000000.0,1,centroid,RES3A,RES3A,POINT (-79.92972682 32.776806657)
+573827758,573827758,COM,COM4,414162.244,414162.244,12.28739544,-79.92947989,32.77702812,450190000000000.0,1,centroid,COM4,COM4,POINT (-79.92947989 32.777028121)
+573831699,573831699,RES,RES1-2SNB,195993.925,97996.9627,10.66339723,-79.93245507,32.77518981,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB,POINT (-79.93245507 32.775189806)
+573831713,573831713,COM,COM4,221441.012,221441.012,10.54490679,-79.93193054,32.7752842,450190000000000.0,1,centroid,COM4,COM4,POINT (-79.93193054 32.775284203)
+573831715,573831715,RES,RES1-2SNB,359315.841,179657.92,10.38239652,-79.93244164,32.77533365,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB,POINT (-79.93244164 32.775333645)
+574267660,574267660,COM,COM2,411264.761,411264.761,11.4503749,-79.92712901,32.77418197,450190000000000.0,1,centroid,COM2,COM2,POINT (-79.92712901 32.774181965)
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+574488874,574488874,RES,RES3A,480189.335,240094.667,8.900641428,-79.92914469,32.77176507,450190000000000.0,1,centroid,RES3A,RES3A,POINT (-79.92914469 32.77176507)
diff --git a/examples/data/update_max_potential_damage/test_update_max_potential_damage_points/settings.toml b/examples/data/update_max_potential_damage/test_update_max_potential_damage_points/settings.toml
new file mode 100644
index 00000000..c9018a8b
--- /dev/null
+++ b/examples/data/update_max_potential_damage/test_update_max_potential_damage_points/settings.toml
@@ -0,0 +1,22 @@
+[global]
+crs = "epsg:4326"
+
+[output]
+path = "output"
+
+[output.csv]
+name = "output.csv"
+
+[output.geom]
+name1 = "spatial.gpkg"
+
+[vulnerability]
+file = "./vulnerability/vulnerability_curves.csv"
+scale = 0.1
+
+[exposure.csv]
+file = "./exposure/exposure.csv"
+
+[exposure.geom]
+crs = "EPSG:4326"
+file1 = "./exposure/building_points.gpkg"
diff --git a/examples/data/update_max_potential_damage/test_update_max_potential_damage_points/vulnerability/vulnerability_curves.csv b/examples/data/update_max_potential_damage/test_update_max_potential_damage_points/vulnerability/vulnerability_curves.csv
new file mode 100644
index 00000000..81d37acf
--- /dev/null
+++ b/examples/data/update_max_potential_damage/test_update_max_potential_damage_points/vulnerability/vulnerability_curves.csv
@@ -0,0 +1,32 @@
+#UNIT=feet
+#METHOD,max,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean
+water depth [feet],AGR1,COM1,COM10,COM2,COM3,COM4,COM5,COM6,COM7,COM8,COM9,EDU1,GOV1,GOV2,IND1,IND2,IND3,IND4,IND5,IND6,REL1,RES1-1SNB,RES1-1SWB,RES1-2SNB,RES1-2SWB,RES1-3SNB,RES1-3SWB,RES2,RES3A,RES3B,RES3C,RES3D,RES3E,RES3F,RES4,RES5,RES6
+-4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,0.0,0.04,0.0,0.03,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,0.0,0.04,0.0,0.03,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,0.0,0.08,0.0,0.06,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.11,0.0,0.14,0.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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+1.0,0.06,0.09,0.03,0.05,0.09,0.11,0.11,0.0,0.11,0.09,0.02,0.05,0.05,0.07,0.1,0.09,0.13,0.1,0.13,0.22,0.1,0.22,0.21,0.12,0.21,0.08,0.14,0.44,0.25,0.25,0.25,0.25,0.25,0.25,0.03,0.07,0.07
+2.0,0.11,0.14,0.05,0.08,0.12,0.16,0.11,0.0,0.12,0.11,0.04,0.07,0.08,0.1,0.12,0.14,0.14,0.14,0.14,0.31,0.11,0.25,0.29,0.14,0.26,0.12,0.2,0.63,0.5,0.5,0.5,0.5,0.5,0.5,0.05,0.1,0.1
+3.0,0.15,0.16,0.06,0.11,0.13,0.22,0.12,0.2,0.13,0.12,0.05,0.09,0.13,0.11,0.15,0.17,0.19,0.18,0.19,0.37,0.11,0.28,0.34,0.18,0.29,0.17,0.25,0.73,0.75,0.75,0.75,0.75,0.75,0.75,0.06,0.14,0.14
+4.0,0.19,0.18,0.07,0.13,0.16,0.28,0.13,0.25,0.14,0.14,0.05,0.09,0.14,0.12,0.19,0.22,0.22,0.22,0.22,0.43,0.12,0.3,0.38,0.2,0.34,0.19,0.31,0.78,1.0,1.0,1.0,1.0,1.0,1.0,0.07,0.15,0.15
+5.0,0.25,0.2,0.08,0.16,0.19,0.35,0.15,0.3,0.16,0.16,0.05,0.1,0.14,0.15,0.22,0.26,0.25,0.26,0.25,0.47,0.12,0.31,0.43,0.22,0.39,0.22,0.36,0.79,1.0,1.0,1.0,1.0,1.0,1.0,0.09,0.15,0.15
+6.0,0.3,0.23,0.1,0.19,0.22,0.38,0.17,0.35,0.17,0.18,0.06,0.11,0.15,0.17,0.26,0.3,0.28,0.34,0.28,0.5,0.13,0.4,0.5,0.24,0.44,0.24,0.38,0.81,1.0,1.0,1.0,1.0,1.0,1.0,0.12,0.16,0.16
+7.0,0.35,0.26,0.13,0.22,0.25,0.41,0.19,0.4,0.18,0.2,0.08,0.13,0.17,0.2,0.3,0.32,0.3,0.41,0.3,0.54,0.14,0.43,0.5,0.26,0.5,0.25,0.41,0.82,1.0,1.0,1.0,1.0,1.0,1.0,0.14,0.18,0.18
+8.0,0.41,0.3,0.17,0.25,0.28,0.44,0.22,0.43,0.2,0.22,0.1,0.15,0.19,0.23,0.35,0.35,0.33,0.42,0.33,0.57,0.14,0.43,0.54,0.3,0.55,0.3,0.44,0.83,1.0,1.0,1.0,1.0,1.0,1.0,0.18,0.2,0.2
+9.0,0.46,0.34,0.21,0.29,0.32,0.47,0.24,0.47,0.22,0.26,0.12,0.17,0.22,0.27,0.39,0.37,0.34,0.42,0.34,0.61,0.15,0.45,0.55,0.34,0.57,0.35,0.48,0.84,1.0,1.0,1.0,1.0,1.0,1.0,0.21,0.23,0.23
+10.0,0.51,0.38,0.25,0.32,0.35,0.5,0.28,0.5,0.24,0.29,0.15,0.2,0.26,0.31,0.42,0.39,0.36,0.45,0.36,0.63,0.17,0.46,0.55,0.38,0.59,0.38,0.5,0.85,1.0,1.0,1.0,1.0,1.0,1.0,0.26,0.26,0.26
+11.0,0.57,0.42,0.3,0.37,0.39,0.54,0.31,0.53,0.27,0.33,0.2,0.24,0.31,0.35,0.48,0.43,0.39,0.47,0.39,0.64,0.19,0.47,0.57,0.39,0.61,0.39,0.52,0.86,1.0,1.0,1.0,1.0,1.0,1.0,0.31,0.3,0.3
+12.0,0.63,0.47,0.35,0.41,0.43,0.57,0.34,0.55,0.3,0.37,0.24,0.28,0.37,0.4,0.5,0.46,0.4,0.49,0.4,0.65,0.24,0.47,0.58,0.4,0.63,0.4,0.54,0.88,1.0,1.0,1.0,1.0,1.0,1.0,0.36,0.34,0.34
+13.0,0.7,0.51,0.41,0.45,0.47,0.59,0.37,0.57,0.34,0.41,0.29,0.33,0.44,0.44,0.51,0.48,0.42,0.5,0.42,0.67,0.3,0.49,0.6,0.42,0.65,0.42,0.56,0.89,1.0,1.0,1.0,1.0,1.0,1.0,0.41,0.38,0.38
+14.0,0.75,0.55,0.47,0.49,0.5,0.62,0.4,0.6,0.37,0.45,0.35,0.39,0.51,0.48,0.53,0.5,0.42,0.5,0.42,0.68,0.38,0.5,0.62,0.43,0.66,0.43,0.57,0.9,1.0,1.0,1.0,1.0,1.0,1.0,0.46,0.42,0.42
+15.0,0.79,0.58,0.52,0.52,0.54,0.66,0.44,0.6,0.41,0.5,0.42,0.45,0.59,0.52,0.54,0.51,0.43,0.5,0.43,0.69,0.45,0.5,0.63,0.44,0.68,0.44,0.59,0.91,1.0,1.0,1.0,1.0,1.0,1.0,0.5,0.47,0.47
+16.0,0.82,0.61,0.58,0.55,0.57,0.68,0.48,0.6,0.44,0.53,0.49,0.52,0.65,0.56,0.55,0.54,0.43,0.5,0.43,0.7,0.52,0.5,0.65,0.45,0.69,0.45,0.61,0.92,1.0,1.0,1.0,1.0,1.0,1.0,0.54,0.52,0.52
+17.0,0.84,0.64,0.65,0.58,0.61,0.7,0.51,0.6,0.48,0.57,0.56,0.59,0.7,0.6,0.55,0.55,0.44,0.5,0.44,0.71,0.58,0.51,0.67,0.47,0.71,0.47,0.63,0.94,1.0,1.0,1.0,1.0,1.0,1.0,0.58,0.57,0.57
+18.0,0.87,0.67,0.71,0.61,0.64,0.72,0.55,0.6,0.51,0.6,0.62,0.64,0.74,0.64,0.56,0.57,0.44,0.5,0.44,0.72,0.64,0.51,0.69,0.48,0.72,0.48,0.65,0.95,1.0,1.0,1.0,1.0,1.0,1.0,0.62,0.62,0.62
+19.0,0.89,0.69,0.76,0.63,0.68,0.74,0.59,0.6,0.54,0.63,0.68,0.69,0.79,0.68,0.56,0.59,0.44,0.5,0.44,0.73,0.69,0.52,0.7,0.49,0.74,0.49,0.67,0.96,1.0,1.0,1.0,1.0,1.0,1.0,0.66,0.67,0.67
+20.0,0.9,0.71,0.81,0.66,0.71,0.76,0.63,0.6,0.56,0.66,0.74,0.74,0.83,0.72,0.57,0.6,0.44,0.5,0.44,0.74,0.74,0.52,0.72,0.5,0.75,0.5,0.69,0.97,1.0,1.0,1.0,1.0,1.0,1.0,0.7,0.72,0.72
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+22.0,0.93,0.76,0.91,0.7,0.78,0.78,0.71,0.6,0.61,0.73,0.86,0.84,0.91,0.8,0.57,0.63,0.45,0.5,0.45,0.76,0.82,0.53,0.76,0.53,0.79,0.53,0.73,0.99,1.0,1.0,1.0,1.0,1.0,1.0,0.78,0.82,0.82
+23.0,0.95,0.78,0.96,0.71,0.82,0.79,0.75,0.6,0.64,0.76,0.92,0.89,0.95,0.84,0.58,0.65,0.45,0.5,0.45,0.76,0.85,0.54,0.77,0.54,0.8,0.54,0.75,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.82,0.87,0.87
+24.0,0.96,0.8,1.0,0.73,0.85,0.8,0.79,0.6,0.66,0.78,0.98,0.94,0.98,0.88,0.58,0.66,0.45,0.5,0.45,0.77,0.88,0.54,0.79,0.56,0.82,0.56,0.77,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.86,0.92,0.92
diff --git a/examples/data/update_max_potential_damage/test_update_max_potential_damage_polygons/exposure/building_points.gpkg b/examples/data/update_max_potential_damage/test_update_max_potential_damage_polygons/exposure/building_points.gpkg
new file mode 100644
index 00000000..581f14e2
Binary files /dev/null and b/examples/data/update_max_potential_damage/test_update_max_potential_damage_polygons/exposure/building_points.gpkg differ
diff --git a/examples/data/update_max_potential_damage/test_update_max_potential_damage_polygons/exposure/exposure.csv b/examples/data/update_max_potential_damage/test_update_max_potential_damage_polygons/exposure/exposure.csv
new file mode 100644
index 00000000..0d50bb9a
--- /dev/null
+++ b/examples/data/update_max_potential_damage/test_update_max_potential_damage_polygons/exposure/exposure.csv
@@ -0,0 +1,1995 @@
+Object ID,Object Name,Primary Object Type,Secondary Object Type,Max Potential Damage: Structure,Max Potential Damage: Content,Ground Elevation,X Coordinate,Y Coordinate,Aggregation Label: Census Block,Ground Floor Height,Extraction Method,Damage Function: Structure,Damage Function: Content,geometry,groundfloorheight,maxpotential_content
+574486973,574486973,RES,RES3A,480189.335,240094.667,7.298348065,-79.93682513,32.77688289,450190000000000.0,1,centroid,RES3A,RES3A,POINT (-79.93682513 32.776882893),,
+573783417,573783417,COM,RES4,10000.0,588807.1,8.381748562,-79.93000008,32.77054524,450190000000000.0,1,centroid,RES4,RES4,POINT (-79.93000008 32.770545239),7.0,10000.0
+573783433,573783433,COM,RES4,10000.0,238108.127,7.374183635,-79.92968382,32.7712713,450190000000000.0,1,centroid,RES4,RES4,POINT (-79.92968382 32.771271301),7.0,10000.0
+573785665,573785665,RES,RES1-2SNB,10000.0,165003.71,7.36403129,-79.93179589,32.77336473,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB,POINT (-79.93179589 32.773364727),10.0,10000.0
+574346729,574346729,RES,RES3A,472285.148,236142.574,10.90203092,-79.93064525,32.77485433,450190000000000.0,1,centroid,RES3A,RES3A,POINT (-79.93064525 32.774854333),,
+574372707,574372707,RES,RES1-3SNB,10000.0,194681.904,6.365223632,-79.93187273,32.77098914,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB,POINT (-79.93187273 32.770989143),10.0,10000.0
+574372721,574372721,RES,RES3A,10000.0,251962.21,7.12000971,-79.93191719,32.77164194,450190000000000.0,1,centroid,RES3A,RES3A,POINT (-79.93191719 32.771641938),10.0,10000.0
+574476728,574476728,RES,RES1-1SNB,96964.414,48482.207,5.867157192,-79.93241085,32.76996811,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB,POINT (-79.93241085 32.769968112),,
+574476811,574476811,RES,RES1-1SNB,152049.256,76024.6281,5.867157192,-79.93241085,32.76996811,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB,POINT (-79.93241085 32.769968112),,
+574486899,574486899,RES,RES3A,480189.335,240094.667,12.33366492,-79.93027436,32.77536076,450190000000000.0,1,centroid,RES3A,RES3A,POINT (-79.93027436 32.775360757),,
+574486932,574486932,RES,RES1-1SNB,177705.21,88852.6051,6.460120566,-79.92998998,32.77314466,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB,POINT (-79.92998998 32.773144664),,
+573827729,573827729,COM,COM8,37755.4885,37755.4885,11.17279794,-79.93080905,32.77672792,450190000000000.0,1,centroid,COM8,COM8,POINT (-79.93080905 32.776727923),,
+574421669,574421669,RES,RES3A,10000.0,85420.3788,11.52017961,-79.92907633,32.7771126,450190000000000.0,1,centroid,RES3A,RES3A,POINT (-79.92907633 32.777112595),8.0,10000.0
+574488781,574488781,RES,RES1-3SNB,10000.0,187435.541,5.99898202,-79.92578441,32.77724701,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB,POINT (-79.92578441 32.77724701),5.0,10000.0
+573467745,573467745,RES,RES3A,516183.793,258091.896,5.893531857,-79.92827685,32.77200663,450190000000000.0,1,centroid,RES3A,RES3A,POINT (-79.92827685 32.772006634),,
+573779323,573779323,RES,RES1-2SWB,437207.93,218603.965,6.975060909,-79.92874432,32.77330072,450190000000000.0,1,centroid,RES1-2SWB,RES1-2SWB,POINT (-79.92874432 32.77330072),,
+573779375,573779375,RES,RES1-1SNB,226202.175,113101.087,12.12123537,-79.92780556,32.7743702,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB,POINT (-79.92780556 32.7743702),,
+573779393,573779393,RES,RES3A,200255.163,100127.581,12.16771917,-79.92811631,32.77473003,450190000000000.0,1,centroid,RES3A,RES3A,POINT (-79.92811631 32.774730033),,
+573779405,573779405,RES,RES1-3SNB,398598.966,199299.483,10.82060957,-79.92862217,32.77481123,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB,POINT (-79.92862217 32.774811231),,
+573782594,573782594,RES,RES3A,489787.119,244893.559,6.500304423,-79.92975812,32.77311597,450190000000000.0,1,centroid,RES3A,RES3A,POINT (-79.92975812 32.773115965),,
+573782596,573782596,RES,RES3A,675402.418,337701.209,6.887255912,-79.93036119,32.77318439,450190000000000.0,1,centroid,RES3A,RES3A,POINT (-79.93036119 32.773184386),,
+573782413,573782413,RES,RES3A,437002.054,218501.027,7.119138325,-79.92871845,32.77219172,450190000000000.0,1,centroid,RES3A,RES3A,POINT (-79.92871845 32.772191724),,
+573782415,573782415,RES,RES1-3SNB,10000.0,184714.679,7.403951541,-79.92887994,32.77219043,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB,POINT (-79.92887994 32.772190428),7.0,10000.0
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+573467727,573467727,COM,RES4,10000.0,341420.064,5.998637455,-79.92868859,32.77061156,450190000000000.0,1,centroid,RES4,RES4,POINT (-79.92868859 32.770611556),7.0,10000.0
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+573467729,573467729,RES,RES3B,10000.0,319717.218,6.184400969,-79.92860345,32.77085682,450190000000000.0,1,centroid,RES3B,RES3B,POINT (-79.92860345 32.770856816),7.0,10000.0
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+573467731,573467731,RES,RES3A,10000.0,212010.861,7.376430152,-79.92924984,32.77110611,450190000000000.0,1,centroid,RES3A,RES3A,POINT (-79.92924984 32.77110611),7.0,10000.0
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+573779311,573779311,RES,RES1-2SNB,217879.449,108939.724,5.791196858,-79.92878222,32.77309016,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB,POINT (-79.92878222 32.773090161),,
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+574421681,574421681,COM,COM8,10000.0,778149.815,13.54457556,-79.92846361,32.77697303,450190000000000.0,1,centroid,COM8,COM8,POINT (-79.92846361 32.776973025),8.0,10000.0
+574421683,574421683,COM,COM1,10000.0,360197.146,13.54457556,-79.92846361,32.77697303,450190000000000.0,1,centroid,COM1,COM1,POINT (-79.92846361 32.776973025),8.0,10000.0
+574431298,574431298,COM,COM4,10000.0,1338267.91,10.1165989,-79.92646291,32.77728981,450190000000000.0,1,centroid,COM4,COM4,POINT (-79.92646291 32.77728981),5.0,10000.0
+574461065,574461065,RES,RES3A,10000.0,133492.964,12.13528704,-79.9297417,32.77681823,450190000000000.0,1,centroid,RES3A,RES3A,POINT (-79.9297417 32.776818232),8.0,10000.0
+574461073,574461073,COM,COM4,10000.0,177990.619,12.27511783,-79.92950828,32.77642489,450190000000000.0,1,centroid,COM4,COM4,POINT (-79.92950828 32.776424893),8.0,10000.0
+574461077,574461077,RES,RES3A,10000.0,136830.288,12.4406709,-79.92903083,32.7763684,450190000000000.0,1,centroid,RES3A,RES3A,POINT (-79.92903083 32.776368402),8.0,10000.0
+574461079,574461079,COM,COM4,10000.0,177990.619,12.86096032,-79.92860499,32.77648743,450190000000000.0,1,centroid,COM4,COM4,POINT (-79.92860499 32.776487427),8.0,10000.0
+574488771,574488771,RES,RES1-1SNB,10000.0,111403.033,13.26134397,-79.92896336,32.77597596,450190000000000.0,1,centroid,RES1-1SNB,RES1-1SNB,POINT (-79.92896336 32.775975959),8.0,10000.0
+574488793,574488793,RES,RES1-2SNB,10000.0,108451.83,12.29002837,-79.92736548,32.77640732,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB,POINT (-79.92736548 32.776407318),5.0,10000.0
+574488804,574488804,RES,RES1-2SNB,10000.0,146263.549,13.21022478,-79.92816916,32.77627743,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB,POINT (-79.92816916 32.776277427),8.0,10000.0
+574488806,574488806,RES,RES1-3SNB,10000.0,179350.234,13.36457265,-79.92824222,32.77627542,450190000000000.0,1,centroid,RES1-3SNB,RES1-3SNB,POINT (-79.92824222 32.776275418),8.0,10000.0
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+574488851,574488851,RES,RES1-2SNB,10000.0,111448.892,10.150554,-79.92671547,32.7755542,450190000000000.0,1,centroid,RES1-2SNB,RES1-2SNB,POINT (-79.92671547 32.775554202),5.0,10000.0
+574488874,574488874,RES,RES3A,10000.0,240094.667,8.900641428,-79.92914469,32.77176507,450190000000000.0,1,centroid,RES3A,RES3A,POINT (-79.92914469 32.77176507),7.0,10000.0
diff --git a/examples/data/update_max_potential_damage/test_update_max_potential_damage_polygons/settings.toml b/examples/data/update_max_potential_damage/test_update_max_potential_damage_polygons/settings.toml
new file mode 100644
index 00000000..c9018a8b
--- /dev/null
+++ b/examples/data/update_max_potential_damage/test_update_max_potential_damage_polygons/settings.toml
@@ -0,0 +1,22 @@
+[global]
+crs = "epsg:4326"
+
+[output]
+path = "output"
+
+[output.csv]
+name = "output.csv"
+
+[output.geom]
+name1 = "spatial.gpkg"
+
+[vulnerability]
+file = "./vulnerability/vulnerability_curves.csv"
+scale = 0.1
+
+[exposure.csv]
+file = "./exposure/exposure.csv"
+
+[exposure.geom]
+crs = "EPSG:4326"
+file1 = "./exposure/building_points.gpkg"
diff --git a/examples/data/update_max_potential_damage/test_update_max_potential_damage_polygons/vulnerability/vulnerability_curves.csv b/examples/data/update_max_potential_damage/test_update_max_potential_damage_polygons/vulnerability/vulnerability_curves.csv
new file mode 100644
index 00000000..81d37acf
--- /dev/null
+++ b/examples/data/update_max_potential_damage/test_update_max_potential_damage_polygons/vulnerability/vulnerability_curves.csv
@@ -0,0 +1,32 @@
+#UNIT=feet
+#METHOD,max,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean
+water depth [feet],AGR1,COM1,COM10,COM2,COM3,COM4,COM5,COM6,COM7,COM8,COM9,EDU1,GOV1,GOV2,IND1,IND2,IND3,IND4,IND5,IND6,REL1,RES1-1SNB,RES1-1SWB,RES1-2SNB,RES1-2SWB,RES1-3SNB,RES1-3SWB,RES2,RES3A,RES3B,RES3C,RES3D,RES3E,RES3F,RES4,RES5,RES6
+-4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,0.0,0.04,0.0,0.03,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,0.0,0.04,0.0,0.03,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,0.0,0.08,0.0,0.06,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.11,0.0,0.14,0.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+0.0,0.0,0.01,0.0,0.0,0.0,0.02,0.0,0.0,0.02,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.18,0.17,0.11,0.19,0.05,0.12,0.11,0.12,0.12,0.12,0.12,0.12,0.12,0.0,0.0,0.0
+1.0,0.06,0.09,0.03,0.05,0.09,0.11,0.11,0.0,0.11,0.09,0.02,0.05,0.05,0.07,0.1,0.09,0.13,0.1,0.13,0.22,0.1,0.22,0.21,0.12,0.21,0.08,0.14,0.44,0.25,0.25,0.25,0.25,0.25,0.25,0.03,0.07,0.07
+2.0,0.11,0.14,0.05,0.08,0.12,0.16,0.11,0.0,0.12,0.11,0.04,0.07,0.08,0.1,0.12,0.14,0.14,0.14,0.14,0.31,0.11,0.25,0.29,0.14,0.26,0.12,0.2,0.63,0.5,0.5,0.5,0.5,0.5,0.5,0.05,0.1,0.1
+3.0,0.15,0.16,0.06,0.11,0.13,0.22,0.12,0.2,0.13,0.12,0.05,0.09,0.13,0.11,0.15,0.17,0.19,0.18,0.19,0.37,0.11,0.28,0.34,0.18,0.29,0.17,0.25,0.73,0.75,0.75,0.75,0.75,0.75,0.75,0.06,0.14,0.14
+4.0,0.19,0.18,0.07,0.13,0.16,0.28,0.13,0.25,0.14,0.14,0.05,0.09,0.14,0.12,0.19,0.22,0.22,0.22,0.22,0.43,0.12,0.3,0.38,0.2,0.34,0.19,0.31,0.78,1.0,1.0,1.0,1.0,1.0,1.0,0.07,0.15,0.15
+5.0,0.25,0.2,0.08,0.16,0.19,0.35,0.15,0.3,0.16,0.16,0.05,0.1,0.14,0.15,0.22,0.26,0.25,0.26,0.25,0.47,0.12,0.31,0.43,0.22,0.39,0.22,0.36,0.79,1.0,1.0,1.0,1.0,1.0,1.0,0.09,0.15,0.15
+6.0,0.3,0.23,0.1,0.19,0.22,0.38,0.17,0.35,0.17,0.18,0.06,0.11,0.15,0.17,0.26,0.3,0.28,0.34,0.28,0.5,0.13,0.4,0.5,0.24,0.44,0.24,0.38,0.81,1.0,1.0,1.0,1.0,1.0,1.0,0.12,0.16,0.16
+7.0,0.35,0.26,0.13,0.22,0.25,0.41,0.19,0.4,0.18,0.2,0.08,0.13,0.17,0.2,0.3,0.32,0.3,0.41,0.3,0.54,0.14,0.43,0.5,0.26,0.5,0.25,0.41,0.82,1.0,1.0,1.0,1.0,1.0,1.0,0.14,0.18,0.18
+8.0,0.41,0.3,0.17,0.25,0.28,0.44,0.22,0.43,0.2,0.22,0.1,0.15,0.19,0.23,0.35,0.35,0.33,0.42,0.33,0.57,0.14,0.43,0.54,0.3,0.55,0.3,0.44,0.83,1.0,1.0,1.0,1.0,1.0,1.0,0.18,0.2,0.2
+9.0,0.46,0.34,0.21,0.29,0.32,0.47,0.24,0.47,0.22,0.26,0.12,0.17,0.22,0.27,0.39,0.37,0.34,0.42,0.34,0.61,0.15,0.45,0.55,0.34,0.57,0.35,0.48,0.84,1.0,1.0,1.0,1.0,1.0,1.0,0.21,0.23,0.23
+10.0,0.51,0.38,0.25,0.32,0.35,0.5,0.28,0.5,0.24,0.29,0.15,0.2,0.26,0.31,0.42,0.39,0.36,0.45,0.36,0.63,0.17,0.46,0.55,0.38,0.59,0.38,0.5,0.85,1.0,1.0,1.0,1.0,1.0,1.0,0.26,0.26,0.26
+11.0,0.57,0.42,0.3,0.37,0.39,0.54,0.31,0.53,0.27,0.33,0.2,0.24,0.31,0.35,0.48,0.43,0.39,0.47,0.39,0.64,0.19,0.47,0.57,0.39,0.61,0.39,0.52,0.86,1.0,1.0,1.0,1.0,1.0,1.0,0.31,0.3,0.3
+12.0,0.63,0.47,0.35,0.41,0.43,0.57,0.34,0.55,0.3,0.37,0.24,0.28,0.37,0.4,0.5,0.46,0.4,0.49,0.4,0.65,0.24,0.47,0.58,0.4,0.63,0.4,0.54,0.88,1.0,1.0,1.0,1.0,1.0,1.0,0.36,0.34,0.34
+13.0,0.7,0.51,0.41,0.45,0.47,0.59,0.37,0.57,0.34,0.41,0.29,0.33,0.44,0.44,0.51,0.48,0.42,0.5,0.42,0.67,0.3,0.49,0.6,0.42,0.65,0.42,0.56,0.89,1.0,1.0,1.0,1.0,1.0,1.0,0.41,0.38,0.38
+14.0,0.75,0.55,0.47,0.49,0.5,0.62,0.4,0.6,0.37,0.45,0.35,0.39,0.51,0.48,0.53,0.5,0.42,0.5,0.42,0.68,0.38,0.5,0.62,0.43,0.66,0.43,0.57,0.9,1.0,1.0,1.0,1.0,1.0,1.0,0.46,0.42,0.42
+15.0,0.79,0.58,0.52,0.52,0.54,0.66,0.44,0.6,0.41,0.5,0.42,0.45,0.59,0.52,0.54,0.51,0.43,0.5,0.43,0.69,0.45,0.5,0.63,0.44,0.68,0.44,0.59,0.91,1.0,1.0,1.0,1.0,1.0,1.0,0.5,0.47,0.47
+16.0,0.82,0.61,0.58,0.55,0.57,0.68,0.48,0.6,0.44,0.53,0.49,0.52,0.65,0.56,0.55,0.54,0.43,0.5,0.43,0.7,0.52,0.5,0.65,0.45,0.69,0.45,0.61,0.92,1.0,1.0,1.0,1.0,1.0,1.0,0.54,0.52,0.52
+17.0,0.84,0.64,0.65,0.58,0.61,0.7,0.51,0.6,0.48,0.57,0.56,0.59,0.7,0.6,0.55,0.55,0.44,0.5,0.44,0.71,0.58,0.51,0.67,0.47,0.71,0.47,0.63,0.94,1.0,1.0,1.0,1.0,1.0,1.0,0.58,0.57,0.57
+18.0,0.87,0.67,0.71,0.61,0.64,0.72,0.55,0.6,0.51,0.6,0.62,0.64,0.74,0.64,0.56,0.57,0.44,0.5,0.44,0.72,0.64,0.51,0.69,0.48,0.72,0.48,0.65,0.95,1.0,1.0,1.0,1.0,1.0,1.0,0.62,0.62,0.62
+19.0,0.89,0.69,0.76,0.63,0.68,0.74,0.59,0.6,0.54,0.63,0.68,0.69,0.79,0.68,0.56,0.59,0.44,0.5,0.44,0.73,0.69,0.52,0.7,0.49,0.74,0.49,0.67,0.96,1.0,1.0,1.0,1.0,1.0,1.0,0.66,0.67,0.67
+20.0,0.9,0.71,0.81,0.66,0.71,0.76,0.63,0.6,0.56,0.66,0.74,0.74,0.83,0.72,0.57,0.6,0.44,0.5,0.44,0.74,0.74,0.52,0.72,0.5,0.75,0.5,0.69,0.97,1.0,1.0,1.0,1.0,1.0,1.0,0.7,0.72,0.72
+21.0,0.92,0.74,0.86,0.68,0.75,0.77,0.67,0.6,0.59,0.69,0.8,0.79,0.87,0.76,0.57,0.62,0.44,0.5,0.44,0.75,0.78,0.53,0.74,0.52,0.77,0.52,0.71,0.98,1.0,1.0,1.0,1.0,1.0,1.0,0.74,0.77,0.77
+22.0,0.93,0.76,0.91,0.7,0.78,0.78,0.71,0.6,0.61,0.73,0.86,0.84,0.91,0.8,0.57,0.63,0.45,0.5,0.45,0.76,0.82,0.53,0.76,0.53,0.79,0.53,0.73,0.99,1.0,1.0,1.0,1.0,1.0,1.0,0.78,0.82,0.82
+23.0,0.95,0.78,0.96,0.71,0.82,0.79,0.75,0.6,0.64,0.76,0.92,0.89,0.95,0.84,0.58,0.65,0.45,0.5,0.45,0.76,0.85,0.54,0.77,0.54,0.8,0.54,0.75,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.82,0.87,0.87
+24.0,0.96,0.8,1.0,0.73,0.85,0.8,0.79,0.6,0.66,0.78,0.98,0.94,0.98,0.88,0.58,0.66,0.45,0.5,0.45,0.77,0.88,0.54,0.79,0.56,0.82,0.56,0.77,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.86,0.92,0.92
diff --git a/hydromt_fiat/data/damage_functions/flooding/JRC_damage_functions_linking.csv b/examples/data/vulnerability_linking/JRC_damage_functions_linking.csv
similarity index 67%
rename from hydromt_fiat/data/damage_functions/flooding/JRC_damage_functions_linking.csv
rename to examples/data/vulnerability_linking/JRC_damage_functions_linking.csv
index 65e94202..6f711297 100644
--- a/hydromt_fiat/data/damage_functions/flooding/JRC_damage_functions_linking.csv
+++ b/examples/data/vulnerability_linking/JRC_damage_functions_linking.csv
@@ -1,4 +1,4 @@
-Name,Link,Damage Type,type
+FIAT Damage Function Name,Exposure Link,Damage Type,type
residential,residential,total,residential
commercial,commercial,total,commercial
industrial,industrial,total,industrial
diff --git a/examples/data/vulnerability_test_file_input.csv b/examples/data/vulnerability_test_file_input.csv
deleted file mode 100644
index b9d6875b..00000000
--- a/examples/data/vulnerability_test_file_input.csv
+++ /dev/null
@@ -1,38 +0,0 @@
-Name,Link,Damage Type,BldgDmgFnID,Occupancy,Source,Description
-AGR1,AGR1,structure,616,AGR1 ,USACE - Galveston,"Average Agriculture, structure"
-COM1,COM1,structure,217,COM1 ,USACE - Galveston,"Average Retail, Structure"
-COM10,COM10,structure,543,COM10,USACE - Galveston,"Garage, structure"
-COM2,COM2,structure,341,COM2 ,USACE - Galveston,"Average wholesale, Structure"
-COM3,COM3,structure,375,COM3 ,USACE - Galveston,"Average Personal & Repair Services, Structure"
-COM4,COM4,structure,431,COM4 ,USACE - Galveston,"Average Prof/Tech Services, Structure"
-COM5,COM5,structure,467,COM5 ,USACE - Galveston,"Bank, Structure"
-COM6,COM6,structure,474,COM6 ,USACE - Galveston,"Hospital, Structure"
-COM7,COM7,structure,475,COM7 ,USACE - Galveston,"Average Medical Office, Structure"
-COM8,COM8,structure,493,COM8 ,USACE - Galveston,"Average Entertainment/Recreation, Structure"
-COM9,COM9,structure,532,COM9 ,USACE - Galveston,"Average theatre, Structure"
-EDU1,EDU1,structure,643,EDU1 ,USACE - Galveston,"Average school, structure"
-GOV1,GOV1,structure,631,GOV1 ,USACE - Galveston,"Average government services, structure"
-GOV2,GOV2,structure,640,GOV2 ,USACE - Galveston,"Average emergency response, Structure"
-IND1,IND1,structure,545,IND1 ,USACE - Galveston,"Average heavy industrial, Structure"
-IND2,IND2,structure,559,IND2 ,USACE - Galveston,"Average light industrial, structure"
-IND3,IND3,structure,575,IND3 ,USACE - Galveston,"Average Food/Drug/Chem, Structure"
-IND4,IND4,structure,586,IND4 ,USACE - Galveston,"Average Metals/Minerals processing, structure"
-IND5,IND5,structure,591,IND5 ,USACE - Galveston,"Average High Technology, structure"
-IND6,IND6,structure,592,IND6 ,USACE - Galveston,"Average Construction, structure"
-REL1,REL1,structure,624,REL1 ,USACE - Galveston,"Church, structure"
-RES1-1SNB,RES1-1SNB,structure,105,RES1 ,FIA,"one floor, no basement, Structure, A-Zone"
-RES1-1SWB,RES1-1SWB,structure,106,RES1 ,FIA (MOD.),"one floor, w/ basement, Structure, A-Zone"
-RES1-2SNB,RES1-2SNB,structure,107,RES1 ,FIA,"two floors, no basement, Structure, A-Zone"
-RES1-2SWB,RES1-2SWB,structure,108,RES1 ,FIA (MOD.),"two floors, w/ basement, Structure, A-Zone"
-RES1-3SNB,RES1-3SNB,structure,109,RES1 ,FIA,"three or more floors, no basement, Structure, A-Zone"
-RES1-3SWB,RES1-3SWB,structure,110,RES1 ,FIA (MOD.),"three or more floors, w/ basement, Structure, A-Zone"
-RES2,RES2,structure,189,RES2 ,FIA,"Mobile home, structure, A-Zone"
-RES3A,RES3A,structure,659,RES3A,BCAR - Jan 2011,"1to2 Stories, slab - no basement, Coastal A or V Zone"
-RES3B,RES3B,structure,660,RES3B,BCAR - Jan 2011,"1to2 Stories, slab - no basement, Coastal A or V Zone"
-RES3C,RES3C,structure,660,RES3B,BCAR - Jan 2011,"1to2 Stories, slab - no basement, Coastal A or V Zone"
-RES3D,RES3D,structure,660,RES3B,BCAR - Jan 2011,"1to2 Stories, slab - no basement, Coastal A or V Zone"
-RES3E,RES3E,structure,660,RES3B,BCAR - Jan 2011,"1to2 Stories, slab - no basement, Coastal A or V Zone"
-RES3F,RES3F,structure,660,RES3B,BCAR - Jan 2011,"1to2 Stories, slab - no basement, Coastal A or V Zone"
-RES4,RES4,structure,209,RES4 ,USACE - Galveston,"Average Hotel & Motel, structure"
-RES5,RES5,structure,214,RES5 ,USACE - Galveston,"Average institutional dormitory, structure"
-RES6,RES6,structure,215,RES6 ,USACE - Galveston,"Nursing Home, structure"
diff --git a/examples/data/vulnerability_test_file_input_miami_landuse.csv b/examples/data/vulnerability_test_file_input_miami_landuse.csv
deleted file mode 100644
index cd5b6a9f..00000000
--- a/examples/data/vulnerability_test_file_input_miami_landuse.csv
+++ /dev/null
@@ -1,28 +0,0 @@
-Name,Link,Damage Type,BldgDmgFnID
-"Single-Family, Med.-Density (2-5 DU/Gross Acre).",RES1 ,structure,105
-"Multi-Family, Low-Density (Under 25 DU/Gross Acre).",RES3,structure,204
-"Single-Family, High Density (Over 5 DU/Gross Acre, other than Townhouses, Duplexes and Mobile Homes).",RES1 ,structure,107
-Vacant Government owned or controlled.,RES2 ,structure,199
-"Vacant, Non-Protected, Privately-Owned.",RES2 ,structure,199
-"Single-Family, Low-Density (Under 2 DU/Gross Acre).",RES1 ,structure,105
-"Houses of Worship and Religious, and associated uses (parking, retreat houses, residencies, childcare, etc.).",GOV1 ,structure,634
-Two-Family (Duplexes).,RES3,structure,204
-"Public Schools, Including Playgrounds (K-12, Vocational Ed., Day Care and Child Nurseries).",EDU1 ,structure,643
-"Nursing homes, Assisted living facilities, and Adult congregate living quarters",GOV1 ,structure,634
-Plant Nurseries (Includes Sod Farms and Ornamental Nurseries).,GOV1 ,structure,634
-"Private Recreational Facilities Associated with private Residential Developments, except marinas/yacht basins, includes landscape and open spaces associated to residential, commercial and office developments.",RES1 ,structure,105
-Townhouses.,RES1 ,structure,105
-Office Building.,COM8 ,structure,526
-"Industrial Intensive, Office type of use",IND5,structure,591
-Governmental/Public Administration (Other than Military or Penal).,GOV2 ,structure,640
-"Street right-of-way and entrance features both public and private, and utility easements.",GOV2 ,structure,640
-Municipal Operated Parks,GOV2 ,structure,640
-Residential MF-- government-owned or government subsidized multi-family residential or elderly housing,RES3,structure,204
-"Sales and Services (Wholesale facilities, Spot commercial, strip commercial, neighborhood shopping centers/plazas). Excludes office facilities.",COM2 ,structure,341
-"Private Schools, Including Playgrounds (K-12, Vocational Ed., Day Care and Child Nurseries).",EDU1 ,structure,643
-"Hospitals, clinics, medical offices and/or dental facilities",GOV2 ,structure,640
-Shopping Centers (Regional and Community).,COM2 ,structure,341
-Water Supply Plants.,IND3 ,structure,575
-"Sports Stadiums, Arenas, and Tracks.",COM8 ,structure,526
-County Operated Parks.,GOV2 ,structure,640
-"Other Nature Preserves and Protected Areas (State Mangrove Preserves, Turkey Point Wilderness Area, Great Cypress Swamp Preserves, and acquired government owned EEL sites).",GOV2 ,structure,640
diff --git a/examples/global_OSM_JRC.ipynb b/examples/global_OSM_JRC.ipynb
index bee5c411..d5aea6e8 100644
--- a/examples/global_OSM_JRC.ipynb
+++ b/examples/global_OSM_JRC.ipynb
@@ -76,7 +76,7 @@
"outputs": [],
"source": [
"model_folder = Path(model_root) / model_name # full path to model folder\n",
- "data_catalog = Path(os.path.abspath(\"\")) / \"..\" / \"hydromt_fiat\" / \"data\" / \"hydromt_fiat_catalog_global.yml\" # path to data catalog relative to this notebook\n",
+ "data_catalog = Path(os.path.abspath(\"\")) / \"data\" / \"hydromt_fiat_catalog_global.yml\" # path to data catalog relative to this notebook\n",
"logger_name = \"hydromt_fiat\" # name of the logger\n",
"logger = setuplog(logger_name, log_level=10) # setup logger"
]
@@ -207,6 +207,7 @@
"occupancy_type = \"OSM\"\n",
"max_potential_damage = \"jrc_damage_values\"\n",
"ground_floor_height = 0\n",
+ "damage_types = [\"total\"]\n",
"unit = \"m\"\n",
"\n",
"### Setup output parameters ###\n",
@@ -242,12 +243,13 @@
" \"continent\": continent,\n",
" \"unit\": unit,\n",
" },\n",
- " \"setup_exposure_vector\": {\n",
+ " \"setup_exposure_buildings\": {\n",
" \"asset_locations\": asset_locations,\n",
" \"occupancy_type\": occupancy_type,\n",
" \"max_potential_damage\": max_potential_damage,\n",
" \"ground_floor_height\": ground_floor_height,\n",
" \"unit\": unit,\n",
+ " \"damage_types\": damage_types,\n",
" \"country\": country,\n",
" },\n",
"}"
@@ -351,7 +353,7 @@
"metadata": {},
"outputs": [],
"source": [
- "gdf.explore(column='Max Potential Damage: Structure')"
+ "gdf.explore(column='Max Potential Damage: Total')"
]
},
{
diff --git a/examples/update_exposure_data.ipynb b/examples/update_exposure_data.ipynb
new file mode 100644
index 00000000..934ee17e
--- /dev/null
+++ b/examples/update_exposure_data.ipynb
@@ -0,0 +1,1069 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Update exposure data with user-input data\n",
+ "\n",
+ "This notebook demonstrates how to update the exposure data in an existing Delft-FIAT model with user-input data. Examples will be shown about how to update the ground floor height and the max potential damages (this will be added later!).\n",
+ "\n",
+ "## **Step 0**: Import required packages\n",
+ "First we need to import the necessary python packages."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from hydromt_fiat.fiat import FiatModel\n",
+ "from hydromt.log import setuplog\n",
+ "from pathlib import Path\n",
+ "import shutil\n",
+ "import copy\n",
+ "import geopandas as gpd\n",
+ "from hydromt.gis_utils import utm_crs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## **Step 1**: Configure\n",
+ "The first step is to set up the configuration needed to initialize the FIAT model. Begin by specifying where the model that should be updated is saved, locating the required data catalog, and setting up the logger."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2023-10-18 13:36:08,112 - hydromt_fiat - log - INFO - HydroMT version: 0.8.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "model_folder = Path().absolute() / \"data\" / \"update_ground_floor_height\" / \"fiat_model\"\n",
+ "data_catalog = Path().absolute() / \"data\" / \"hydromt_fiat_catalog_USA.yml\"\n",
+ "logger = setuplog(\"hydromt_fiat\", log_level=10)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## **Step 2**: Initialize and read existing model\n",
+ "In this step we initialize HydroMT-FIAT in read-mode (`mode=\"r\"`) with the `model_folder`, `data_catalog`, and `logger` that we defined above. We also read in the existing dummy model."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2023-10-18 13:36:08,144 - hydromt_fiat - data_catalog - INFO - Parsing data catalog from c:\\Repositories\\hydromt_fiat\\examples\\data\\hydromt_fiat_catalog_USA.yml\n",
+ "2023-10-18 13:36:08,148 - hydromt_fiat - log - DEBUG - Appending log messages to file c:\\Repositories\\hydromt_fiat\\examples\\data\\update_ground_floor_height\\fiat_model\\hydromt.log.\n",
+ "2023-10-18 13:36:08,148 - hydromt_fiat - model_api - INFO - Initializing fiat model from hydromt_fiat (v0.2.1.dev0).\n",
+ "2023-10-18 13:36:08,149 - hydromt_fiat - fiat - INFO - Reading model data from c:\\Repositories\\hydromt_fiat\\examples\\data\\update_ground_floor_height\\fiat_model\n",
+ "2023-10-18 13:36:08,150 - hydromt_fiat - model_api - DEBUG - User defined config read from c:\\Repositories\\hydromt_fiat\\examples\\data\\update_ground_floor_height\\fiat_model\\settings.toml\n",
+ "2023-10-18 13:36:08,150 - hydromt_fiat - fiat - INFO - Reading model table files.\n",
+ "2023-10-18 13:36:08,151 - hydromt_fiat - fiat - DEBUG - Reading vulnerability table c:\\Repositories\\hydromt_fiat\\examples\\data\\update_ground_floor_height\\fiat_model\\vulnerability\\vulnerability_curves.csv\n",
+ "2023-10-18 13:36:08,208 - hydromt_fiat - fiat - DEBUG - Reading exposure table c:\\Repositories\\hydromt_fiat\\examples\\data\\update_ground_floor_height\\fiat_model\\exposure\\exposure.csv\n",
+ "2023-10-18 13:36:08,220 - hydromt_fiat - fiat - INFO - Reading exposure geometries.\n",
+ "2023-10-18 13:36:08,221 - hydromt_fiat - exposure_vector - INFO - Setting geometry name to building_points...\n",
+ "2023-10-18 13:36:08,256 - hydromt_fiat - exposure_vector - INFO - Setting exposure geometries...\n"
+ ]
+ }
+ ],
+ "source": [
+ "fm = FiatModel(root=model_folder, mode=\"r\", data_libs=[data_catalog], logger=logger)\n",
+ "fm.read()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## **Step 3**: Update the Ground Floor Height of the exposure data\n",
+ "Next, we will update the Ground Floor Height of some of the exposure assets in our model. We have created some ficticious data that represents elevation certificates. We will first inspect the current exposure data and ground floor heights and the \"elevation certificate\" data.\n",
+ "\n",
+ "As can be seen below, apparently the ground floor height of all assets is set to 1 (foot)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([1], dtype=int64)"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "original_exposure = fm.exposure.get_full_gdf(fm.exposure.exposure_db)\n",
+ "original_exposure[\"Ground Floor Height\"].unique()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now we will load the \"elevation certificate\" data and visualize it together with the exposure assets below on the map."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "Make this Notebook Trusted to load map: File -> Trust Notebook
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 42,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "elevation_certificates_path = Path().absolute() / \"data\" / \"update_ground_floor_height\" / \"fake_elevation_certificates.gpkg\"\n",
+ "elevation_certificates = gpd.read_file(elevation_certificates_path)\n",
+ "\n",
+ "# Plot the exposure and elevation certificates data\n",
+ "m = elevation_certificates.explore(column='FFE', cmap=\"Reds\", style_kwds={'radius': 5})\n",
+ "m = original_exposure.explore(m=m, column=\"Ground Floor Height\", cmap=\"Reds\")\n",
+ "m"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We will use a maximum distance of 10 meter to join the elevation certificate data to their nearest building. The attribute in the elevation certificate data that we need to use is `FFE`, which stands for First Floor Elevation and is often used in the US to indicate the Ground Floor Height with."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2023-10-18 13:51:17,288 - hydromt_fiat - data_catalog - INFO - DataCatalog: Getting fake_elevation_certificates GeoDataFrame vector data from c:\\Repositories\\hydromt_fiat\\examples\\data\\update_ground_floor_height\\fake_elevation_certificates.gpkg\n",
+ "2023-10-18 13:51:17,293 - hydromt_fiat - geodataframe - INFO - GeoDataFrame: Read vector data.\n"
+ ]
+ }
+ ],
+ "source": [
+ "attribute = \"FFE\"\n",
+ "method = \"nearest\"\n",
+ "max_dist = 50\n",
+ "\n",
+ "fm.exposure.setup_ground_floor_height(\n",
+ " ground_floor_height=elevation_certificates_path,\n",
+ " attr_name=attribute,\n",
+ " method=method,\n",
+ " max_dist=max_dist,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## **Step 4**: Check the resulting exposure data\n",
+ "As you can see below, the ground floor height of the exposure assets also contains other values."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([ 1, 2, 11, 20, 10, 12], dtype=int64)"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "updated_exposure = fm.exposure.get_full_gdf(fm.exposure.exposure_db)\n",
+ "updated_exposure[\"Ground Floor Height\"].unique()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We will make a buffer of 10 meter around the "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "Make this Notebook Trusted to load map: File -> Trust Notebook
"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 39,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Check if the Ground Floor Heigh attribute is set correctly\n",
+ "nearest_utm = utm_crs(elevation_certificates.total_bounds)\n",
+ "elevation_certificates_projected = elevation_certificates.to_crs(nearest_utm)\n",
+ "elevation_certificates_projected.geometry = elevation_certificates_projected.geometry.buffer(max_dist)\n",
+ "\n",
+ "# Plot the exposure and elevation certificates data\n",
+ "m = elevation_certificates_projected.explore(column='FFE', cmap=\"Reds\")\n",
+ "m = updated_exposure.explore(m=m, column=\"Ground Floor Height\", cmap=\"Reds\")\n",
+ "m"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## **Step 5**: Save the updated FIAT model\n",
+ "Set the folder where you want to save the model to and write it to that folder."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "2023-10-18 15:53:42,160 - hydromt_fiat - log - DEBUG - Writing log messages to new file c:\\Repositories\\hydromt_fiat\\examples\\data\\update_ground_floor_height\\updated_fiat_model\\hydromt.log.\n",
+ "2023-10-18 15:53:42,161 - hydromt_fiat - fiat - INFO - Updating all data objects...\n",
+ "2023-10-18 15:53:42,162 - hydromt_fiat - model_api - WARNING - Replacing geom: building_points\n",
+ "2023-10-18 15:53:42,163 - hydromt_fiat - fiat - INFO - Writing model data to c:\\Repositories\\hydromt_fiat\\examples\\data\\update_ground_floor_height\\updated_fiat_model\n",
+ "2023-10-18 15:53:42,163 - hydromt_fiat - model_api - INFO - Writing model config to c:\\Repositories\\hydromt_fiat\\examples\\data\\update_ground_floor_height\\updated_fiat_model\\settings.toml\n",
+ "2023-10-18 15:53:42,165 - hydromt_fiat - model_api - DEBUG - Writing file exposure/building_points.gpkg\n",
+ "2023-10-18 15:53:42,256 - hydromt_fiat - fiat - INFO - Writing model vulnerability_curves table file to vulnerability/vulnerability_curves.csv.\n",
+ "2023-10-18 15:53:42,260 - hydromt_fiat - fiat - INFO - Writing model exposure table file to exposure/exposure.csv.\n"
+ ]
+ }
+ ],
+ "source": [
+ "save_folder = Path().absolute() / \"data\" / \"update_ground_floor_height\" / \"updated_fiat_model\"\n",
+ "\n",
+ "# Remove the new root folder if it already exists\n",
+ "if save_folder.exists():\n",
+ " shutil.rmtree(save_folder)\n",
+ "\n",
+ "# Set the new root and write the model\n",
+ "fm.set_root(save_folder)\n",
+ "fm.write()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "hydromt-fiat-dev",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/hydromt_fiat/api/data_types.py b/hydromt_fiat/api/data_types.py
index f1421399..c4c8ad46 100644
--- a/hydromt_fiat/api/data_types.py
+++ b/hydromt_fiat/api/data_types.py
@@ -1,7 +1,7 @@
from enum import Enum
-from typing_extensions import Optional, TypedDict, Union
+from typing_extensions import Optional, TypedDict, Union, List
-from pydantic import BaseModel
+from pydantic import BaseModel, Extra
class ExtractionMethod(str, Enum):
@@ -10,8 +10,8 @@ class ExtractionMethod(str, Enum):
class Units(str, Enum):
- m = "meter"
- ft = "feet"
+ m = "m"
+ ft = "ft"
class Category(str, Enum):
@@ -46,43 +46,85 @@ class DataCatalogEntry(BaseModel):
meta: Meta
-class ModelIni(BaseModel):
- site_name: str
- scenario_name: str
+class GlobalSettings(BaseModel):
+ crs: Union[str, int]
+
+
+class OutputSettings(BaseModel):
output_dir: str
- crs: str
+ output_csv_name: str
+ output_vector_name: str
+
+class VulnerabilitySettings(BaseModel):
+ vulnerability_fn: str
+ vulnerability_identifiers_and_linking_fn: str
+ unit: Units
+ functions_mean: Union[str, list]
+ functions_max: Union[str, list, None]
+ step_size: Union[float, None]
-class ExposureVectorIni(BaseModel):
+
+class ExposureBuildingsSettings(BaseModel):
asset_locations: str
occupancy_type: str
max_potential_damage: str
ground_floor_height: str
unit: Units
extraction_method: ExtractionMethod
+ damage_types : Union[List[str], None]
-class HazardIni(BaseModel):
- hazard_map_fn: str
- hazard_type: str
- return_period: Optional[Union[int, None]] = None
- crs: Optional[str] = None
- no_data: Optional[int] = -9999
- var: Optional[Union[str, None]] = None
- chunks: Optional[Union[int, str]] = "auto"
- no_data: Optional[int] = -9999
- var: Optional[Union[str, None]] = None
- chunks: Optional[Union[int, str]] = "auto"
+class ExposureSetupGroundFloorHeight(BaseModel):
+ source: str
+ attribute_name: Union[str, List[str], None]
+ method: Union[str, List[str], None]
+ max_dist: Union[float, int, None]
-class VulnerabilityIni(BaseModel):
- vulnerability_fn: str
- link_table: str
- units: Units
+class ExposureSetupGroundElevation(BaseModel):
+ source: Union[int, float, None, str]
+
+
+class ExposureSetupDamages(BaseModel):
+ source: str
+ attribute_name: Union[str, List[str], None]
+ method: Union[str, List[str], None]
+ max_dist: Union[float, int, None]
+
+
+class RoadVulnerabilitySettings(BaseModel):
+ threshold_value: float
+ min_hazard_value: float
+ max_hazard_value: float
+ step_hazard_value: float
+ vertical_unit: Units
+
+
+class ExposureRoadsSettings(BaseModel):
+ roads_fn: str
+ road_types: Union[List[str], bool]
+ road_damage: int
+ unit: Units
+
+
+class AggregationAreaSettings(BaseModel):
+ aggregation_area_fn: Union[List[str], str]
+ attribute_names: Union[List[str], str]
+ label_names: Union[List[str], str]
+
+
+class SocialVulnerabilityIndexSettings(BaseModel):
+ census_key: str
+ codebook_fn: str
+ year_data: int
+
+
+class EquityDataSettings(BaseModel):
+ census_key: str
+ year_data: int
-class ConfigIni(BaseModel):
- setup_config: ModelIni
- setup_hazard: HazardIni
- setup_vulnerability: VulnerabilityIni
- setup_exposure_vector: ExposureVectorIni
+class ConfigYaml(BaseModel, extra=Extra.allow):
+ setup_global_settings: GlobalSettings
+ setup_output: OutputSettings
\ No newline at end of file
diff --git a/hydromt_fiat/api/exposure_vm.py b/hydromt_fiat/api/exposure_vm.py
index da07bbc0..aa886482 100644
--- a/hydromt_fiat/api/exposure_vm.py
+++ b/hydromt_fiat/api/exposure_vm.py
@@ -1,4 +1,4 @@
-from typing import Dict, Optional, Union
+from typing import Dict, Optional, Union, List
from hydromt import DataCatalog
@@ -12,8 +12,13 @@
DataCatalogEntry,
DataType,
Driver,
- ExposureVectorIni,
+ ExposureBuildingsSettings,
+ ExposureSetupGroundFloorHeight,
+ ExposureSetupDamages,
+ ExposureSetupGroundElevation,
+ ExposureRoadsSettings,
ExtractionMethod,
+ AggregationAreaSettings,
Units,
)
@@ -22,17 +27,17 @@ class ExposureViewModel:
def __init__(
self, database: IDatabase, data_catalog: DataCatalog, logger: logging.Logger
):
- self.exposure_model = ExposureVectorIni(
- asset_locations="",
- occupancy_type="",
- max_potential_damage=-999,
- ground_floor_height=-999,
- unit=Units.m.value,
- extraction_method=ExtractionMethod.centroid.value,
- )
+ self.exposure_buildings_model = None
+ self.exposure_roads_model = None
+ self.aggregation_areas_model = None
+ self.exposure_ground_floor_height_model = None
+ self.exposure_damages_model = None
+ self.exposure_ground_elevation_model = None
+
self.database: IDatabase = database
self.data_catalog: DataCatalog = data_catalog
self.logger: logging.Logger = logger
+ self.exposure: ExposureVector = None
def create_interest_area(self, **kwargs: str):
fpath = kwargs.get("fpath")
@@ -51,57 +56,30 @@ def create_interest_area(self, **kwargs: str):
def set_asset_locations_source(
self,
- input_source: str,
+ source: str,
+ ground_floor_height: str,
fiat_key_maps: Optional[Dict[str, str]] = None,
crs: Union[str, int] = None,
- ):
- if input_source == "NSI":
+ ) -> None:
+ if source == "NSI":
# NSI is already defined in the data catalog
- # Add NSI to the configuration file
- self.exposure_model.asset_locations = input_source
- self.exposure_model.occupancy_type = input_source
- self.exposure_model.max_potential_damage = input_source
- self.exposure_model.ground_floor_height = 1 # TODO: make flexible
- self.exposure_model.unit = (
- Units.ft.value
- ) # TODO: make flexible
-
- # Download NSI from the database
- region = self.data_catalog.get_geodataframe("area_of_interest")
- exposure = ExposureVector(
- data_catalog=self.data_catalog,
- logger=self.logger,
- region=region,
- crs=crs,
- )
-
- exposure.setup_from_single_source(
- input_source,
- self.exposure_model.ground_floor_height,
- "centroid", # TODO: MAKE FLEXIBLE
- )
- primary_object_types = (
- exposure.exposure_db["Primary Object Type"].unique().tolist()
- )
- secondary_object_types = (
- exposure.exposure_db["Secondary Object Type"].unique().tolist()
- )
- exposure.set_exposure_geoms_from_xy()
-
- return (
- exposure.exposure_geoms[0],
- primary_object_types,
- secondary_object_types,
+ self.exposure_buildings_model = ExposureBuildingsSettings(
+ asset_locations=source,
+ occupancy_type=source,
+ max_potential_damage=source,
+ ground_floor_height=ground_floor_height,
+ unit=Units.ft.value, # TODO: make flexible
+ extraction_method=ExtractionMethod.centroid.value,
+ damage_types=["structure", "content"],
)
-
- elif input_source == "file" and fiat_key_maps is not None:
+ elif source == "file" and fiat_key_maps is not None:
# maybe save fiat_key_maps file in database
# make calls to backend to derive file meta info such as crs, data type and driver
crs: str = "4326"
# save keymaps to database
catalog_entry = DataCatalogEntry(
- path=input_source,
+ path=source,
data_type="GeoDataFrame",
driver="vector",
crs=crs,
@@ -112,13 +90,125 @@ def set_asset_locations_source(
print(catalog_entry)
# write to data catalog
- def create_extraction_map(self, *args):
- # TODO: implement callback
- # if no exceptions, then self.exposure_model.extraction_method = args[0]
- # else if
- # make backend call to api with arguments to set extraction method per object:
- # create first with default method. Then get uploaded or drawn area and merge with default methid
- # save file to database
- # change self.exposure_model.extraction_method to file
- ...
- # change self.exposure_model.extraction_method to file
+ def get_object_types(self):
+ if self.exposure:
+ primary_object_types = (
+ self.exposure.exposure_db["Primary Object Type"].unique().tolist()
+ )
+ secondary_object_types = (
+ self.exposure.exposure_db["Secondary Object Type"].unique().tolist()
+ )
+ return (
+ primary_object_types,
+ secondary_object_types,
+ )
+
+ def set_asset_data_source(self, source):
+ self.exposure_buildings_model.asset_locations = source
+
+ def setup_extraction_method(self, extraction_method):
+ if self.exposure:
+ self.exposure.setup_extraction_method(extraction_method)
+
+ def set_ground_floor_height(
+ self,
+ source: str,
+ attribute_name: Union[str, List[str], None] = None,
+ method: Union[str, List[str], None] = "nearest",
+ max_dist: Union[float, int, None] = 10,
+ ):
+ self.exposure_ground_floor_height_model = ExposureSetupGroundFloorHeight(
+ source=source,
+ attribute_name=attribute_name,
+ method=method,
+ max_dist=max_dist,
+ )
+
+ def set_damages(
+ self,
+ source: str,
+ attribute_name: Union[str, List[str], None] = None,
+ method: Union[str, List[str], None] = "nearest",
+ max_dist: Union[float, int, None] = 10,
+ ):
+ self.exposure_damages_model = ExposureSetupDamages(
+ source=source,
+ attribute_name=attribute_name,
+ method=method,
+ max_dist=max_dist,
+ )
+
+ def set_ground_elevation(self, source: Union[int, float, None, str]):
+ self.exposure_ground_elevation_model = ExposureSetupGroundElevation(
+ source=source
+ )
+
+ def set_roads_settings(
+ self,
+ road_types: List[str] = [
+ "motorway",
+ "motorway_link",
+ "trunk",
+ "trunk_link",
+ "primary",
+ "primary_link",
+ "secondary",
+ "secondary_link",
+ ],
+ ):
+ self.exposure_roads_model = ExposureRoadsSettings(
+ roads_fn="OSM",
+ road_types=road_types,
+ road_damage=1,
+ unit=Units.ft.value,
+ )
+
+
+ def get_osm_roads(
+ self,
+ road_types: List[str] = [
+ "motorway",
+ "motorway_link",
+ "trunk",
+ "trunk_link",
+ "primary",
+ "primary_link",
+ "secondary",
+ "secondary_link",
+ ],
+ crs=4326,
+ ):
+ if self.exposure is None:
+ region = self.data_catalog.get_geodataframe("area_of_interest")
+ self.exposure = ExposureVector(
+ data_catalog=self.data_catalog,
+ logger=self.logger,
+ region=region,
+ crs=crs,
+ )
+
+ self.exposure.setup_roads(
+ source="OSM",
+ road_damage=1,
+ road_types=road_types,
+ )
+ roads = self.exposure.exposure_db.loc[
+ self.exposure.exposure_db["Primary Object Type"] == "roads"
+ ]
+ gdf = self.exposure.get_full_gdf(roads)
+
+ self.exposure_roads_model = ExposureRoadsSettings(
+ roads_fn="OSM",
+ road_types=road_types,
+ road_damage=1,
+ unit=Units.ft.value,
+ )
+
+ return gdf
+
+ def set_aggregation_areas_config(self, files, attribute_names, label_names):
+ self.aggregation_areas_model = AggregationAreaSettings(
+ aggregation_area_fn=files,
+ attribute_names=attribute_names,
+ label_names=label_names,
+ )
diff --git a/hydromt_fiat/api/hazard_vm.py b/hydromt_fiat/api/hazard_vm.py
deleted file mode 100644
index fb7fe11b..00000000
--- a/hydromt_fiat/api/hazard_vm.py
+++ /dev/null
@@ -1,6 +0,0 @@
-from hydromt_fiat.api.data_types import HazardIni
-
-
-class HazardViewModel:
- def __init__(self):
- self.hazard_model = HazardIni(hazard_map_fn="", hazard_type="")
diff --git a/hydromt_fiat/api/hydromt_fiat_vm.py b/hydromt_fiat/api/hydromt_fiat_vm.py
index 3c5f3072..9a86f07a 100644
--- a/hydromt_fiat/api/hydromt_fiat_vm.py
+++ b/hydromt_fiat/api/hydromt_fiat_vm.py
@@ -1,30 +1,22 @@
from typing import Any, Union, List
+import geopandas as gpd
import tomli_w
from hydromt import DataCatalog
from pathlib import Path
-from hydromt_fiat.api.data_types import ConfigIni
+from hydromt_fiat.api.data_types import ConfigYaml
from hydromt_fiat.api.dbs_controller import LocalDatabase
from hydromt_fiat.api.exposure_vm import ExposureViewModel
-from hydromt_fiat.api.hazard_vm import HazardViewModel
from hydromt_fiat.api.model_vm import ModelViewModel
from hydromt_fiat.api.vulnerability_vm import VulnerabilityViewModel
+from hydromt_fiat.api.svi_vm import SviViewModel
from hydromt_fiat.fiat import FiatModel
from hydromt.log import setuplog
-class Singleton(object):
- _instance = None
- def __new__(cls, *args: Any, **kwargs: Any):
- if not isinstance(cls._instance, cls):
- cls._instance = object.__new__(cls)
- return cls._instance
-
-
-class HydroMtViewModel(Singleton):
- is_initialized: bool = False
+class HydroMtViewModel:
data_catalog: DataCatalog
database: LocalDatabase
@@ -32,33 +24,31 @@ def __init__(
self,
database_path: str,
catalog_path: Union[List, str],
- hydromt_fiat_path: str = None,
+ hydromt_fiat_path: str,
):
- if not self.__class__.is_initialized:
- database_path = Path(database_path)
-
- HydroMtViewModel.database = LocalDatabase.create_database(database_path)
- HydroMtViewModel.data_catalog = DataCatalog(catalog_path)
-
- if hydromt_fiat_path is not None:
- logger = setuplog("hydromt_fiat", log_level=10)
- self.fiat_model = FiatModel(
- data_libs=catalog_path,
- root=hydromt_fiat_path,
- mode="w+",
- logger=logger,
- )
-
- self.model_vm = ModelViewModel()
- self.exposure_vm = ExposureViewModel(
- HydroMtViewModel.database, HydroMtViewModel.data_catalog, logger
- )
- self.vulnerability_vm = VulnerabilityViewModel(
- HydroMtViewModel.database, HydroMtViewModel.data_catalog, logger
- )
- self.hazard_vm = HazardViewModel()
-
- self.__class__.is_initialized = True
+ database_path = Path(database_path)
+
+ HydroMtViewModel.database = LocalDatabase.create_database(database_path)
+ HydroMtViewModel.data_catalog = DataCatalog(catalog_path)
+
+ logger = setuplog("hydromt_fiat", log_level=10)
+ self.fiat_model = FiatModel(
+ data_libs=catalog_path,
+ root=hydromt_fiat_path,
+ mode="w+",
+ logger=logger,
+ )
+
+ self.model_vm = ModelViewModel()
+ self.exposure_vm = ExposureViewModel(
+ HydroMtViewModel.database, HydroMtViewModel.data_catalog, logger
+ )
+ self.vulnerability_vm = VulnerabilityViewModel(
+ HydroMtViewModel.database, HydroMtViewModel.data_catalog, logger
+ )
+ self.svi_vm = SviViewModel(
+ HydroMtViewModel.database, HydroMtViewModel.data_catalog, logger
+ )
def clear_database(self):
# TODO: delete database after hydromt_fiat has run
@@ -68,20 +58,67 @@ def save_data_catalog(self):
database_path = self.__class__.database.drive
self.__class__.data_catalog.to_yml(database_path / "data_catalog.yml")
- def build_config_ini(self):
- config_ini = ConfigIni(
- setup_config=self.model_vm.config_model,
- setup_hazard=self.hazard_vm.hazard_model,
- setup_vulnerability=self.vulnerability_vm.vulnerability_model,
- setup_exposure_vector=self.exposure_vm.exposure_model,
+ def build_config_yaml(self):
+ config_yaml = ConfigYaml(
+ setup_global_settings=self.model_vm.global_settings_model,
+ setup_output=self.model_vm.output_model,
)
-
+
+ if self.vulnerability_vm.vulnerability_buildings_model:
+ config_yaml.setup_vulnerability = self.vulnerability_vm.vulnerability_buildings_model
+
+ if self.exposure_vm.exposure_buildings_model:
+ config_yaml.setup_exposure_buildings = self.exposure_vm.exposure_buildings_model
+
+ if self.exposure_vm.aggregation_areas_model:
+ config_yaml.setup_aggregation_areas = self.exposure_vm.aggregation_areas_model
+
+ if self.exposure_vm.exposure_roads_model:
+ config_yaml.setup_exposure_roads = self.exposure_vm.exposure_roads_model
+
+ if self.exposure_vm.exposure_damages_model:
+ config_yaml.update_max_potential_damage = self.exposure_vm.exposure_damages_model
+
+ if self.exposure_vm.exposure_ground_floor_height_model:
+ config_yaml.update_ground_floor_height = self.exposure_vm.exposure_ground_floor_height_model
+
+ if self.vulnerability_vm.vulnerability_roads_model:
+ config_yaml.setup_road_vulnerability = self.vulnerability_vm.vulnerability_roads_model
+
+ if self.svi_vm.svi_model:
+ config_yaml.setup_social_vulnerability_index = self.svi_vm.svi_model
+
+ if self.svi_vm.equity_model:
+ config_yaml.setup_equity_data = self.svi_vm.equity_model
+
database_path = self.__class__.database.drive
- with open(database_path / "config.ini", "wb") as f:
- tomli_w.dump(config_ini.dict(exclude_none=True), f)
+ with open(database_path / "config.yaml", "wb") as f:
+ tomli_w.dump(config_yaml.dict(exclude_none=True), f)
+
+ return config_yaml
+ def read(self):
+ self.fiat_model.read()
+
def run_hydromt_fiat(self):
+ self.save_data_catalog()
+ config_yaml = self.build_config_yaml()
region = self.data_catalog.get_geodataframe("area_of_interest")
- self.fiat_model.build(region={"geom": region}, opt=ConfigIni.dict())
- self.fiat_model.write()
+ self.fiat_model.build(region={"geom": region}, opt=config_yaml.dict())
+
+ exposure_db = self.fiat_model.exposure.exposure_db
+ if "setup_exposure_buildings" in config_yaml.dict() and "setup_exposure_roads" not in config_yaml.dict():
+ # Only buildings are set up
+ buildings_gdf = self.fiat_model.exposure.get_full_gdf(exposure_db)
+ return buildings_gdf, None
+ elif "setup_exposure_buildings" in config_yaml.dict() and "setup_exposure_roads" in config_yaml.dict():
+ # Buildings and roads are set up
+ full_gdf = self.fiat_model.exposure.get_full_gdf(exposure_db)
+ buildings_gdf = full_gdf.loc[full_gdf["Primary Object Type"] != "roads"]
+ roads_gdf = full_gdf.loc[full_gdf["Primary Object Type"] != "roads"]
+ return buildings_gdf, roads_gdf
+ elif "setup_exposure_buildings" not in config_yaml.dict() and "setup_exposure_roads" in config_yaml.dict():
+ # Only roads are set up
+ roads_gdf = self.fiat_model.exposure.get_full_gdf(exposure_db)
+ return None, roads_gdf
diff --git a/hydromt_fiat/api/model_vm.py b/hydromt_fiat/api/model_vm.py
index 3f0c6d79..046d35f8 100644
--- a/hydromt_fiat/api/model_vm.py
+++ b/hydromt_fiat/api/model_vm.py
@@ -1,8 +1,11 @@
-from hydromt_fiat.api.data_types import ModelIni
+from hydromt_fiat.api.data_types import OutputSettings, GlobalSettings
class ModelViewModel:
def __init__(self):
- self.config_model = ModelIni(
- site_name="", scenario_name="", output_dir="", crs=""
+ self.global_settings_model = GlobalSettings(crs=4326)
+ self.output_model = OutputSettings(
+ output_dir="output",
+ output_csv_name="output.csv",
+ output_vector_name="spatial.gpkg",
)
diff --git a/hydromt_fiat/api/svi_vm.py b/hydromt_fiat/api/svi_vm.py
new file mode 100644
index 00000000..e03df57e
--- /dev/null
+++ b/hydromt_fiat/api/svi_vm.py
@@ -0,0 +1,40 @@
+from hydromt import DataCatalog
+
+from hydromt_fiat.interface.database import IDatabase
+from hydromt_fiat.workflows.social_vulnerability_index import list_of_states
+import logging
+
+from .data_types import (
+ SocialVulnerabilityIndexSettings,
+ EquityDataSettings,
+)
+
+
+class SviViewModel:
+ def __init__(
+ self, database: IDatabase, data_catalog: DataCatalog, logger: logging.Logger
+ ):
+ self.svi_model = None
+ self.equity_model = None
+
+ self.database: IDatabase = database
+ self.data_catalog: DataCatalog = data_catalog
+ self.logger: logging.Logger = logger
+
+ @staticmethod
+ def get_state_names():
+ dict_states = list_of_states()
+ return list(dict_states.keys())
+
+ def set_svi_settings(self, census_key, year_data):
+ self.svi_model = SocialVulnerabilityIndexSettings(
+ census_key = census_key,
+ codebook_fn = "social_vulnerability",
+ year_data = year_data,
+ )
+
+ def set_equity_settings(self, census_key, year_data):
+ self.equity_model = EquityDataSettings(
+ census_key = census_key,
+ year_data = year_data,
+ )
diff --git a/hydromt_fiat/api/vulnerability_vm.py b/hydromt_fiat/api/vulnerability_vm.py
index c46c6d27..4ac99a22 100644
--- a/hydromt_fiat/api/vulnerability_vm.py
+++ b/hydromt_fiat/api/vulnerability_vm.py
@@ -1,51 +1,67 @@
-from hydromt_fiat.api.data_types import VulnerabilityIni
+from hydromt_fiat.api.data_types import VulnerabilitySettings, RoadVulnerabilitySettings
from hydromt_fiat.interface.database import IDatabase
from hydromt import DataCatalog
import logging
import pandas as pd
from .data_types import Units
+from typing import Union
class VulnerabilityViewModel:
def __init__(
self, database: IDatabase, data_catalog: DataCatalog, logger: logging.Logger
):
- self.vulnerability_model = VulnerabilityIni(
- vulnerability_fn="", link_table="", units=Units.m.value
- )
+ self.vulnerability_buildings_model = None
+ self.vulnerability_roads_model = None
+
self.database: IDatabase = database
self.data_catalog: DataCatalog = data_catalog
self.logger: logging.Logger = logger
- self.linking_standard_colnames = ["Name", "Link"]
- self.linking_colnames = list()
- self.linking_list = list()
-
- def get_hazus_curves(self):
- self.logger.info("Getting Hazus curves")
- df_vulnerability = self.data_catalog.get_dataframe("hazus_vulnerability_curves")
- self.linking_colnames = ["BldgDmgFnID", "Occupancy", "Source", "Description"]
- list_vulnerability_curve_ids = (
- df_vulnerability[self.linking_colnames]
- .apply(lambda row: "; ".join(row.values.astype(str)), axis=1)
- .to_list()
+ self.linking_standard_colnames = [
+ "FIAT Damage Function Name",
+ "Exposure Link",
+ "Damage Type",
+ "ID",
+ "Occupancy",
+ "Description",
+ "Source",
+ ]
+ self.linking_table = pd.DataFrame()
+
+ def get_damage_curves_linking_file(self):
+ if self.linking_table.empty:
+ return self.data_catalog.get_dataframe("default_hazus_iwr_linking")
+
+ return self.linking_table
+
+ def add_vulnerability_curves_to_model(self, vulnerability_fn, link_table):
+ self.vulnerability_buildings_model = VulnerabilitySettings(
+ vulnerability_fn=vulnerability_fn,
+ vulnerability_identifiers_and_linking_fn=link_table,
+ unit=Units.ft.value, # TODO: make flexible
+ functions_mean="default", # TODO: make flexible
+ )
+ if not self.linking_table.empty:
+ self.save_linking_table()
+
+ def update_linking_table(self, df: pd.DataFrame) -> None:
+ self.linking_table = df
+
+ def save_linking_table(self) -> None:
+ self.vulnerability_buildings_model.vulnerability_identifiers_and_linking_fn = (
+ str(self.database.drive / "linking_table.csv")
)
- self.vulnerability_model.vulnerability_fn = "hazus_vulnerability_curves"
- return list_vulnerability_curve_ids
-
- def set_linking_table(self, name, link):
- self.logger.info("Setting linking table")
- linking_info = link.split("; ")
- linking_info = [name, name] + linking_info
- self.linking_list.append(linking_info)
- return " ".join(linking_info)
-
- def save_linking_table(self):
- self.linking_standard_colnames.extend(self.linking_colnames)
- df = pd.DataFrame(
- columns=self.linking_standard_colnames, data=self.linking_list
+ self.linking_table.to_csv(
+ self.vulnerability_buildings_model.vulnerability_identifiers_and_linking_fn,
+ index=False,
)
- self.vulnerability_model.link_table = str(
- self.database.drive / "linking_table.csv"
+
+ def set_road_damage_threshold(self, threshold: Union[int, float]):
+ self.vulnerability_roads_model = RoadVulnerabilitySettings(
+ threshold_value=threshold,
+ min_hazard_value=0,
+ max_hazard_value=16,
+ step_hazard_value=1.0,
+ vertical_unit=Units.ft.value,
)
- df.to_csv(self.vulnerability_model.link_table, index=False)
diff --git a/hydromt_fiat/data/attribute_linking/NSI_attributes_to_FIAT.json b/hydromt_fiat/data/attribute_linking/NSI_attributes_to_FIAT.json
index 0d238b7e..411b0e40 100644
--- a/hydromt_fiat/data/attribute_linking/NSI_attributes_to_FIAT.json
+++ b/hydromt_fiat/data/attribute_linking/NSI_attributes_to_FIAT.json
@@ -5,8 +5,8 @@
"Secondary Object Type": "occtype",
"Max Potential Damage: Structure": "val_struct",
"Max Potential Damage: Content": "val_cont",
+ "Ground Floor Height": "found_ht",
"Ground Elevation": "ground_elv",
- "X Coordinate": "x",
- "Y Coordinate": "y",
- "Aggregation Label: Census Block": "cbfips"
+ "geometry": "geometry",
+ "Aggregation Label: Census Blockgroup": "cbfips"
}
\ No newline at end of file
diff --git a/hydromt_fiat/data/hydromt_fiat_catalog_USA.yml b/hydromt_fiat/data/hydromt_fiat_catalog_USA.yml
index 88b984b5..ba698394 100644
--- a/hydromt_fiat/data/hydromt_fiat_catalog_USA.yml
+++ b/hydromt_fiat/data/hydromt_fiat_catalog_USA.yml
@@ -1,41 +1,71 @@
-meta:
- root: C:\Repositories\hydromt_fiat\hydromt_fiat\data
-
NSI:
- path: "https://nsi.sec.usace.army.mil/nsiapi/structures?fmt=fc"
- data_type: GeoDataFrame
- driver: vector
- crs: 4326
- translation_fn: "attribute_linking/NSI_attributes_to_FIAT.json"
- meta:
- category: exposure
-
-hazus_vulnerability_curves:
- path: damage_functions/flooding/AllDDF_HAZUS.xlsx
- data_type: DataFrame
- driver: xlsx
- meta:
- category: vulnerability
+ path: "https://nsi.sec.usace.army.mil/nsiapi/structures?fmt=fc"
+ data_type: GeoDataFrame
+ driver: vector
+ crs: 4326
+ translation_fn: "attribute_linking/NSI_attributes_to_FIAT.json"
+ meta:
+ category: exposure
+
+default_vulnerability_curves:
+ path: damage_functions/flooding/Hazus_IWR_curves.csv
+ data_type: DataFrame
+ driver: csv
+ meta:
+ unit: ft
+ category: vulnerability
+ source: HAZUS SQL database, USACE-IWR and FEMA expert elicitation curves. The source of these curves is the Draft Report Nonresidential Flood Depth-Damage Functions Derived from Expert Elicitation, April 2009, Revised 2013. FEMA Contract Number HSFEHQ-06-D-0162. Task Order HSFEHQ-08-J-0014. Not for distribution, but data shared by USACE.
jrc_vulnerability_curves:
- path: damage_functions/flooding/JRC_damage_functions.xlsx
- data_type: DataFrame
- driver: xlsx
- meta:
- category: vulnerability
+ path: damage_functions/flooding/JRC_damage_functions.xlsx
+ data_type: DataFrame
+ driver: xlsx
+ meta:
+ unit: m
+ category: vulnerability
hazus_max_potential_damages:
- path: max_potential_damages/damage_values_fema_hazus-inventory-technical-manual-4.2.3.xlsx
- data_type: DataFrame
- driver: xlsx
- meta:
- category: vulnerability
+ path: max_potential_damages/damage_values_fema_hazus-inventory-technical-manual-4.2.3.xlsx
+ data_type: DataFrame
+ driver: xlsx
+ meta:
+ unit: ft
+ category: vulnerability
social_vulnerability:
- path: social_vulnerability/census_vulnerability_data_codebook.xlsx
- data_type: DataFrame
- driver: xlsx
- meta:
- category: social_vulnerability
+ path: social_vulnerability/census_vulnerability_data_codebook.xlsx
+ data_type: DataFrame
+ driver: xlsx
+ meta:
+ category: social_vulnerability
+
+us_states_counties:
+ path: social_vulnerability/us_states_counties.csv
+ data_type: DataFrame
+ driver: csv
+ meta:
+ category: social_vulnerability
+
+default_hazus_iwr_linking:
+ path: vulnerability_linking/default_hazus_iwr_curve_linking.csv
+ data_type: DataFrame
+ driver: csv
+ meta:
+ category: vulnerability
+default_road_max_potential_damages:
+ path: max_potential_damages/us_road_damage.csv
+ data_type: DataFrame
+ driver: csv
+ meta:
+ unit: ft
+ category: exposure
+ source: Bouwer, Laurens & Haasnoot, Marjolijn & Wagenaar, Dennis & Roscoe, Kathryn. (2018). Assessment of alternative flood mitigation strategies for the C-7 Basin in Miami, Florida.
+
+hazus_iwr_occupancy_classes:
+ path: vulnerability_linking/hazus_iwr_occupancy_classes.csv
+ data_type: DataFrame
+ driver: csv
+ meta:
+ category: vulnerability
\ No newline at end of file
diff --git a/hydromt_fiat/data/hydromt_fiat_catalog_global.yml b/hydromt_fiat/data/hydromt_fiat_catalog_global.yml
index 7563f533..982f52df 100644
--- a/hydromt_fiat/data/hydromt_fiat_catalog_global.yml
+++ b/hydromt_fiat/data/hydromt_fiat_catalog_global.yml
@@ -1,7 +1,4 @@
-meta:
- root: C:\Repositories\hydromt_fiat\hydromt_fiat\data
-
jrc_vulnerability_curves:
path: damage_functions/flooding/JRC_damage_functions.csv
data_type: DataFrame
@@ -14,7 +11,7 @@ jrc_vulnerability_curves:
paper_doi: https://dx.doi.org/10.2760/16510
jrc_vulnerability_curves_linking:
- path: damage_functions/flooding/JRC_damage_functions_linking.csv
+ path: vulnerability_linking/JRC_damage_functions_linking.csv
data_type: DataFrame
driver: csv
meta:
diff --git a/hydromt_fiat/data/max_potential_damages/us_road_damage.csv b/hydromt_fiat/data/max_potential_damages/us_road_damage.csv
new file mode 100644
index 00000000..54ceaf71
--- /dev/null
+++ b/hydromt_fiat/data/max_potential_damages/us_road_damage.csv
@@ -0,0 +1,7 @@
+lanes,cost [USD/ft]
+1,240
+2,240
+3,360
+4,480
+5,600
+6,720
diff --git a/hydromt_fiat/data/new_composite_areas/new_development_area_test.gpkg b/hydromt_fiat/data/new_composite_areas/new_development_area_test.gpkg
new file mode 100644
index 00000000..dc9519cb
Binary files /dev/null and b/hydromt_fiat/data/new_composite_areas/new_development_area_test.gpkg differ
diff --git a/hydromt_fiat/data/new_composite_areas/reference_groundHeight_test.cpg b/hydromt_fiat/data/new_composite_areas/reference_groundHeight_test.cpg
new file mode 100644
index 00000000..3ad133c0
--- /dev/null
+++ b/hydromt_fiat/data/new_composite_areas/reference_groundHeight_test.cpg
@@ -0,0 +1 @@
+UTF-8
\ No newline at end of file
diff --git a/hydromt_fiat/data/new_composite_areas/reference_groundHeight_test.dbf b/hydromt_fiat/data/new_composite_areas/reference_groundHeight_test.dbf
new file mode 100644
index 00000000..ce5d81d9
Binary files /dev/null and b/hydromt_fiat/data/new_composite_areas/reference_groundHeight_test.dbf differ
diff --git a/hydromt_fiat/data/new_composite_areas/reference_groundHeight_test.prj b/hydromt_fiat/data/new_composite_areas/reference_groundHeight_test.prj
new file mode 100644
index 00000000..f45cbadf
--- /dev/null
+++ b/hydromt_fiat/data/new_composite_areas/reference_groundHeight_test.prj
@@ -0,0 +1 @@
+GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]]
\ No newline at end of file
diff --git a/hydromt_fiat/data/new_composite_areas/reference_groundHeight_test.qix b/hydromt_fiat/data/new_composite_areas/reference_groundHeight_test.qix
new file mode 100644
index 00000000..54c17c12
Binary files /dev/null and b/hydromt_fiat/data/new_composite_areas/reference_groundHeight_test.qix differ
diff --git a/hydromt_fiat/data/new_composite_areas/reference_groundHeight_test.shp b/hydromt_fiat/data/new_composite_areas/reference_groundHeight_test.shp
new file mode 100644
index 00000000..89f3e873
Binary files /dev/null and b/hydromt_fiat/data/new_composite_areas/reference_groundHeight_test.shp differ
diff --git a/hydromt_fiat/data/new_composite_areas/reference_groundHeight_test.shx b/hydromt_fiat/data/new_composite_areas/reference_groundHeight_test.shx
new file mode 100644
index 00000000..33153795
Binary files /dev/null and b/hydromt_fiat/data/new_composite_areas/reference_groundHeight_test.shx differ
diff --git a/hydromt_fiat/data/social_vulnerability/test_blockgroup_shp/tl_2022_45_bg.dbf b/hydromt_fiat/data/social_vulnerability/test_blockgroup_shp/tl_2022_45_bg.dbf
deleted file mode 100644
index a03d23d7..00000000
Binary files a/hydromt_fiat/data/social_vulnerability/test_blockgroup_shp/tl_2022_45_bg.dbf and /dev/null differ
diff --git a/hydromt_fiat/data/social_vulnerability/test_blockgroup_shp/tl_2022_45_bg.prj b/hydromt_fiat/data/social_vulnerability/test_blockgroup_shp/tl_2022_45_bg.prj
deleted file mode 100644
index 747df588..00000000
--- a/hydromt_fiat/data/social_vulnerability/test_blockgroup_shp/tl_2022_45_bg.prj
+++ /dev/null
@@ -1 +0,0 @@
-GEOGCS["GCS_North_American_1983",DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137,298.257222101]],PRIMEM["Greenwich",0],UNIT["Degree",0.017453292519943295]]
\ No newline at end of file
diff --git a/hydromt_fiat/data/social_vulnerability/test_blockgroup_shp/tl_2022_45_bg.shp b/hydromt_fiat/data/social_vulnerability/test_blockgroup_shp/tl_2022_45_bg.shp
deleted file mode 100644
index 32820bf3..00000000
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diff --git a/hydromt_fiat/data/social_vulnerability/test_blockgroup_shp/tl_2022_45_bg.shp.ea.iso.xml b/hydromt_fiat/data/social_vulnerability/test_blockgroup_shp/tl_2022_45_bg.shp.ea.iso.xml
deleted file mode 100644
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+++ /dev/null
@@ -1,406 +0,0 @@
-
-
-
- Feature Catalog for the Current Block Groups TIGER/Line Shapefiles
-
-
- Block Groups
-
-
- 2022
-
-
- 2022-09-29
-
-
- eng; USA
-
-
- utf8
-
-
-
-
- U.S. Department of Commerce, U.S. Census Bureau, Geography Division, Spatial Data Collection and Products Branch
-
-
-
-
-
-
- 301-763-1128
-
-
- 301-763-4710
-
-
-
-
-
-
- 4600 Silver Hill Road, Stop 7400
-
-
- Washington
-
-
- DC
-
-
- 20233-7400
-
-
- United States
-
-
- geo.geography@census.gov
-
-
-
-
-
-
- pointOfContact
-
-
-
-
-
-
- BG.shp
-
-
- Current Block Group State-based
-
-
- false
-
-
-
-
-
- STATEFP
-
-
- Current state Federal Information Processing Series (FIPS) code
-
-
-
-
-
-
- National Standard Codes (ANSI INCITS 38-2009), Federal Information Processing Series (FIPS) - States/State Equivalents
-
-
-
-
-
-
-
-
-
- COUNTYFP
-
-
- Current county Federal Information Processing Series (FIPS) code
-
-
-
-
-
-
- National Standard Codes (ANSI INCITS 31-2009), Federal Information Processing Series (FIPS) - Counties/County Equivalents
-
-
-
-
-
-
-
-
-
- TRACTCE
-
-
- Current census tract code
-
-
-
-
-
-
- 000100 to 998999
-
-
- Census tract number
-
-
-
-
-
-
-
- 990000 to 990099
-
-
- Water tract (2010 and continuing)
-
-
-
-
-
-
-
- 990100 to 998999
-
-
- Census tract number
-
-
-
-
-
-
-
-
-
- BLKGRPCE
-
-
- Current block group number. A block group number of 0 represents a water block group.
-
-
-
-
-
-
- 0
-
-
- 9
-
-
-
-
-
-
-
-
-
-
-
- GEOID
-
-
- Census block group identifier; a concatenation of the current state Federal Information Processing Series (FIPS) code, county FIPS code, census tract code, and block group number
-
-
-
-
-
-
-
- The GEOID attribute is a concatenation of the state FIPS code, followed by the county FIPS code, followed by the census tract code, followed by the block group number. No spaces are allowed between the two codes. The State FIPS code is taken from "National Standard Codes (ANSI INCITS 38-2009), Federal Information Processing Series (FIPS) - States". The county FIPS code is taken from "National Standard Codes (ANSI INCITS 31-2009), Federal Information Processing Series (FIPS) - Counties/County Equivalents". The census tract code is taken from the "TRACTCE" attribute. The block group number is taken from the "BLKGRPCE" attribute.
-
-
-
-
-
-
-
-
- NAMELSAD
-
-
- Current translated legal/statistical area description and the block group number
-
-
-
-
-
-
-
- Refer to the block group number that appears in BLKGRPCE and translated legal/statistical area description code BG, Block Group
-
-
-
-
-
-
-
-
- MTFCC
-
-
- MAF/TIGER feature class code
-
-
-
-
-
-
- G5030
-
-
- Block Group
-
-
-
-
-
-
-
-
-
- FUNCSTAT
-
-
- Current functional status
-
-
-
-
-
-
- S
-
-
- Statistical entity
-
-
-
-
-
-
-
-
-
- ALAND
-
-
- Current land area (square meters)
-
-
-
-
-
-
- 0
-
-
- 9,999,999,999,999
-
-
-
-
-
-
-
-
-
-
-
- AWATER
-
-
- Current water area (square meters)
-
-
-
-
-
-
- 0
-
-
- 9,999,999,999,999
-
-
-
-
-
-
-
-
-
-
-
- INTPTLAT
-
-
- Current latitude of the internal point
-
-
-
-
-
-
- -90
-
-
- 90
-
-
-
-
-
-
-
-
-
-
-
- INTPTLON
-
-
- Current longitude of the internal point
-
-
-
-
-
-
- -180
-
-
- 180
-
-
-
-
-
-
-
-
-
-
-
\ No newline at end of file
diff --git a/hydromt_fiat/data/social_vulnerability/test_blockgroup_shp/tl_2022_45_bg.shp.iso.xml b/hydromt_fiat/data/social_vulnerability/test_blockgroup_shp/tl_2022_45_bg.shp.iso.xml
deleted file mode 100644
index f30c2eb7..00000000
--- a/hydromt_fiat/data/social_vulnerability/test_blockgroup_shp/tl_2022_45_bg.shp.iso.xml
+++ /dev/null
@@ -1,852 +0,0 @@
-
-
-
- tl_2022_45_bg.shp.iso.xml
-
-
- eng; USA
-
-
- utf8
-
-
- series_tl_2022_bg.shp.iso.xml
-
-
- dataset
-
-
-
-
- U.S. Department of Commerce, U.S. Census Bureau, Geography Division, Spatial Data Collection and Products Branch
-
-
-
-
-
-
- 301-763-1128
-
-
- 301-763-4710
-
-
-
-
-
-
- 4600 Silver Hill Road, Stop 7400
-
-
- Washington
-
-
- DC
-
-
- 20233-7400
-
-
- United States
-
-
- geo.geography@census.gov
-
-
-
-
-
-
- pointOfContact
-
-
-
-
- 2022-09-29
-
-
- ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data
-
-
- ISO 19115-2:2009(E)
-
-
- https://meta.geo.census.gov/data/existing/decennial/GEO/GPMB/TIGERline/Current_19115/tl_2022_45_bg.shp.iso.xml
-
-
-
-
-
-
- complex
-
-
- 3408
-
-
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-
-
-
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-
-
-
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-
- North American Datum of 1983
-
-
- NAD83
-
-
-
-
- 2007-01-19
-
-
- revision
-
-
-
-
-
-
-
-
-
-
-
- https://spatialreference.org/ref/epsg/4269/gml/
-
-
- NAD83
-
-
- Link to Geographic Markup Language (GML) description of reference system.
-
-
- information
-
-
-
-
-
-
- resourceProvider
-
-
-
-
-
-
- urn:ogc:def:crs:EPSG::4269
-
-
-
-
-
-
-
-
-
-
- TIGER/Line Shapefile, Current, State, South Carolina, Block Group
-
-
- TIGER/Line Shapefile, 2022, State, South Carolina, SC, Block Group
-
-
-
-
- 2022
-
-
- publication
-
-
-
-
-
-
- 2022-08
-
-
- creation
-
-
-
-
-
-
- 2022-08
-
-
- lastUpdate
-
-
-
-
-
-
- tl_2022_45_bg
-
-
-
-
-
-
- U.S. Department of Commerce, U.S. Census Bureau, Geography Division, Spatial Data Collection and Products Branch
-
-
-
-
-
-
- 301-763-1128
-
-
- 301-763-4710
-
-
-
-
-
-
- 4600 Silver Hill Road, Stop 7400
-
-
- Washington
-
-
- DC
-
-
- 20233-7400
-
-
- United States
-
-
- geo.geography@census.gov
-
-
-
-
-
-
- pointOfContact
-
-
-
-
- mapDigital
-
-
-
-
- This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation.
-Block Groups (BGs) are clusters of blocks within the same census tract. Each census tract contains at least one BG, and BGs are uniquely numbered within census tracts. BGs have a valid code range of 0 through 9. BGs have the same first digit of their 4-digit census block number from the same decennial census. For example, tabulation blocks numbered 3001, 3002, 3003,.., 3999 within census tract 1210.02 are also within BG 3 within that census tract. BGs coded 0 are intended to only include water area, no land area, and they are generally in territorial seas, coastal water, and Great Lakes water areas. Block groups generally contain between 600 and 3,000 people. A BG usually covers a contiguous area but never crosses county or census tract boundaries. They may, however, cross the boundaries of other geographic entities like county subdivisions, places, urban areas, voting districts, congressional districts, and American Indian / Alaska Native / Native Hawaiian areas.
-The BG boundaries in this release are those that were delineated as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2020 Census.
-
-
- The Census Bureau releases extracts of the MAF/TIGER database in the form of TIGER/Line Shapefiles for others to use in geographic information systems (GIS) or for other geographic applications.
-
-
- completed
-
-
-
-
- U.S. Department of Commerce, U.S. Census Bureau, Geography Division, Spatial Data Collection and Products Branch
-
-
-
-
-
-
- 301-763-1128
-
-
- 301-763-4710
-
-
-
-
-
-
- 4600 Silver Hill Road, Stop 7400
-
-
- Washington
-
-
- DC
-
-
- 20233-7400
-
-
- United States
-
-
- geo.geography@census.gov
-
-
-
-
-
-
- pointOfContact
-
-
-
-
-
-
- notPlanned
-
-
-
-
-
-
- https://tigerweb.geo.census.gov/arcgis/services/TIGERweb/tigerWMS_Current/MapServer/WmsServer?REQUEST=GetMap&SERVICE=WMS&VERSION=1.3.0&LAYERS=Census_Block_Groups59805,Census_Block_Groups_Labels32376&STYLES=default,default&FORMAT=image/png+xml&BGCOLOR=0xFFFFFF&TRANSPARENT=TRUE&CRS=EPSG:4326&BBOX=+38.8810,-77.0401,+38.9008,-77.0035&WIDTH=256&HEIGHT=256
-
-
- URL for the TIGERWeb Mapping Service
-
-
- URL
-
-
-
-
-
-
- State or equivalent entity
-
-
- Polygon
-
-
- Block Group
-
-
- BG
-
-
-
-
-
-
-
- None
-
-
-
-
-
-
-
-
-
- State or Equivalent Entity
-
-
- South Carolina
-
-
- SC
-
-
- 45
-
-
-
-
-
-
-
- INCITS 38-2009[R2019]: Information Technology - Codes for the Identification of the States and Equivalent Areas within the United States, Puerto Rico, and the Insular Areas.
-
-
-
-
-
-
-
-
-
- otherRestrictions
-
-
- otherRestrictions
-
-
- Access constraints: None
-
-
- Use Constraints: The TIGER/Line Shapefile products are not copyrighted however TIGER/Line and Census TIGER are registered trademarks of the U.S. Census Bureau. These products are free to use in a product or publication, however acknowledgement must be given to the U.S. Census Bureau as the source.
-The boundary information in the TIGER/Line Shapefiles are for statistical data collection and tabulation purposes only; their depiction and designation for statistical purposes does not constitute a determination of jurisdictional authority or rights of ownership or entitlement and they are not legal land descriptions. Coordinates in the TIGER/Line shapefiles have six implied decimal places, but the positional accuracy of these coordinates is not as great as the six decimal places suggest.
-
-
-
-
-
-
-
-
- Series Information for Block Group State-based TIGER/Line Shapefiles, Current
-
-
-
-
- 2022
-
-
- publication
-
-
-
-
-
-
- series_tl_2022_bg
-
-
-
-
-
-
- U.S. Department of Commerce, U.S. Census Bureau, Geography Division, Spatial Data Collection and Products Branch
-
-
-
-
-
-
- 301-763-1128
-
-
- 301-763-4710
-
-
-
-
-
-
- 4600 Silver Hill Road, Stop 7400
-
-
- Washington
-
-
- DC
-
-
- 20233-7400
-
-
- United States
-
-
- geo.geography@census.gov
-
-
-
-
-
-
- pointOfContact
-
-
-
-
- mapDigital
-
-
-
-
- largerWorkCitation
-
-
-
-
- vector
-
-
- eng; USA
-
-
- utf8
-
-
- boundaries
-
-
- The TIGER/Line shapefiles contain geographic data only and do not include display mapping software or statistical data. For information on how to use the TIGER/Line shapefile data with specific software package users shall contact the company that produced the software.
-
-
-
-
-
-
- -83.353928
-
-
- -78.499301
-
-
- 31.995954
-
-
- 35.215485
-
-
-
-
-
-
-
- publication date
- 2021-06
- 2022-08
-
-
-
-
-
-
-
-
-
-
-
- 1
-
-
- Block Groups
-
-
-
-
- Feature Catalog for the Current Block Groups TIGER/Line Shapefiles
-
-
-
-
- 2022
-
-
- publication
-
-
-
-
-
-
- U.S. Department of Commerce, U.S. Census Bureau, Geography Division, Spatial Data Collection and Products Branch
-
-
-
-
-
-
- 301-763-1128
-
-
- 301-763-4710
-
-
-
-
-
-
- 4600 Silver Hill Road, Stop 7400
-
-
- Washington
-
-
- DC
-
-
- 20233-7400
-
-
- United States
-
-
- geo.geography@census.gov
-
-
-
-
-
-
- pointOfContact
-
-
-
-
- https://meta.geo.census.gov/data/existing/decennial/GEO/GPMB/TIGERline/Current_19110/tl_2022_bg.shp.ea.iso.xml
-
-
-
-
-
-
-
-
-
-
- ZIP
-
-
-
-
-
-
-
-
-
- U.S. Department of Commerce, U.S. Census Bureau,
- Geography Division, Geographic Customer Services Branch
-
-
-
-
-
-
-
- 301-763-1128
-
-
- 301-763-4710
-
-
-
-
-
-
- 4600 Silver Hill Road, Stop 7400
-
-
- Washington
-
-
- DC
-
-
- 20233-7400
-
-
- United States
-
-
- geo.geography@census.gov
-
-
-
-
-
-
- distributor
-
-
-
-
-
-
- The online copy of the TIGER/Line Shapefiles may be accessed without charge.
-
-
-
-
-
-
-
-
-
-
- https://www2.census.gov/geo/tiger/TIGER2022/BG/tl_2022_45_bg.zip
-
-
- Shapefile Zip File
-
-
- tl_2022_45_bg.zip
-
-
- The TIGER/Line Shapefiles are the fully supported, core geographic product from the U.S. Census Bureau. They are extracts of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File/Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) database.
-
-
- download
-
-
-
-
-
-
-
-
-
-
- https://meta.geo.census.gov/data/existing/decennial/GEO/GPMB/TIGERline/Current_19110/tl_2022_bg.shp.ea.iso.xml
-
-
- XML
-
-
- tl_2022_bg.shp.ea.iso.xml
-
-
- Entity and attribute file
-
-
- download
-
-
-
-
-
-
-
-
-
-
- https://tigerweb.geo.census.gov/arcgis/rest/services/TIGERweb/tigerWMS_Current/MapServer
-
-
- https://www.ogc.org/standards/wms
-
-
- TIGERweb/tigerWMS_Current (MapServer)
-
-
- The Open Geospatial Consortium, Inc. (OGC) Web Map Service interface standard (WMS) provides a simple HTTP interface for requesting geo-registered map images from our geospatial database. The response to the request is one or more geo-registered map images that can be displayed in a browser or WMS client application. By gaining access to our data through our WMS, users can produce maps containing TIGERweb layers combined with layers from other servers.
-
-
- search
-
-
-
-
-
-
-
-
-
-
- https://tigerweb.geo.census.gov/arcgis/rest/services/TIGERweb/Tracts_Blocks/MapServer
-
-
- https://geoservices.github.io
-
-
- TIGERweb/Tracts_Blocks (MapServer)
-
-
- The GeoServices REST Specification provides a way for Web clients to communicate with geographic information system (GIS) servers through Representational State Transfer (REST) technology.
-
-
- search
-
-
-
-
-
-
-
-
-
-
-
-
- dataset
-
-
-
-
-
-
-
- The Census Bureau performed automated tests to ensure logical consistency and limits of shapefiles. Segments making up the outer and inner boundaries of a polygon tie end-to-end to completely enclose the area. All polygons are tested for closure.
- The Census Bureau uses its internally developed geographic update system to enhance and modify spatial and attribute data in the Census MAF/TIGER database. Standard geographic codes, such as FIPS codes for states, counties, municipalities, county subdivisions, places, American Indian/Alaska Native/Native Hawaiian areas, and congressional districts are used when encoding spatial entities. The Census Bureau performed spatial data tests for logical consistency of the codes during the compilation of the original Census MAF/TIGER database files. Most of the codes for geographic entities except states, counties, urban areas, Core Based Statistical Areas (CBSAs), American Indian Areas (AIAs), and congressional districts were provided to the Census Bureau by the USGS, the agency responsible for maintaining the Geographic Names Information System (GNIS). Feature attribute information has been examined but has not been fully tested for consistency.
- For the TIGER/Line Shapefiles, the Point and Vector Object Count for the G-polygon SDTS Point and Vector Object Type reflects the number of records in the shapefile attribute table. For multi-polygon features, only one attribute record exists for each multi-polygon rather than one attribute record per individual G-polygon component of the multi-polygon feature. TIGER/Line Shapefile multi-polygons are an exception to the G-polygon object type classification. Therefore, when multi-polygons exist in a shapefile, the object count will be less than the actual number of G-polygons.
-
-
-
-
-
-
-
-
- Data completeness of the TIGER/Line Shapefiles reflects the contents of the Census MAF/TIGER database at the time the TIGER/Line Shapefiles were created.
-
-
-
-
-
-
-
- Data completeness of the TIGER/Line Shapefiles reflects the contents of the Census MAF/TIGER database at the time the TIGER/Line Shapefiles were created.
-
-
-
-
-
-
-
-
-
- annually
-
-
- This was transformed from the Census Bureau Geospatial Product Metadata Content Standard.
-
-
-
-
\ No newline at end of file
diff --git a/hydromt_fiat/data/social_vulnerability/test_blockgroup_shp/tl_2022_45_bg.shx b/hydromt_fiat/data/social_vulnerability/test_blockgroup_shp/tl_2022_45_bg.shx
deleted file mode 100644
index d07ebcd9..00000000
Binary files a/hydromt_fiat/data/social_vulnerability/test_blockgroup_shp/tl_2022_45_bg.shx and /dev/null differ
diff --git a/hydromt_fiat/data/social_vulnerability/us_states_counties.csv b/hydromt_fiat/data/social_vulnerability/us_states_counties.csv
new file mode 100644
index 00000000..54dac37c
--- /dev/null
+++ b/hydromt_fiat/data/social_vulnerability/us_states_counties.csv
@@ -0,0 +1,3236 @@
+STATE,STATEFP,COUNTYFP,COUNTYNS,COUNTYNAME,CLASSFP,FUNCSTAT
+AL,1,1,161526,Autauga County,H1,A
+AL,1,3,161527,Baldwin County,H1,A
+AL,1,5,161528,Barbour County,H1,A
+AL,1,7,161529,Bibb County,H1,A
+AL,1,9,161530,Blount County,H1,A
+AL,1,11,161531,Bullock County,H1,A
+AL,1,13,161532,Butler County,H1,A
+AL,1,15,161533,Calhoun County,H1,A
+AL,1,17,161534,Chambers County,H1,A
+AL,1,19,161535,Cherokee County,H1,A
+AL,1,21,161536,Chilton County,H1,A
+AL,1,23,161537,Choctaw County,H1,A
+AL,1,25,161538,Clarke County,H1,A
+AL,1,27,161539,Clay County,H1,A
+AL,1,29,161540,Cleburne County,H1,A
+AL,1,31,161541,Coffee County,H1,A
+AL,1,33,161542,Colbert County,H1,A
+AL,1,35,161543,Conecuh County,H1,A
+AL,1,37,161544,Coosa County,H1,A
+AL,1,39,161545,Covington County,H1,A
+AL,1,41,161546,Crenshaw County,H1,A
+AL,1,43,161547,Cullman County,H1,A
+AL,1,45,161548,Dale County,H1,A
+AL,1,47,161549,Dallas County,H1,A
+AL,1,49,161550,DeKalb County,H1,A
+AL,1,51,161551,Elmore County,H1,A
+AL,1,53,161552,Escambia County,H1,A
+AL,1,55,161553,Etowah County,H1,A
+AL,1,57,161554,Fayette County,H1,A
+AL,1,59,161555,Franklin County,H1,A
+AL,1,61,161556,Geneva County,H1,A
+AL,1,63,161557,Greene County,H1,A
+AL,1,65,161558,Hale County,H1,A
+AL,1,67,161559,Henry County,H1,A
+AL,1,69,161560,Houston County,H1,A
+AL,1,71,161561,Jackson County,H1,A
+AL,1,73,161562,Jefferson County,H1,A
+AL,1,75,161563,Lamar County,H1,A
+AL,1,77,161564,Lauderdale County,H1,A
+AL,1,79,161565,Lawrence County,H1,A
+AL,1,81,161566,Lee County,H1,A
+AL,1,83,161567,Limestone County,H1,A
+AL,1,85,161568,Lowndes County,H1,A
+AL,1,87,161569,Macon County,H1,A
+AL,1,89,161570,Madison County,H1,A
+AL,1,91,161572,Marengo County,H1,A
+AL,1,93,161573,Marion County,H1,A
+AL,1,95,161574,Marshall County,H1,A
+AL,1,97,161575,Mobile County,H1,A
+AL,1,99,161576,Monroe County,H1,A
+AL,1,101,161577,Montgomery County,H1,A
+AL,1,103,161578,Morgan County,H1,A
+AL,1,105,161579,Perry County,H1,A
+AL,1,107,161580,Pickens County,H1,A
+AL,1,109,161581,Pike County,H1,A
+AL,1,111,161582,Randolph County,H1,A
+AL,1,113,161583,Russell County,H1,A
+AL,1,115,164997,St. Clair County,H1,A
+AL,1,117,161584,Shelby County,H1,A
+AL,1,119,161585,Sumter County,H1,A
+AL,1,121,161586,Talladega County,H1,A
+AL,1,123,161587,Tallapoosa County,H1,A
+AL,1,125,161588,Tuscaloosa County,H1,A
+AL,1,127,161589,Walker County,H1,A
+AL,1,129,161590,Washington County,H1,A
+AL,1,131,161591,Wilcox County,H1,A
+AL,1,133,161592,Winston County,H1,A
+AK,2,13,1419964,Aleutians East Borough,H1,A
+AK,2,16,1419965,Aleutians West Census Area,H5,S
+AK,2,20,1416061,Anchorage Municipality,H6,C
+AK,2,50,1419966,Bethel Census Area,H5,S
+AK,2,60,1419967,Bristol Bay Borough,H1,A
+AK,2,63,2804400,Chugach Census Area,H5,S
+AK,2,66,2804401,Copper River Census Area,H5,S
+AK,2,68,1419988,Denali Borough,H1,A
+AK,2,70,1419968,Dillingham Census Area,H5,S
+AK,2,90,1419969,Fairbanks North Star Borough,H1,A
+AK,2,100,1419970,Haines Borough,H1,A
+AK,2,105,2371430,Hoonah-Angoon Census Area,H5,S
+AK,2,110,1419971,Juneau City and Borough,H6,C
+AK,2,122,1419972,Kenai Peninsula Borough,H1,A
+AK,2,130,1419973,Ketchikan Gateway Borough,H1,A
+AK,2,150,1419974,Kodiak Island Borough,H1,A
+AK,2,158,1419985,Kusilvak Census Area,H5,S
+AK,2,164,1419975,Lake and Peninsula Borough,H1,A
+AK,2,170,1419976,Matanuska-Susitna Borough,H1,A
+AK,2,180,1419977,Nome Census Area,H5,S
+AK,2,185,1419978,North Slope Borough,H1,A
+AK,2,188,1419979,Northwest Arctic Borough,H1,A
+AK,2,195,2516404,Petersburg Borough,H1,A
+AK,2,198,1419980,Prince of Wales-Hyder Census Area,H5,S
+AK,2,220,1419981,Sitka City and Borough,H6,C
+AK,2,230,2339479,Skagway Municipality,H1,A
+AK,2,240,1419983,Southeast Fairbanks Census Area,H5,S
+AK,2,275,2516402,Wrangell City and Borough,H6,C
+AK,2,282,1419986,Yakutat City and Borough,H1,A
+AK,2,290,1419987,Yukon-Koyukuk Census Area,H5,S
+AZ,4,1,25441,Apache County,H1,A
+AZ,4,3,25442,Cochise County,H1,A
+AZ,4,5,25443,Coconino County,H1,A
+AZ,4,7,40471,Gila County,H1,A
+AZ,4,9,25444,Graham County,H1,A
+AZ,4,11,42807,Greenlee County,H1,A
+AZ,4,12,43540,La Paz County,H1,A
+AZ,4,13,37026,Maricopa County,H1,A
+AZ,4,15,25445,Mohave County,H1,A
+AZ,4,17,42808,Navajo County,H1,A
+AZ,4,19,25446,Pima County,H1,A
+AZ,4,21,25447,Pinal County,H1,A
+AZ,4,23,40472,Santa Cruz County,H1,A
+AZ,4,25,42809,Yavapai County,H1,A
+AZ,4,27,23901,Yuma County,H1,A
+AR,5,1,63755,Arkansas County,H1,A
+AR,5,3,63756,Ashley County,H1,A
+AR,5,5,69895,Baxter County,H1,A
+AR,5,7,69896,Benton County,H1,A
+AR,5,9,69897,Boone County,H1,A
+AR,5,11,63757,Bradley County,H1,A
+AR,5,13,63758,Calhoun County,H1,A
+AR,5,15,66975,Carroll County,H1,A
+AR,5,17,69160,Chicot County,H1,A
+AR,5,19,66845,Clark County,H1,A
+AR,5,21,63759,Clay County,H1,A
+AR,5,23,66983,Cleburne County,H1,A
+AR,5,25,63760,Cleveland County,H1,A
+AR,5,27,63761,Columbia County,H1,A
+AR,5,29,66846,Conway County,H1,A
+AR,5,31,66847,Craighead County,H1,A
+AR,5,33,69161,Crawford County,H1,A
+AR,5,35,69162,Crittenden County,H1,A
+AR,5,37,66848,Cross County,H1,A
+AR,5,39,66849,Dallas County,H1,A
+AR,5,41,66850,Desha County,H1,A
+AR,5,43,66851,Drew County,H1,A
+AR,5,45,66852,Faulkner County,H1,A
+AR,5,47,66853,Franklin County,H1,A
+AR,5,49,66854,Fulton County,H1,A
+AR,5,51,66855,Garland County,H1,A
+AR,5,53,66856,Grant County,H1,A
+AR,5,55,66857,Greene County,H1,A
+AR,5,57,66858,Hempstead County,H1,A
+AR,5,59,66859,Hot Spring County,H1,A
+AR,5,61,66872,Howard County,H1,A
+AR,5,63,69163,Independence County,H1,A
+AR,5,65,66860,Izard County,H1,A
+AR,5,67,66861,Jackson County,H1,A
+AR,5,69,66862,Jefferson County,H1,A
+AR,5,71,66863,Johnson County,H1,A
+AR,5,73,66864,Lafayette County,H1,A
+AR,5,75,66865,Lawrence County,H1,A
+AR,5,77,66866,Lee County,H1,A
+AR,5,79,66873,Lincoln County,H1,A
+AR,5,81,66874,Little River County,H1,A
+AR,5,83,82850,Logan County,H1,A
+AR,5,85,69164,Lonoke County,H1,A
+AR,5,87,69165,Madison County,H1,A
+AR,5,89,69898,Marion County,H1,A
+AR,5,91,69166,Miller County,H1,A
+AR,5,93,69899,Mississippi County,H1,A
+AR,5,95,69167,Monroe County,H1,A
+AR,5,97,69168,Montgomery County,H1,A
+AR,5,99,69169,Nevada County,H1,A
+AR,5,101,69900,Newton County,H1,A
+AR,5,103,69170,Ouachita County,H1,A
+AR,5,105,69171,Perry County,H1,A
+AR,5,107,69172,Phillips County,H1,A
+AR,5,109,69173,Pike County,H1,A
+AR,5,111,69174,Poinsett County,H1,A
+AR,5,113,69175,Polk County,H1,A
+AR,5,115,69901,Pope County,H1,A
+AR,5,117,69176,Prairie County,H1,A
+AR,5,119,69177,Pulaski County,H1,A
+AR,5,121,69178,Randolph County,H1,A
+AR,5,123,83331,St. Francis County,H1,A
+AR,5,125,67102,Saline County,H1,A
+AR,5,127,69179,Scott County,H1,A
+AR,5,129,69180,Searcy County,H1,A
+AR,5,131,69181,Sebastian County,H1,A
+AR,5,133,69182,Sevier County,H1,A
+AR,5,135,69183,Sharp County,H1,A
+AR,5,137,69902,Stone County,H1,A
+AR,5,139,69903,Union County,H1,A
+AR,5,141,69904,Van Buren County,H1,A
+AR,5,143,69905,Washington County,H1,A
+AR,5,145,69906,White County,H1,A
+AR,5,147,69907,Woodruff County,H1,A
+AR,5,149,69908,Yell County,H1,A
+CA,6,1,1675839,Alameda County,H1,A
+CA,6,3,1675840,Alpine County,H1,A
+CA,6,5,1675841,Amador County,H1,A
+CA,6,7,1675842,Butte County,H1,A
+CA,6,9,1675885,Calaveras County,H1,A
+CA,6,11,1675902,Colusa County,H1,A
+CA,6,13,1675903,Contra Costa County,H1,A
+CA,6,15,1682074,Del Norte County,H1,A
+CA,6,17,277273,El Dorado County,H1,A
+CA,6,19,277274,Fresno County,H1,A
+CA,6,21,277275,Glenn County,H1,A
+CA,6,23,1681908,Humboldt County,H1,A
+CA,6,25,277277,Imperial County,H1,A
+CA,6,27,1804637,Inyo County,H1,A
+CA,6,29,2054176,Kern County,H1,A
+CA,6,31,277280,Kings County,H1,A
+CA,6,33,277281,Lake County,H1,A
+CA,6,35,1693324,Lassen County,H1,A
+CA,6,37,277283,Los Angeles County,H1,A
+CA,6,39,277284,Madera County,H1,A
+CA,6,41,277285,Marin County,H1,A
+CA,6,43,277286,Mariposa County,H1,A
+CA,6,45,277287,Mendocino County,H1,A
+CA,6,47,277288,Merced County,H1,A
+CA,6,49,277289,Modoc County,H1,A
+CA,6,51,277290,Mono County,H1,A
+CA,6,53,277291,Monterey County,H1,A
+CA,6,55,277292,Napa County,H1,A
+CA,6,57,1682927,Nevada County,H1,A
+CA,6,59,277294,Orange County,H1,A
+CA,6,61,277295,Placer County,H1,A
+CA,6,63,277296,Plumas County,H1,A
+CA,6,65,277297,Riverside County,H1,A
+CA,6,67,277298,Sacramento County,H1,A
+CA,6,69,277299,San Benito County,H1,A
+CA,6,71,277300,San Bernardino County,H1,A
+CA,6,73,277301,San Diego County,H1,A
+CA,6,75,277302,San Francisco County,H6,C
+CA,6,77,277303,San Joaquin County,H1,A
+CA,6,79,277304,San Luis Obispo County,H1,A
+CA,6,81,277305,San Mateo County,H1,A
+CA,6,83,277306,Santa Barbara County,H1,A
+CA,6,85,277307,Santa Clara County,H1,A
+CA,6,87,277308,Santa Cruz County,H1,A
+CA,6,89,1682610,Shasta County,H1,A
+CA,6,91,277310,Sierra County,H1,A
+CA,6,93,277311,Siskiyou County,H1,A
+CA,6,95,277312,Solano County,H1,A
+CA,6,97,1657246,Sonoma County,H1,A
+CA,6,99,277314,Stanislaus County,H1,A
+CA,6,101,277315,Sutter County,H1,A
+CA,6,103,1692767,Tehama County,H1,A
+CA,6,105,277317,Trinity County,H1,A
+CA,6,107,277318,Tulare County,H1,A
+CA,6,109,277319,Tuolumne County,H1,A
+CA,6,111,277320,Ventura County,H1,A
+CA,6,113,277321,Yolo County,H1,A
+CA,6,115,277322,Yuba County,H1,A
+CO,8,1,198116,Adams County,H1,A
+CO,8,3,198117,Alamosa County,H1,A
+CO,8,5,198118,Arapahoe County,H1,A
+CO,8,7,198119,Archuleta County,H1,A
+CO,8,9,198120,Baca County,H1,A
+CO,8,11,198121,Bent County,H1,A
+CO,8,13,198122,Boulder County,H1,A
+CO,8,14,1945881,Broomfield County,H6,C
+CO,8,15,198123,Chaffee County,H1,A
+CO,8,17,198124,Cheyenne County,H1,A
+CO,8,19,198125,Clear Creek County,H1,A
+CO,8,21,198126,Conejos County,H1,A
+CO,8,23,198127,Costilla County,H1,A
+CO,8,25,198128,Crowley County,H1,A
+CO,8,27,198129,Custer County,H1,A
+CO,8,29,198130,Delta County,H1,A
+CO,8,31,198131,Denver County,H6,C
+CO,8,33,198132,Dolores County,H1,A
+CO,8,35,198133,Douglas County,H1,A
+CO,8,37,198134,Eagle County,H1,A
+CO,8,39,198136,Elbert County,H1,A
+CO,8,41,198135,El Paso County,H1,A
+CO,8,43,198137,Fremont County,H1,A
+CO,8,45,198138,Garfield County,H1,A
+CO,8,47,198139,Gilpin County,H1,A
+CO,8,49,198140,Grand County,H1,A
+CO,8,51,198141,Gunnison County,H1,A
+CO,8,53,198142,Hinsdale County,H1,A
+CO,8,55,198143,Huerfano County,H1,A
+CO,8,57,198144,Jackson County,H1,A
+CO,8,59,198145,Jefferson County,H1,A
+CO,8,61,198146,Kiowa County,H1,A
+CO,8,63,198147,Kit Carson County,H1,A
+CO,8,65,198149,Lake County,H1,A
+CO,8,67,198148,La Plata County,H1,A
+CO,8,69,198150,Larimer County,H1,A
+CO,8,71,198151,Las Animas County,H1,A
+CO,8,73,198152,Lincoln County,H1,A
+CO,8,75,198153,Logan County,H1,A
+CO,8,77,198154,Mesa County,H1,A
+CO,8,79,198155,Mineral County,H1,A
+CO,8,81,198156,Moffat County,H1,A
+CO,8,83,198157,Montezuma County,H1,A
+CO,8,85,198158,Montrose County,H1,A
+CO,8,87,198159,Morgan County,H1,A
+CO,8,89,198160,Otero County,H1,A
+CO,8,91,198161,Ouray County,H1,A
+CO,8,93,198162,Park County,H1,A
+CO,8,95,198163,Phillips County,H1,A
+CO,8,97,198164,Pitkin County,H1,A
+CO,8,99,198165,Prowers County,H1,A
+CO,8,101,198166,Pueblo County,H1,A
+CO,8,103,198167,Rio Blanco County,H1,A
+CO,8,105,198168,Rio Grande County,H1,A
+CO,8,107,198169,Routt County,H1,A
+CO,8,109,198170,Saguache County,H1,A
+CO,8,111,198171,San Juan County,H1,A
+CO,8,113,198172,San Miguel County,H1,A
+CO,8,115,198173,Sedgwick County,H1,A
+CO,8,117,198174,Summit County,H1,A
+CO,8,119,198175,Teller County,H1,A
+CO,8,121,198176,Washington County,H1,A
+CO,8,123,198177,Weld County,H1,A
+CO,8,125,198178,Yuma County,H1,A
+CT,9,1,212794,Fairfield County,H4,N
+CT,9,3,212338,Hartford County,H4,N
+CT,9,5,212796,Litchfield County,H4,N
+CT,9,7,212797,Middlesex County,H4,N
+CT,9,9,212798,New Haven County,H4,N
+CT,9,11,212799,New London County,H4,N
+CT,9,13,212668,Tolland County,H4,N
+CT,9,15,212801,Windham County,H4,N
+DE,10,1,217271,Kent County,H1,A
+DE,10,3,217270,New Castle County,H1,A
+DE,10,5,217269,Sussex County,H1,A
+DC,11,1,1702382,District of Columbia,H6,F
+FL,12,1,308548,Alachua County,H1,A
+FL,12,3,306920,Baker County,H1,A
+FL,12,5,295738,Bay County,H1,A
+FL,12,7,303634,Bradford County,H1,A
+FL,12,9,295749,Brevard County,H1,A
+FL,12,11,295753,Broward County,H1,A
+FL,12,13,306919,Calhoun County,H1,A
+FL,12,15,295742,Charlotte County,H1,A
+FL,12,17,295752,Citrus County,H1,A
+FL,12,19,295726,Clay County,H1,A
+FL,12,21,295754,Collier County,H1,A
+FL,12,23,306909,Columbia County,H1,A
+FL,12,27,294452,DeSoto County,H1,A
+FL,12,29,295725,Dixie County,H1,A
+FL,12,31,293656,Duval County,H6,C
+FL,12,33,295737,Escambia County,H1,A
+FL,12,35,308547,Flagler County,H1,A
+FL,12,37,306911,Franklin County,H1,A
+FL,12,39,306918,Gadsden County,H1,A
+FL,12,41,303633,Gilchrist County,H1,A
+FL,12,43,295743,Glades County,H1,A
+FL,12,45,306917,Gulf County,H1,A
+FL,12,47,295736,Hamilton County,H1,A
+FL,12,49,295746,Hardee County,H1,A
+FL,12,51,307626,Hendry County,H1,A
+FL,12,53,295751,Hernando County,H1,A
+FL,12,55,295756,Highlands County,H1,A
+FL,12,57,295757,Hillsborough County,H1,A
+FL,12,59,295735,Holmes County,H1,A
+FL,12,61,307624,Indian River County,H1,A
+FL,12,63,295734,Jackson County,H1,A
+FL,12,65,295733,Jefferson County,H1,A
+FL,12,67,295732,Lafayette County,H1,A
+FL,12,69,308551,Lake County,H1,A
+FL,12,71,295758,Lee County,H1,A
+FL,12,73,306916,Leon County,H1,A
+FL,12,75,295724,Levy County,H1,A
+FL,12,77,308549,Liberty County,H1,A
+FL,12,79,295731,Madison County,H1,A
+FL,12,81,295744,Manatee County,H1,A
+FL,12,83,306922,Marion County,H1,A
+FL,12,85,308550,Martin County,H1,A
+FL,12,86,295755,Miami-Dade County,H1,A
+FL,12,87,295759,Monroe County,H1,A
+FL,12,89,295730,Nassau County,H1,A
+FL,12,91,306915,Okaloosa County,H1,A
+FL,12,93,295760,Okeechobee County,H1,A
+FL,12,95,295750,Orange County,H1,A
+FL,12,97,295748,Osceola County,H1,A
+FL,12,99,295761,Palm Beach County,H1,A
+FL,12,101,295739,Pasco County,H1,A
+FL,12,103,295745,Pinellas County,H1,A
+FL,12,105,295747,Polk County,H1,A
+FL,12,107,306910,Putnam County,H1,A
+FL,12,109,308371,St. Johns County,H1,A
+FL,12,111,308372,St. Lucie County,H1,A
+FL,12,113,306914,Santa Rosa County,H1,A
+FL,12,115,295741,Sarasota County,H1,A
+FL,12,117,303665,Seminole County,H1,A
+FL,12,119,295740,Sumter County,H1,A
+FL,12,121,295729,Suwannee County,H1,A
+FL,12,123,295728,Taylor County,H1,A
+FL,12,125,306913,Union County,H1,A
+FL,12,127,306921,Volusia County,H1,A
+FL,12,129,306912,Wakulla County,H1,A
+FL,12,131,295727,Walton County,H1,A
+FL,12,133,295762,Washington County,H1,A
+GA,13,1,349113,Appling County,H1,A
+GA,13,3,345784,Atkinson County,H1,A
+GA,13,5,344784,Bacon County,H1,A
+GA,13,7,342832,Baker County,H1,A
+GA,13,9,345255,Baldwin County,H1,A
+GA,13,11,349230,Banks County,H1,A
+GA,13,13,356976,Barrow County,H1,A
+GA,13,15,351604,Bartow County,H1,A
+GA,13,17,351606,Ben Hill County,H1,A
+GA,13,19,326638,Berrien County,H1,A
+GA,13,21,1672039,Bibb County,H6,C
+GA,13,23,347451,Bleckley County,H1,A
+GA,13,25,351605,Brantley County,H1,A
+GA,13,27,345518,Brooks County,H1,A
+GA,13,29,350496,Bryan County,H1,A
+GA,13,31,350302,Bulloch County,H1,A
+GA,13,33,347944,Burke County,H1,A
+GA,13,35,352295,Butts County,H1,A
+GA,13,37,344074,Calhoun County,H1,A
+GA,13,39,1671316,Camden County,H1,A
+GA,13,43,342852,Candler County,H1,A
+GA,13,45,326650,Carroll County,H1,A
+GA,13,47,353230,Catoosa County,H1,A
+GA,13,49,357747,Charlton County,H1,A
+GA,13,51,1694477,Chatham County,H1,A
+GA,13,53,351012,Chattahoochee County,H6,C
+GA,13,55,352213,Chattooga County,H1,A
+GA,13,57,1685718,Cherokee County,H1,A
+GA,13,59,1672699,Clarke County,H6,C
+GA,13,61,344805,Clay County,H1,A
+GA,13,63,1672399,Clayton County,H1,A
+GA,13,65,351285,Clinch County,H1,A
+GA,13,67,1686112,Cobb County,H1,A
+GA,13,69,347456,Coffee County,H1,A
+GA,13,71,343936,Colquitt County,H1,A
+GA,13,73,348865,Columbia County,H1,A
+GA,13,75,343878,Cook County,H1,A
+GA,13,77,326666,Coweta County,H1,A
+GA,13,79,342918,Crawford County,H1,A
+GA,13,81,351607,Crisp County,H1,A
+GA,13,83,347465,Dade County,H1,A
+GA,13,85,351312,Dawson County,H1,A
+GA,13,87,352234,Decatur County,H1,A
+GA,13,89,1687424,DeKalb County,H1,A
+GA,13,91,348116,Dodge County,H1,A
+GA,13,93,351608,Dooly County,H1,A
+GA,13,95,351259,Dougherty County,H1,A
+GA,13,97,1686467,Douglas County,H1,A
+GA,13,99,345041,Early County,H1,A
+GA,13,101,348092,Echols County,H6,C
+GA,13,103,350219,Effingham County,H1,A
+GA,13,105,347828,Elbert County,H1,A
+GA,13,107,347508,Emanuel County,H1,A
+GA,13,109,345923,Evans County,H1,A
+GA,13,111,351094,Fannin County,H1,A
+GA,13,113,1687740,Fayette County,H1,A
+GA,13,115,353665,Floyd County,H1,A
+GA,13,117,1673358,Forsyth County,H1,A
+GA,13,119,349324,Franklin County,H1,A
+GA,13,121,1694833,Fulton County,H1,A
+GA,13,123,351260,Gilmer County,H1,A
+GA,13,125,345642,Glascock County,H1,A
+GA,13,127,1671513,Glynn County,H1,A
+GA,13,129,356672,Gordon County,H1,A
+GA,13,131,351261,Grady County,H1,A
+GA,13,133,348672,Greene County,H1,A
+GA,13,135,1688166,Gwinnett County,H1,A
+GA,13,137,351383,Habersham County,H1,A
+GA,13,139,1686953,Hall County,H1,A
+GA,13,141,348209,Hancock County,H1,A
+GA,13,143,350637,Haralson County,H1,A
+GA,13,145,326700,Harris County,H1,A
+GA,13,147,1687995,Hart County,H1,A
+GA,13,149,347067,Heard County,H1,A
+GA,13,151,1671894,Henry County,H1,A
+GA,13,153,1671693,Houston County,H1,A
+GA,13,155,352302,Irwin County,H1,A
+GA,13,157,349570,Jackson County,H1,A
+GA,13,159,346144,Jasper County,H1,A
+GA,13,161,1687999,Jeff Davis County,H1,A
+GA,13,163,345714,Jefferson County,H1,A
+GA,13,165,342985,Jenkins County,H1,A
+GA,13,167,346664,Johnson County,H1,A
+GA,13,169,345367,Jones County,H1,A
+GA,13,171,326713,Lamar County,H1,A
+GA,13,173,348102,Lanier County,H1,A
+GA,13,175,346568,Laurens County,H1,A
+GA,13,177,343390,Lee County,H1,A
+GA,13,179,357095,Liberty County,H1,A
+GA,13,181,1688000,Lincoln County,H1,A
+GA,13,183,326719,Long County,H1,A
+GA,13,185,326720,Lowndes County,H1,A
+GA,13,187,357196,Lumpkin County,H1,A
+GA,13,189,348794,McDuffie County,H1,A
+GA,13,191,356886,McIntosh County,H1,A
+GA,13,193,346957,Macon County,H1,A
+GA,13,195,1688004,Madison County,H1,A
+GA,13,197,344107,Marion County,H1,A
+GA,13,199,346892,Meriwether County,H1,A
+GA,13,201,344088,Miller County,H1,A
+GA,13,205,343645,Mitchell County,H1,A
+GA,13,207,343073,Monroe County,H1,A
+GA,13,209,352276,Montgomery County,H1,A
+GA,13,211,346008,Morgan County,H1,A
+GA,13,213,1688005,Murray County,H1,A
+GA,13,215,357428,Muscogee County,H6,C
+GA,13,217,1673547,Newton County,H1,A
+GA,13,219,356774,Oconee County,H1,A
+GA,13,221,351262,Oglethorpe County,H1,A
+GA,13,223,349912,Paulding County,H1,A
+GA,13,225,343153,Peach County,H1,A
+GA,13,227,1673011,Pickens County,H1,A
+GA,13,229,349086,Pierce County,H1,A
+GA,13,231,346482,Pike County,H1,A
+GA,13,233,343585,Polk County,H1,A
+GA,13,235,347593,Pulaski County,H1,A
+GA,13,237,347246,Putnam County,H1,A
+GA,13,239,350547,Quitman County,H6,C
+GA,13,241,351489,Rabun County,H1,A
+GA,13,243,352242,Randolph County,H1,A
+GA,13,245,358024,Richmond County,H6,C
+GA,13,247,357592,Rockdale County,H1,A
+GA,13,249,344129,Schley County,H1,A
+GA,13,251,350204,Screven County,H1,A
+GA,13,253,351263,Seminole County,H1,A
+GA,13,255,353055,Spalding County,H1,A
+GA,13,257,350028,Stephens County,H1,A
+GA,13,259,347360,Stewart County,H1,A
+GA,13,261,343504,Sumter County,H1,A
+GA,13,263,326757,Talbot County,H1,A
+GA,13,265,347654,Taliaferro County,H1,A
+GA,13,267,347445,Tattnall County,H1,A
+GA,13,269,344156,Taylor County,H1,A
+GA,13,271,356958,Telfair County,H1,A
+GA,13,273,352238,Terrell County,H1,A
+GA,13,275,343835,Thomas County,H1,A
+GA,13,277,344090,Tift County,H1,A
+GA,13,279,345220,Toombs County,H1,A
+GA,13,281,351252,Towns County,H1,A
+GA,13,283,347505,Treutlen County,H1,A
+GA,13,285,1673298,Troup County,H1,A
+GA,13,287,343238,Turner County,H1,A
+GA,13,289,345456,Twiggs County,H1,A
+GA,13,291,353193,Union County,H1,A
+GA,13,293,346759,Upson County,H1,A
+GA,13,295,354216,Walker County,H1,A
+GA,13,297,351264,Walton County,H1,A
+GA,13,299,357722,Ware County,H1,A
+GA,13,301,347777,Warren County,H1,A
+GA,13,303,348428,Washington County,H1,A
+GA,13,305,350608,Wayne County,H1,A
+GA,13,307,352287,Webster County,H6,C
+GA,13,309,351278,Wheeler County,H1,A
+GA,13,311,326781,White County,H1,A
+GA,13,313,353513,Whitfield County,H1,A
+GA,13,315,345499,Wilcox County,H1,A
+GA,13,317,351265,Wilkes County,H1,A
+GA,13,319,346824,Wilkinson County,H1,A
+GA,13,321,343365,Worth County,H1,A
+HI,15,1,365280,Hawaii County,H1,A
+HI,15,3,365281,Honolulu County,H6,A
+HI,15,5,1702380,Kalawao County,H4,G
+HI,15,7,365282,Kauai County,H1,A
+HI,15,9,365283,Maui County,H1,A
+ID,16,1,395066,Ada County,H1,A
+ID,16,3,395067,Adams County,H1,A
+ID,16,5,395091,Bannock County,H1,A
+ID,16,7,395090,Bear Lake County,H1,A
+ID,16,9,395190,Benewah County,H1,A
+ID,16,11,400593,Bingham County,H1,A
+ID,16,13,395696,Blaine County,H1,A
+ID,16,15,395157,Boise County,H1,A
+ID,16,17,395158,Bonner County,H1,A
+ID,16,19,395407,Bonneville County,H1,A
+ID,16,21,395159,Boundary County,H1,A
+ID,16,23,401703,Butte County,H1,A
+ID,16,25,399408,Camas County,H1,A
+ID,16,27,399406,Canyon County,H1,A
+ID,16,29,395406,Caribou County,H1,A
+ID,16,31,395698,Cassia County,H1,A
+ID,16,33,399755,Clark County,H1,A
+ID,16,35,395697,Clearwater County,H1,A
+ID,16,37,399758,Custer County,H1,A
+ID,16,39,400252,Elmore County,H1,A
+ID,16,41,395585,Franklin County,H1,A
+ID,16,43,395408,Fremont County,H1,A
+ID,16,45,395442,Gem County,H1,A
+ID,16,47,395441,Gooding County,H1,A
+ID,16,49,395699,Idaho County,H1,A
+ID,16,51,399751,Jefferson County,H1,A
+ID,16,53,395662,Jerome County,H1,A
+ID,16,55,395661,Kootenai County,H1,A
+ID,16,57,395613,Latah County,H1,A
+ID,16,59,399754,Lemhi County,H1,A
+ID,16,61,395622,Lewis County,H1,A
+ID,16,63,395624,Lincoln County,H1,A
+ID,16,65,394803,Madison County,H1,A
+ID,16,67,394807,Minidoka County,H1,A
+ID,16,69,399394,Nez Perce County,H1,A
+ID,16,71,399759,Oneida County,H1,A
+ID,16,73,399760,Owyhee County,H1,A
+ID,16,75,394896,Payette County,H1,A
+ID,16,77,394951,Power County,H1,A
+ID,16,79,1702379,Shoshone County,H1,A
+ID,16,81,399297,Teton County,H1,A
+ID,16,83,399334,Twin Falls County,H1,A
+ID,16,85,399756,Valley County,H1,A
+ID,16,87,399757,Washington County,H1,A
+IL,17,1,424202,Adams County,H1,A
+IL,17,3,424203,Alexander County,H1,A
+IL,17,5,424204,Bond County,H1,A
+IL,17,7,424205,Boone County,H1,A
+IL,17,9,424206,Brown County,H1,A
+IL,17,11,424207,Bureau County,H1,A
+IL,17,13,424208,Calhoun County,H1,A
+IL,17,15,424209,Carroll County,H1,A
+IL,17,17,424210,Cass County,H1,A
+IL,17,19,424211,Champaign County,H1,A
+IL,17,21,424212,Christian County,H1,A
+IL,17,23,424213,Clark County,H1,A
+IL,17,25,424214,Clay County,H1,A
+IL,17,27,424215,Clinton County,H1,A
+IL,17,29,424216,Coles County,H1,A
+IL,17,31,1784766,Cook County,H1,A
+IL,17,33,424218,Crawford County,H1,A
+IL,17,35,424219,Cumberland County,H1,A
+IL,17,37,422190,DeKalb County,H1,A
+IL,17,39,426598,De Witt County,H1,A
+IL,17,41,424222,Douglas County,H1,A
+IL,17,43,422191,DuPage County,H1,A
+IL,17,45,424224,Edgar County,H1,A
+IL,17,47,424225,Edwards County,H1,A
+IL,17,49,424226,Effingham County,H1,A
+IL,17,51,424227,Fayette County,H1,A
+IL,17,53,424228,Ford County,H1,A
+IL,17,55,424229,Franklin County,H1,A
+IL,17,57,424230,Fulton County,H1,A
+IL,17,59,424231,Gallatin County,H1,A
+IL,17,61,424232,Greene County,H1,A
+IL,17,63,424233,Grundy County,H1,A
+IL,17,65,424234,Hamilton County,H1,A
+IL,17,67,424235,Hancock County,H1,A
+IL,17,69,424236,Hardin County,H1,A
+IL,17,71,424237,Henderson County,H1,A
+IL,17,73,424238,Henry County,H1,A
+IL,17,75,424239,Iroquois County,H1,A
+IL,17,77,424240,Jackson County,H1,A
+IL,17,79,424241,Jasper County,H1,A
+IL,17,81,424242,Jefferson County,H1,A
+IL,17,83,424243,Jersey County,H1,A
+IL,17,85,424244,Jo Daviess County,H1,A
+IL,17,87,424245,Johnson County,H1,A
+IL,17,89,424246,Kane County,H1,A
+IL,17,91,424247,Kankakee County,H1,A
+IL,17,93,424248,Kendall County,H1,A
+IL,17,95,424249,Knox County,H1,A
+IL,17,97,1784796,Lake County,H1,A
+IL,17,99,422247,LaSalle County,H1,A
+IL,17,101,424252,Lawrence County,H1,A
+IL,17,103,424253,Lee County,H1,A
+IL,17,105,424254,Livingston County,H1,A
+IL,17,107,424255,Logan County,H1,A
+IL,17,109,1784729,McDonough County,H1,A
+IL,17,111,1784815,McHenry County,H1,A
+IL,17,113,1784833,McLean County,H1,A
+IL,17,115,424256,Macon County,H1,A
+IL,17,117,424257,Macoupin County,H1,A
+IL,17,119,424258,Madison County,H1,A
+IL,17,121,424259,Marion County,H1,A
+IL,17,123,424260,Marshall County,H1,A
+IL,17,125,424261,Mason County,H1,A
+IL,17,127,1784730,Massac County,H1,A
+IL,17,129,424266,Menard County,H1,A
+IL,17,131,1784750,Mercer County,H1,A
+IL,17,133,1784865,Monroe County,H1,A
+IL,17,135,1784866,Montgomery County,H1,A
+IL,17,137,424270,Morgan County,H1,A
+IL,17,139,1784885,Moultrie County,H1,A
+IL,17,141,1784894,Ogle County,H1,A
+IL,17,143,1784920,Peoria County,H1,A
+IL,17,145,1784940,Perry County,H1,A
+IL,17,147,424275,Piatt County,H1,A
+IL,17,149,1784941,Pike County,H1,A
+IL,17,151,424277,Pope County,H1,A
+IL,17,153,1784966,Pulaski County,H1,A
+IL,17,155,424279,Putnam County,H1,A
+IL,17,157,1784967,Randolph County,H1,A
+IL,17,159,424281,Richland County,H1,A
+IL,17,161,424282,Rock Island County,H1,A
+IL,17,163,1784987,St. Clair County,H1,A
+IL,17,165,424283,Saline County,H1,A
+IL,17,167,1785010,Sangamon County,H1,A
+IL,17,169,1785037,Schuyler County,H1,A
+IL,17,171,424286,Scott County,H1,A
+IL,17,173,1785051,Shelby County,H1,A
+IL,17,175,424288,Stark County,H1,A
+IL,17,177,1785076,Stephenson County,H1,A
+IL,17,179,1785094,Tazewell County,H1,A
+IL,17,181,1785113,Union County,H1,A
+IL,17,183,1785114,Vermilion County,H1,A
+IL,17,185,424293,Wabash County,H1,A
+IL,17,187,1785134,Warren County,H1,A
+IL,17,189,1785150,Washington County,H1,A
+IL,17,191,424296,Wayne County,H1,A
+IL,17,193,424297,White County,H1,A
+IL,17,195,1785167,Whiteside County,H1,A
+IL,17,197,1785190,Will County,H1,A
+IL,17,199,1785215,Williamson County,H1,A
+IL,17,201,1785216,Winnebago County,H1,A
+IL,17,203,1785231,Woodford County,H1,A
+IN,18,1,450401,Adams County,H1,A
+IN,18,3,450402,Allen County,H1,A
+IN,18,5,451683,Bartholomew County,H1,A
+IN,18,7,450403,Benton County,H1,A
+IN,18,9,450404,Blackford County,H1,A
+IN,18,11,450405,Boone County,H1,A
+IN,18,13,451684,Brown County,H1,A
+IN,18,15,450406,Carroll County,H1,A
+IN,18,17,450339,Cass County,H1,A
+IN,18,19,450340,Clark County,H1,A
+IN,18,21,450341,Clay County,H1,A
+IN,18,23,450342,Clinton County,H1,A
+IN,18,25,451674,Crawford County,H1,A
+IN,18,27,450343,Daviess County,H1,A
+IN,18,29,450344,Dearborn County,H1,A
+IN,18,31,450345,Decatur County,H1,A
+IN,18,33,450346,DeKalb County,H1,A
+IN,18,35,450347,Delaware County,H1,A
+IN,18,37,451675,Dubois County,H1,A
+IN,18,39,450348,Elkhart County,H1,A
+IN,18,41,450349,Fayette County,H1,A
+IN,18,43,450350,Floyd County,H1,A
+IN,18,45,450351,Fountain County,H1,A
+IN,18,47,450352,Franklin County,H1,A
+IN,18,49,450353,Fulton County,H1,A
+IN,18,51,450354,Gibson County,H1,A
+IN,18,53,450355,Grant County,H1,A
+IN,18,55,451676,Greene County,H1,A
+IN,18,57,450356,Hamilton County,H1,A
+IN,18,59,450357,Hancock County,H1,A
+IN,18,61,451677,Harrison County,H1,A
+IN,18,63,450358,Hendricks County,H1,A
+IN,18,65,450359,Henry County,H1,A
+IN,18,67,450360,Howard County,H1,A
+IN,18,69,450361,Huntington County,H1,A
+IN,18,71,451678,Jackson County,H1,A
+IN,18,73,450494,Jasper County,H1,A
+IN,18,75,450362,Jay County,H1,A
+IN,18,77,450363,Jefferson County,H1,A
+IN,18,79,450364,Jennings County,H1,A
+IN,18,81,450365,Johnson County,H1,A
+IN,18,83,450366,Knox County,H1,A
+IN,18,85,450367,Kosciusko County,H1,A
+IN,18,87,450368,LaGrange County,H1,A
+IN,18,89,450495,Lake County,H1,A
+IN,18,91,450507,LaPorte County,H1,A
+IN,18,93,451703,Lawrence County,H1,A
+IN,18,95,450370,Madison County,H1,A
+IN,18,97,450371,Marion County,H6,C
+IN,18,99,450372,Marshall County,H1,A
+IN,18,101,451679,Martin County,H1,A
+IN,18,103,450373,Miami County,H1,A
+IN,18,105,451680,Monroe County,H1,A
+IN,18,107,450374,Montgomery County,H1,A
+IN,18,109,450375,Morgan County,H1,A
+IN,18,111,450376,Newton County,H1,A
+IN,18,113,450377,Noble County,H1,A
+IN,18,115,450378,Ohio County,H1,A
+IN,18,117,451681,Orange County,H1,A
+IN,18,119,450379,Owen County,H1,A
+IN,18,121,450380,Parke County,H1,A
+IN,18,123,451682,Perry County,H1,A
+IN,18,125,450381,Pike County,H1,A
+IN,18,127,450382,Porter County,H1,A
+IN,18,129,450383,Posey County,H1,A
+IN,18,131,446852,Pulaski County,H1,A
+IN,18,133,450384,Putnam County,H1,A
+IN,18,135,446853,Randolph County,H1,A
+IN,18,137,450385,Ripley County,H1,A
+IN,18,139,446854,Rush County,H1,A
+IN,18,141,452855,St. Joseph County,H1,A
+IN,18,143,450386,Scott County,H1,A
+IN,18,145,450387,Shelby County,H1,A
+IN,18,147,450388,Spencer County,H1,A
+IN,18,149,450389,Starke County,H1,A
+IN,18,151,450390,Steuben County,H1,A
+IN,18,153,450391,Sullivan County,H1,A
+IN,18,155,450392,Switzerland County,H1,A
+IN,18,157,450393,Tippecanoe County,H1,A
+IN,18,159,450394,Tipton County,H1,A
+IN,18,161,450395,Union County,H1,A
+IN,18,163,450396,Vanderburgh County,H1,A
+IN,18,165,450397,Vermillion County,H1,A
+IN,18,167,450398,Vigo County,H1,A
+IN,18,169,450399,Wabash County,H1,A
+IN,18,171,450400,Warren County,H1,A
+IN,18,173,450335,Warrick County,H1,A
+IN,18,175,451665,Washington County,H1,A
+IN,18,177,450336,Wayne County,H1,A
+IN,18,179,450337,Wells County,H1,A
+IN,18,181,450338,White County,H1,A
+IN,18,183,450369,Whitley County,H1,A
+IA,19,1,465190,Adair County,H1,A
+IA,19,3,465191,Adams County,H1,A
+IA,19,5,465192,Allamakee County,H1,A
+IA,19,7,465193,Appanoose County,H1,A
+IA,19,9,465194,Audubon County,H1,A
+IA,19,11,465195,Benton County,H1,A
+IA,19,13,465196,Black Hawk County,H1,A
+IA,19,15,465197,Boone County,H1,A
+IA,19,17,465198,Bremer County,H1,A
+IA,19,19,465199,Buchanan County,H1,A
+IA,19,21,465200,Buena Vista County,H1,A
+IA,19,23,465201,Butler County,H1,A
+IA,19,25,465202,Calhoun County,H1,A
+IA,19,27,465203,Carroll County,H1,A
+IA,19,29,465204,Cass County,H1,A
+IA,19,31,465205,Cedar County,H1,A
+IA,19,33,465206,Cerro Gordo County,H1,A
+IA,19,35,465207,Cherokee County,H1,A
+IA,19,37,465208,Chickasaw County,H1,A
+IA,19,39,465209,Clarke County,H1,A
+IA,19,41,465625,Clay County,H1,A
+IA,19,43,465210,Clayton County,H1,A
+IA,19,45,465211,Clinton County,H1,A
+IA,19,47,465212,Crawford County,H1,A
+IA,19,49,465213,Dallas County,H1,A
+IA,19,51,465214,Davis County,H1,A
+IA,19,53,465215,Decatur County,H1,A
+IA,19,55,465216,Delaware County,H1,A
+IA,19,57,465217,Des Moines County,H1,A
+IA,19,59,465218,Dickinson County,H1,A
+IA,19,61,465219,Dubuque County,H1,A
+IA,19,63,465220,Emmet County,H1,A
+IA,19,65,465221,Fayette County,H1,A
+IA,19,67,465222,Floyd County,H1,A
+IA,19,69,465223,Franklin County,H1,A
+IA,19,71,465224,Fremont County,H1,A
+IA,19,73,465225,Greene County,H1,A
+IA,19,75,465226,Grundy County,H1,A
+IA,19,77,465227,Guthrie County,H1,A
+IA,19,79,465228,Hamilton County,H1,A
+IA,19,81,465229,Hancock County,H1,A
+IA,19,83,465230,Hardin County,H1,A
+IA,19,85,465231,Harrison County,H1,A
+IA,19,87,465232,Henry County,H1,A
+IA,19,89,465233,Howard County,H1,A
+IA,19,91,465234,Humboldt County,H1,A
+IA,19,93,465235,Ida County,H1,A
+IA,19,95,465236,Iowa County,H1,A
+IA,19,97,465237,Jackson County,H1,A
+IA,19,99,465238,Jasper County,H1,A
+IA,19,101,465239,Jefferson County,H1,A
+IA,19,103,465240,Johnson County,H1,A
+IA,19,105,465241,Jones County,H1,A
+IA,19,107,465242,Keokuk County,H1,A
+IA,19,109,465243,Kossuth County,H1,A
+IA,19,111,465244,Lee County,H1,A
+IA,19,113,465245,Linn County,H1,A
+IA,19,115,465246,Louisa County,H1,A
+IA,19,117,465247,Lucas County,H1,A
+IA,19,119,465248,Lyon County,H1,A
+IA,19,121,465249,Madison County,H1,A
+IA,19,123,465250,Mahaska County,H1,A
+IA,19,125,465251,Marion County,H1,A
+IA,19,127,465252,Marshall County,H1,A
+IA,19,129,465253,Mills County,H1,A
+IA,19,131,465254,Mitchell County,H1,A
+IA,19,133,465255,Monona County,H1,A
+IA,19,135,465256,Monroe County,H1,A
+IA,19,137,465257,Montgomery County,H1,A
+IA,19,139,465258,Muscatine County,H1,A
+IA,19,141,465259,O'Brien County,H1,A
+IA,19,143,465260,Osceola County,H1,A
+IA,19,145,465261,Page County,H1,A
+IA,19,147,465262,Palo Alto County,H1,A
+IA,19,149,465263,Plymouth County,H1,A
+IA,19,151,465264,Pocahontas County,H1,A
+IA,19,153,465265,Polk County,H1,A
+IA,19,155,465266,Pottawattamie County,H1,A
+IA,19,157,465267,Poweshiek County,H1,A
+IA,19,159,465268,Ringgold County,H1,A
+IA,19,161,465269,Sac County,H1,A
+IA,19,163,465270,Scott County,H1,A
+IA,19,165,465271,Shelby County,H1,A
+IA,19,167,465272,Sioux County,H1,A
+IA,19,169,465273,Story County,H1,A
+IA,19,171,465274,Tama County,H1,A
+IA,19,173,465275,Taylor County,H1,A
+IA,19,175,465276,Union County,H1,A
+IA,19,177,465277,Van Buren County,H1,A
+IA,19,179,465278,Wapello County,H1,A
+IA,19,181,465279,Warren County,H1,A
+IA,19,183,465280,Washington County,H1,A
+IA,19,185,465281,Wayne County,H1,A
+IA,19,187,465282,Webster County,H1,A
+IA,19,189,465283,Winnebago County,H1,A
+IA,19,191,465284,Winneshiek County,H1,A
+IA,19,193,465285,Woodbury County,H1,A
+IA,19,195,465286,Worth County,H1,A
+IA,19,197,465287,Wright County,H1,A
+KS,20,1,484970,Allen County,H1,A
+KS,20,3,484971,Anderson County,H1,A
+KS,20,5,484972,Atchison County,H1,A
+KS,20,7,484973,Barber County,H1,A
+KS,20,9,484974,Barton County,H1,A
+KS,20,11,484975,Bourbon County,H1,A
+KS,20,13,484976,Brown County,H1,A
+KS,20,15,484977,Butler County,H1,A
+KS,20,17,484978,Chase County,H1,A
+KS,20,19,484979,Chautauqua County,H1,A
+KS,20,21,484980,Cherokee County,H1,A
+KS,20,23,484981,Cheyenne County,H1,A
+KS,20,25,484982,Clark County,H1,A
+KS,20,27,484983,Clay County,H1,A
+KS,20,29,484984,Cloud County,H1,A
+KS,20,31,484985,Coffey County,H1,A
+KS,20,33,484986,Comanche County,H1,A
+KS,20,35,484987,Cowley County,H1,A
+KS,20,37,484988,Crawford County,H1,A
+KS,20,39,484989,Decatur County,H1,A
+KS,20,41,484990,Dickinson County,H1,A
+KS,20,43,484991,Doniphan County,H1,A
+KS,20,45,484992,Douglas County,H1,A
+KS,20,47,484993,Edwards County,H1,A
+KS,20,49,484994,Elk County,H1,A
+KS,20,51,484995,Ellis County,H1,A
+KS,20,53,484996,Ellsworth County,H1,A
+KS,20,55,485326,Finney County,H1,A
+KS,20,57,484997,Ford County,H1,A
+KS,20,59,484998,Franklin County,H1,A
+KS,20,61,484999,Geary County,H1,A
+KS,20,63,485000,Gove County,H1,A
+KS,20,65,481811,Graham County,H1,A
+KS,20,67,485099,Grant County,H1,A
+KS,20,69,485001,Gray County,H1,A
+KS,20,71,485002,Greeley County,H6,C
+KS,20,73,485003,Greenwood County,H1,A
+KS,20,75,485327,Hamilton County,H1,A
+KS,20,77,485004,Harper County,H1,A
+KS,20,79,485005,Harvey County,H1,A
+KS,20,81,485328,Haskell County,H1,A
+KS,20,83,485006,Hodgeman County,H1,A
+KS,20,85,485007,Jackson County,H1,A
+KS,20,87,485008,Jefferson County,H1,A
+KS,20,89,485009,Jewell County,H1,A
+KS,20,91,485010,Johnson County,H1,A
+KS,20,93,485011,Kearny County,H1,A
+KS,20,95,485012,Kingman County,H1,A
+KS,20,97,485013,Kiowa County,H1,A
+KS,20,99,485014,Labette County,H1,A
+KS,20,101,485015,Lane County,H1,A
+KS,20,103,485016,Leavenworth County,H1,A
+KS,20,105,485017,Lincoln County,H1,A
+KS,20,107,485018,Linn County,H1,A
+KS,20,109,485019,Logan County,H1,A
+KS,20,111,485020,Lyon County,H1,A
+KS,20,113,485021,McPherson County,H1,A
+KS,20,115,485022,Marion County,H1,A
+KS,20,117,485023,Marshall County,H1,A
+KS,20,119,485024,Meade County,H1,A
+KS,20,121,485025,Miami County,H1,A
+KS,20,123,485026,Mitchell County,H1,A
+KS,20,125,485027,Montgomery County,H1,A
+KS,20,127,485028,Morris County,H1,A
+KS,20,129,485135,Morton County,H1,A
+KS,20,131,485029,Nemaha County,H1,A
+KS,20,133,485030,Neosho County,H1,A
+KS,20,135,485031,Ness County,H1,A
+KS,20,137,485032,Norton County,H1,A
+KS,20,139,485033,Osage County,H1,A
+KS,20,141,485034,Osborne County,H1,A
+KS,20,143,485035,Ottawa County,H1,A
+KS,20,145,485036,Pawnee County,H1,A
+KS,20,147,485037,Phillips County,H1,A
+KS,20,149,485038,Pottawatomie County,H1,A
+KS,20,151,485039,Pratt County,H1,A
+KS,20,153,485040,Rawlins County,H1,A
+KS,20,155,485041,Reno County,H1,A
+KS,20,157,485042,Republic County,H1,A
+KS,20,159,485043,Rice County,H1,A
+KS,20,161,485044,Riley County,H1,A
+KS,20,163,485045,Rooks County,H1,A
+KS,20,165,485358,Rush County,H1,A
+KS,20,167,485046,Russell County,H1,A
+KS,20,169,485047,Saline County,H1,A
+KS,20,171,485048,Scott County,H1,A
+KS,20,173,485049,Sedgwick County,H1,A
+KS,20,175,485050,Seward County,H1,A
+KS,20,177,485051,Shawnee County,H1,A
+KS,20,179,485052,Sheridan County,H1,A
+KS,20,181,485053,Sherman County,H1,A
+KS,20,183,484969,Smith County,H1,A
+KS,20,185,485054,Stafford County,H1,A
+KS,20,187,485055,Stanton County,H1,A
+KS,20,189,485056,Stevens County,H1,A
+KS,20,191,481812,Sumner County,H1,A
+KS,20,193,485057,Thomas County,H1,A
+KS,20,195,485058,Trego County,H1,A
+KS,20,197,485059,Wabaunsee County,H1,A
+KS,20,199,485060,Wallace County,H1,A
+KS,20,201,485061,Washington County,H1,A
+KS,20,203,485062,Wichita County,H1,A
+KS,20,205,485063,Wilson County,H1,A
+KS,20,207,485064,Woodson County,H1,A
+KS,20,209,485065,Wyandotte County,H6,C
+KY,21,1,516847,Adair County,H1,A
+KY,21,3,516848,Allen County,H1,A
+KY,21,5,516849,Anderson County,H1,A
+KY,21,7,516850,Ballard County,H1,A
+KY,21,9,516851,Barren County,H1,A
+KY,21,11,516852,Bath County,H1,A
+KY,21,13,516853,Bell County,H1,A
+KY,21,15,516854,Boone County,H1,A
+KY,21,17,516855,Bourbon County,H1,A
+KY,21,19,516856,Boyd County,H1,A
+KY,21,21,516857,Boyle County,H1,A
+KY,21,23,516858,Bracken County,H1,A
+KY,21,25,516859,Breathitt County,H1,A
+KY,21,27,516860,Breckinridge County,H1,A
+KY,21,29,516861,Bullitt County,H1,A
+KY,21,31,516862,Butler County,H1,A
+KY,21,33,516863,Caldwell County,H1,A
+KY,21,35,516864,Calloway County,H1,A
+KY,21,37,516865,Campbell County,H1,A
+KY,21,39,516866,Carlisle County,H1,A
+KY,21,41,516867,Carroll County,H1,A
+KY,21,43,516868,Carter County,H1,A
+KY,21,45,516869,Casey County,H1,A
+KY,21,47,516870,Christian County,H1,A
+KY,21,49,516871,Clark County,H1,A
+KY,21,51,516872,Clay County,H1,A
+KY,21,53,516873,Clinton County,H1,A
+KY,21,55,516874,Crittenden County,H1,A
+KY,21,57,516875,Cumberland County,H1,A
+KY,21,59,516876,Daviess County,H1,A
+KY,21,61,516877,Edmonson County,H1,A
+KY,21,63,516878,Elliott County,H1,A
+KY,21,65,516879,Estill County,H1,A
+KY,21,67,516880,Fayette County,H6,C
+KY,21,69,516881,Fleming County,H1,A
+KY,21,71,516882,Floyd County,H1,A
+KY,21,73,516883,Franklin County,H1,A
+KY,21,75,516884,Fulton County,H1,A
+KY,21,77,516885,Gallatin County,H1,A
+KY,21,79,516886,Garrard County,H1,A
+KY,21,81,516887,Grant County,H1,A
+KY,21,83,516888,Graves County,H1,A
+KY,21,85,516889,Grayson County,H1,A
+KY,21,87,516890,Green County,H1,A
+KY,21,89,516891,Greenup County,H1,A
+KY,21,91,516892,Hancock County,H1,A
+KY,21,93,516893,Hardin County,H1,A
+KY,21,95,516894,Harlan County,H1,A
+KY,21,97,516895,Harrison County,H1,A
+KY,21,99,516896,Hart County,H1,A
+KY,21,101,516897,Henderson County,H1,A
+KY,21,103,516898,Henry County,H1,A
+KY,21,105,516899,Hickman County,H1,A
+KY,21,107,516900,Hopkins County,H1,A
+KY,21,109,516901,Jackson County,H1,A
+KY,21,111,516902,Jefferson County,H6,C
+KY,21,113,516903,Jessamine County,H1,A
+KY,21,115,516904,Johnson County,H1,A
+KY,21,117,516905,Kenton County,H1,A
+KY,21,119,516906,Knott County,H1,A
+KY,21,121,516907,Knox County,H1,A
+KY,21,123,516908,Larue County,H1,A
+KY,21,125,516909,Laurel County,H1,A
+KY,21,127,516910,Lawrence County,H1,A
+KY,21,129,516911,Lee County,H1,A
+KY,21,131,516912,Leslie County,H1,A
+KY,21,133,516913,Letcher County,H1,A
+KY,21,135,516914,Lewis County,H1,A
+KY,21,137,516915,Lincoln County,H1,A
+KY,21,139,516916,Livingston County,H1,A
+KY,21,141,516917,Logan County,H1,A
+KY,21,143,516918,Lyon County,H1,A
+KY,21,145,516925,McCracken County,H1,A
+KY,21,147,516926,McCreary County,H1,A
+KY,21,149,516927,McLean County,H1,A
+KY,21,151,516919,Madison County,H1,A
+KY,21,153,516920,Magoffin County,H1,A
+KY,21,155,516921,Marion County,H1,A
+KY,21,157,516922,Marshall County,H1,A
+KY,21,159,516923,Martin County,H1,A
+KY,21,161,516924,Mason County,H1,A
+KY,21,163,516928,Meade County,H1,A
+KY,21,165,516929,Menifee County,H1,A
+KY,21,167,516930,Mercer County,H1,A
+KY,21,169,516931,Metcalfe County,H1,A
+KY,21,171,516932,Monroe County,H1,A
+KY,21,173,516933,Montgomery County,H1,A
+KY,21,175,516934,Morgan County,H1,A
+KY,21,177,516935,Muhlenberg County,H1,A
+KY,21,179,516936,Nelson County,H1,A
+KY,21,181,516937,Nicholas County,H1,A
+KY,21,183,516938,Ohio County,H1,A
+KY,21,185,516939,Oldham County,H1,A
+KY,21,187,516940,Owen County,H1,A
+KY,21,189,516941,Owsley County,H1,A
+KY,21,191,516942,Pendleton County,H1,A
+KY,21,193,516943,Perry County,H1,A
+KY,21,195,516944,Pike County,H1,A
+KY,21,197,516945,Powell County,H1,A
+KY,21,199,516946,Pulaski County,H1,A
+KY,21,201,516947,Robertson County,H1,A
+KY,21,203,516948,Rockcastle County,H1,A
+KY,21,205,516949,Rowan County,H1,A
+KY,21,207,516950,Russell County,H1,A
+KY,21,209,516951,Scott County,H1,A
+KY,21,211,516952,Shelby County,H1,A
+KY,21,213,516953,Simpson County,H1,A
+KY,21,215,516954,Spencer County,H1,A
+KY,21,217,516955,Taylor County,H1,A
+KY,21,219,516956,Todd County,H1,A
+KY,21,221,516957,Trigg County,H1,A
+KY,21,223,516958,Trimble County,H1,A
+KY,21,225,516959,Union County,H1,A
+KY,21,227,516960,Warren County,H1,A
+KY,21,229,516961,Washington County,H1,A
+KY,21,231,516962,Wayne County,H1,A
+KY,21,233,516963,Webster County,H1,A
+KY,21,235,516964,Whitley County,H1,A
+KY,21,237,516965,Wolfe County,H1,A
+KY,21,239,516966,Woodford County,H1,A
+LA,22,1,558389,Acadia Parish,H1,A
+LA,22,3,558397,Allen Parish,H1,A
+LA,22,5,558403,Ascension Parish,H1,A
+LA,22,7,558414,Assumption Parish,H1,A
+LA,22,9,558424,Avoyelles Parish,H1,A
+LA,22,11,558436,Beauregard Parish,H1,A
+LA,22,13,558445,Bienville Parish,H1,A
+LA,22,15,558453,Bossier Parish,H1,A
+LA,22,17,558458,Caddo Parish,H1,A
+LA,22,19,558468,Calcasieu Parish,H1,A
+LA,22,21,558477,Caldwell Parish,H1,A
+LA,22,23,558488,Cameron Parish,H1,A
+LA,22,25,558495,Catahoula Parish,H1,A
+LA,22,27,558505,Claiborne Parish,H1,A
+LA,22,29,558515,Concordia Parish,H1,A
+LA,22,31,558521,De Soto Parish,H1,A
+LA,22,33,558530,East Baton Rouge Parish,H6,B
+LA,22,35,558534,East Carroll Parish,H1,A
+LA,22,37,558542,East Feliciana Parish,H1,A
+LA,22,39,558551,Evangeline Parish,H1,A
+LA,22,41,558557,Franklin Parish,H1,A
+LA,22,43,558567,Grant Parish,H1,A
+LA,22,45,558576,Iberia Parish,H1,A
+LA,22,47,558582,Iberville Parish,H1,A
+LA,22,49,558592,Jackson Parish,H1,A
+LA,22,51,558027,Jefferson Parish,H1,A
+LA,22,53,558039,Jefferson Davis Parish,H1,A
+LA,22,55,558059,Lafayette Parish,H6,B
+LA,22,57,558065,Lafourche Parish,H1,A
+LA,22,59,558049,LaSalle Parish,H1,A
+LA,22,61,558077,Lincoln Parish,H1,A
+LA,22,63,558083,Livingston Parish,H1,A
+LA,22,65,558088,Madison Parish,H1,A
+LA,22,67,558097,Morehouse Parish,H1,A
+LA,22,69,558108,Natchitoches Parish,H1,A
+LA,22,71,558113,Orleans Parish,H6,C
+LA,22,73,558114,Ouachita Parish,H1,A
+LA,22,75,558125,Plaquemines Parish,H1,A
+LA,22,77,559449,Pointe Coupee Parish,H1,A
+LA,22,79,559460,Rapides Parish,H1,A
+LA,22,81,559472,Red River Parish,H1,A
+LA,22,83,559481,Richland Parish,H1,A
+LA,22,85,559489,Sabine Parish,H1,A
+LA,22,87,1629542,St. Bernard Parish,H1,A
+LA,22,89,1629449,St. Charles Parish,H1,A
+LA,22,91,1629457,St. Helena Parish,H1,A
+LA,22,93,1629464,St. James Parish,H1,A
+LA,22,95,1629472,St. John the Baptist Parish,H1,A
+LA,22,97,1629479,St. Landry Parish,H1,A
+LA,22,99,1629486,St. Martin Parish,H1,A
+LA,22,101,1629492,St. Mary Parish,H1,A
+LA,22,103,1629503,St. Tammany Parish,H1,A
+LA,22,105,559500,Tangipahoa Parish,H1,A
+LA,22,107,559509,Tensas Parish,H1,A
+LA,22,109,559517,Terrebonne Parish,H6,A
+LA,22,111,559528,Union Parish,H1,A
+LA,22,113,559538,Vermilion Parish,H1,A
+LA,22,115,559548,Vernon Parish,H1,A
+LA,22,117,559557,Washington Parish,H1,A
+LA,22,119,559567,Webster Parish,H1,A
+LA,22,121,559570,West Baton Rouge Parish,H1,A
+LA,22,123,559578,West Carroll Parish,H1,A
+LA,22,125,559584,West Feliciana Parish,H1,A
+LA,22,127,559594,Winn Parish,H1,A
+ME,23,1,581286,Androscoggin County,H1,A
+ME,23,3,581287,Aroostook County,H1,A
+ME,23,5,581288,Cumberland County,H1,A
+ME,23,7,581289,Franklin County,H1,A
+ME,23,9,581290,Hancock County,H1,A
+ME,23,11,581291,Kennebec County,H1,A
+ME,23,13,581292,Knox County,H1,A
+ME,23,15,581293,Lincoln County,H1,A
+ME,23,17,581294,Oxford County,H1,A
+ME,23,19,581295,Penobscot County,H1,A
+ME,23,21,581296,Piscataquis County,H1,A
+ME,23,23,581297,Sagadahoc County,H1,A
+ME,23,25,581298,Somerset County,H1,A
+ME,23,27,581299,Waldo County,H1,A
+ME,23,29,581300,Washington County,H1,A
+ME,23,31,581301,York County,H1,A
+MD,24,1,1713506,Allegany County,H1,A
+MD,24,3,1710958,Anne Arundel County,H1,A
+MD,24,5,1695314,Baltimore County,H1,A
+MD,24,9,1676636,Calvert County,H1,A
+MD,24,11,595737,Caroline County,H1,A
+MD,24,13,1696228,Carroll County,H1,A
+MD,24,15,596115,Cecil County,H1,A
+MD,24,17,1676992,Charles County,H1,A
+MD,24,19,596495,Dorchester County,H1,A
+MD,24,21,1711211,Frederick County,H1,A
+MD,24,23,1711058,Garrett County,H1,A
+MD,24,25,1698178,Harford County,H1,A
+MD,24,27,1709077,Howard County,H1,A
+MD,24,29,593907,Kent County,H1,A
+MD,24,31,1712500,Montgomery County,H1,A
+MD,24,33,1714670,Prince George's County,H1,A
+MD,24,35,596089,Queen Anne's County,H1,A
+MD,24,37,1697853,St. Mary's County,H1,A
+MD,24,39,596907,Somerset County,H1,A
+MD,24,41,592947,Talbot County,H1,A
+MD,24,43,1714220,Washington County,H1,A
+MD,24,45,1668606,Wicomico County,H1,A
+MD,24,47,1668802,Worcester County,H1,A
+MD,24,510,1702381,Baltimore city,C7,F
+MA,25,1,606927,Barnstable County,H1,A
+MA,25,3,606928,Berkshire County,H4,N
+MA,25,5,606929,Bristol County,H1,A
+MA,25,7,606930,Dukes County,H1,A
+MA,25,9,606931,Essex County,H4,N
+MA,25,11,606932,Franklin County,H4,N
+MA,25,13,606933,Hampden County,H4,N
+MA,25,15,606934,Hampshire County,H4,N
+MA,25,17,606935,Middlesex County,H4,N
+MA,25,19,606936,Nantucket County,H4,N
+MA,25,21,606937,Norfolk County,H1,A
+MA,25,23,606938,Plymouth County,H1,A
+MA,25,25,606939,Suffolk County,H4,N
+MA,25,27,606940,Worcester County,H4,N
+MI,26,1,1622943,Alcona County,H1,A
+MI,26,3,1622944,Alger County,H1,A
+MI,26,5,1622945,Allegan County,H1,A
+MI,26,7,1622946,Alpena County,H1,A
+MI,26,9,1622947,Antrim County,H1,A
+MI,26,11,1622948,Arenac County,H1,A
+MI,26,13,1622949,Baraga County,H1,A
+MI,26,15,1622950,Barry County,H1,A
+MI,26,17,1622951,Bay County,H1,A
+MI,26,19,1622952,Benzie County,H1,A
+MI,26,21,1622953,Berrien County,H1,A
+MI,26,23,1622954,Branch County,H1,A
+MI,26,25,1622955,Calhoun County,H1,A
+MI,26,27,1622956,Cass County,H1,A
+MI,26,29,1622957,Charlevoix County,H1,A
+MI,26,31,1622958,Cheboygan County,H1,A
+MI,26,33,1622959,Chippewa County,H1,A
+MI,26,35,1622960,Clare County,H1,A
+MI,26,37,1622961,Clinton County,H1,A
+MI,26,39,1622962,Crawford County,H1,A
+MI,26,41,1622963,Delta County,H1,A
+MI,26,43,1622964,Dickinson County,H1,A
+MI,26,45,1622965,Eaton County,H1,A
+MI,26,47,1622966,Emmet County,H1,A
+MI,26,49,1622967,Genesee County,H1,A
+MI,26,51,1622968,Gladwin County,H1,A
+MI,26,53,1622969,Gogebic County,H1,A
+MI,26,55,1622970,Grand Traverse County,H1,A
+MI,26,57,1622971,Gratiot County,H1,A
+MI,26,59,1622972,Hillsdale County,H1,A
+MI,26,61,1622973,Houghton County,H1,A
+MI,26,63,1622974,Huron County,H1,A
+MI,26,65,1622975,Ingham County,H1,A
+MI,26,67,1622976,Ionia County,H1,A
+MI,26,69,1622977,Iosco County,H1,A
+MI,26,71,1622978,Iron County,H1,A
+MI,26,73,1622979,Isabella County,H1,A
+MI,26,75,1622980,Jackson County,H1,A
+MI,26,77,1622981,Kalamazoo County,H1,A
+MI,26,79,1622982,Kalkaska County,H1,A
+MI,26,81,1622983,Kent County,H1,A
+MI,26,83,1622984,Keweenaw County,H1,A
+MI,26,85,1622985,Lake County,H1,A
+MI,26,87,1622986,Lapeer County,H1,A
+MI,26,89,1622987,Leelanau County,H1,A
+MI,26,91,1622988,Lenawee County,H1,A
+MI,26,93,1622989,Livingston County,H1,A
+MI,26,95,1622990,Luce County,H1,A
+MI,26,97,1622991,Mackinac County,H1,A
+MI,26,99,1622992,Macomb County,H1,A
+MI,26,101,1622993,Manistee County,H1,A
+MI,26,103,1622994,Marquette County,H1,A
+MI,26,105,1622995,Mason County,H1,A
+MI,26,107,1622996,Mecosta County,H1,A
+MI,26,109,1622997,Menominee County,H1,A
+MI,26,111,1622998,Midland County,H1,A
+MI,26,113,1622999,Missaukee County,H1,A
+MI,26,115,1623000,Monroe County,H1,A
+MI,26,117,1623001,Montcalm County,H1,A
+MI,26,119,1623002,Montmorency County,H1,A
+MI,26,121,1623003,Muskegon County,H1,A
+MI,26,123,1623004,Newaygo County,H1,A
+MI,26,125,1623005,Oakland County,H1,A
+MI,26,127,1623006,Oceana County,H1,A
+MI,26,129,1623007,Ogemaw County,H1,A
+MI,26,131,1623008,Ontonagon County,H1,A
+MI,26,133,1623009,Osceola County,H1,A
+MI,26,135,1623010,Oscoda County,H1,A
+MI,26,137,1623011,Otsego County,H1,A
+MI,26,139,1623012,Ottawa County,H1,A
+MI,26,141,1623013,Presque Isle County,H1,A
+MI,26,143,1623014,Roscommon County,H1,A
+MI,26,145,1623015,Saginaw County,H1,A
+MI,26,147,1625033,St. Clair County,H1,A
+MI,26,149,1625034,St. Joseph County,H1,A
+MI,26,151,1623016,Sanilac County,H1,A
+MI,26,153,1623017,Schoolcraft County,H1,A
+MI,26,155,1623018,Shiawassee County,H1,A
+MI,26,157,1623019,Tuscola County,H1,A
+MI,26,159,1623020,Van Buren County,H1,A
+MI,26,161,1623021,Washtenaw County,H1,A
+MI,26,163,1623022,Wayne County,H1,A
+MI,26,165,1623023,Wexford County,H1,A
+MN,27,1,663198,Aitkin County,H1,A
+MN,27,3,659447,Anoka County,H1,A
+MN,27,5,659448,Becker County,H1,A
+MN,27,7,659449,Beltrami County,H1,A
+MN,27,9,659450,Benton County,H1,A
+MN,27,11,659451,Big Stone County,H1,A
+MN,27,13,659452,Blue Earth County,H1,A
+MN,27,15,659453,Brown County,H1,A
+MN,27,17,659454,Carlton County,H1,A
+MN,27,19,659455,Carver County,H1,A
+MN,27,21,659456,Cass County,H1,A
+MN,27,23,659457,Chippewa County,H1,A
+MN,27,25,659458,Chisago County,H1,A
+MN,27,27,659459,Clay County,H1,A
+MN,27,29,659460,Clearwater County,H1,A
+MN,27,31,659461,Cook County,H1,A
+MN,27,33,659462,Cottonwood County,H1,A
+MN,27,35,659463,Crow Wing County,H1,A
+MN,27,37,659464,Dakota County,H1,A
+MN,27,39,659465,Dodge County,H1,A
+MN,27,41,659466,Douglas County,H1,A
+MN,27,43,659467,Faribault County,H1,A
+MN,27,45,659468,Fillmore County,H1,A
+MN,27,47,659469,Freeborn County,H1,A
+MN,27,49,659470,Goodhue County,H1,A
+MN,27,51,659471,Grant County,H1,A
+MN,27,53,659472,Hennepin County,H1,A
+MN,27,55,659473,Houston County,H1,A
+MN,27,57,659474,Hubbard County,H1,A
+MN,27,59,659475,Isanti County,H1,A
+MN,27,61,659476,Itasca County,H1,A
+MN,27,63,659477,Jackson County,H1,A
+MN,27,65,659478,Kanabec County,H1,A
+MN,27,67,659479,Kandiyohi County,H1,A
+MN,27,69,659480,Kittson County,H1,A
+MN,27,71,659481,Koochiching County,H1,A
+MN,27,73,659482,Lac qui Parle County,H1,A
+MN,27,75,659484,Lake County,H1,A
+MN,27,77,659483,Lake of the Woods County,H1,A
+MN,27,79,659485,Le Sueur County,H1,A
+MN,27,81,659486,Lincoln County,H1,A
+MN,27,83,659487,Lyon County,H1,A
+MN,27,85,659491,McLeod County,H1,A
+MN,27,87,659488,Mahnomen County,H1,A
+MN,27,89,659489,Marshall County,H1,A
+MN,27,91,659490,Martin County,H1,A
+MN,27,93,659492,Meeker County,H1,A
+MN,27,95,659493,Mille Lacs County,H1,A
+MN,27,97,659494,Morrison County,H1,A
+MN,27,99,659495,Mower County,H1,A
+MN,27,101,659496,Murray County,H1,A
+MN,27,103,659497,Nicollet County,H1,A
+MN,27,105,659498,Nobles County,H1,A
+MN,27,107,659499,Norman County,H1,A
+MN,27,109,659500,Olmsted County,H1,A
+MN,27,111,659501,Otter Tail County,H1,A
+MN,27,113,659502,Pennington County,H1,A
+MN,27,115,659503,Pine County,H1,A
+MN,27,117,659504,Pipestone County,H1,A
+MN,27,119,659505,Polk County,H1,A
+MN,27,121,659506,Pope County,H1,A
+MN,27,123,659507,Ramsey County,H1,A
+MN,27,125,659508,Red Lake County,H1,A
+MN,27,127,659509,Redwood County,H1,A
+MN,27,129,659510,Renville County,H1,A
+MN,27,131,659511,Rice County,H1,A
+MN,27,133,659512,Rock County,H1,A
+MN,27,135,659513,Roseau County,H1,A
+MN,27,137,662850,St. Louis County,H1,A
+MN,27,139,659514,Scott County,H1,A
+MN,27,141,659515,Sherburne County,H1,A
+MN,27,143,659516,Sibley County,H1,A
+MN,27,145,659517,Stearns County,H1,A
+MN,27,147,659518,Steele County,H1,A
+MN,27,149,659519,Stevens County,H1,A
+MN,27,151,659520,Swift County,H1,A
+MN,27,153,659521,Todd County,H1,A
+MN,27,155,659522,Traverse County,H1,A
+MN,27,157,659523,Wabasha County,H1,A
+MN,27,159,659524,Wadena County,H1,A
+MN,27,161,659525,Waseca County,H1,A
+MN,27,163,659526,Washington County,H1,A
+MN,27,165,659527,Watonwan County,H1,A
+MN,27,167,659528,Wilkin County,H1,A
+MN,27,169,659529,Winona County,H1,A
+MN,27,171,659530,Wright County,H1,A
+MN,27,173,659531,Yellow Medicine County,H1,A
+MS,28,1,695726,Adams County,H1,A
+MS,28,3,711755,Alcorn County,H1,A
+MS,28,5,695727,Amite County,H1,A
+MS,28,7,695728,Attala County,H1,A
+MS,28,9,695729,Benton County,H1,A
+MS,28,11,695730,Bolivar County,H1,A
+MS,28,13,695731,Calhoun County,H1,A
+MS,28,15,695732,Carroll County,H1,A
+MS,28,17,695733,Chickasaw County,H1,A
+MS,28,19,695734,Choctaw County,H1,A
+MS,28,21,695735,Claiborne County,H1,A
+MS,28,23,695736,Clarke County,H1,A
+MS,28,25,695737,Clay County,H1,A
+MS,28,27,695738,Coahoma County,H1,A
+MS,28,29,695739,Copiah County,H1,A
+MS,28,31,695740,Covington County,H1,A
+MS,28,33,695741,DeSoto County,H1,A
+MS,28,35,695742,Forrest County,H1,A
+MS,28,37,695743,Franklin County,H1,A
+MS,28,39,695744,George County,H1,A
+MS,28,41,695745,Greene County,H1,A
+MS,28,43,695746,Grenada County,H1,A
+MS,28,45,711756,Hancock County,H1,A
+MS,28,47,695747,Harrison County,H1,A
+MS,28,49,695748,Hinds County,H1,A
+MS,28,51,695749,Holmes County,H1,A
+MS,28,53,695750,Humphreys County,H1,A
+MS,28,55,695751,Issaquena County,H1,A
+MS,28,57,695752,Itawamba County,H1,A
+MS,28,59,695753,Jackson County,H1,A
+MS,28,61,695754,Jasper County,H1,A
+MS,28,63,695755,Jefferson County,H1,A
+MS,28,65,695756,Jefferson Davis County,H1,A
+MS,28,67,695757,Jones County,H1,A
+MS,28,69,695758,Kemper County,H1,A
+MS,28,71,695759,Lafayette County,H1,A
+MS,28,73,695760,Lamar County,H1,A
+MS,28,75,695761,Lauderdale County,H1,A
+MS,28,77,695762,Lawrence County,H1,A
+MS,28,79,695763,Leake County,H1,A
+MS,28,81,695764,Lee County,H1,A
+MS,28,83,695765,Leflore County,H1,A
+MS,28,85,695766,Lincoln County,H1,A
+MS,28,87,695767,Lowndes County,H1,A
+MS,28,89,695768,Madison County,H1,A
+MS,28,91,695769,Marion County,H1,A
+MS,28,93,695770,Marshall County,H1,A
+MS,28,95,695771,Monroe County,H1,A
+MS,28,97,711757,Montgomery County,H1,A
+MS,28,99,695772,Neshoba County,H1,A
+MS,28,101,695773,Newton County,H1,A
+MS,28,103,695774,Noxubee County,H1,A
+MS,28,105,695775,Oktibbeha County,H1,A
+MS,28,107,695776,Panola County,H1,A
+MS,28,109,695777,Pearl River County,H1,A
+MS,28,111,695778,Perry County,H1,A
+MS,28,113,711758,Pike County,H1,A
+MS,28,115,695803,Pontotoc County,H1,A
+MS,28,117,695779,Prentiss County,H1,A
+MS,28,119,695780,Quitman County,H1,A
+MS,28,121,695781,Rankin County,H1,A
+MS,28,123,695782,Scott County,H1,A
+MS,28,125,695783,Sharkey County,H1,A
+MS,28,127,695784,Simpson County,H1,A
+MS,28,129,695785,Smith County,H1,A
+MS,28,131,695786,Stone County,H1,A
+MS,28,133,695787,Sunflower County,H1,A
+MS,28,135,695788,Tallahatchie County,H1,A
+MS,28,137,695789,Tate County,H1,A
+MS,28,139,695790,Tippah County,H1,A
+MS,28,141,695791,Tishomingo County,H1,A
+MS,28,143,695792,Tunica County,H1,A
+MS,28,145,695793,Union County,H1,A
+MS,28,147,695794,Walthall County,H1,A
+MS,28,149,695795,Warren County,H1,A
+MS,28,151,695796,Washington County,H1,A
+MS,28,153,695797,Wayne County,H1,A
+MS,28,155,695798,Webster County,H1,A
+MS,28,157,695799,Wilkinson County,H1,A
+MS,28,159,695800,Winston County,H1,A
+MS,28,161,695801,Yalobusha County,H1,A
+MS,28,163,695802,Yazoo County,H1,A
+MO,29,1,765805,Adair County,H1,A
+MO,29,3,758456,Andrew County,H1,A
+MO,29,5,758457,Atchison County,H1,A
+MO,29,7,758458,Audrain County,H1,A
+MO,29,9,758459,Barry County,H1,A
+MO,29,11,758460,Barton County,H1,A
+MO,29,13,758461,Bates County,H1,A
+MO,29,15,758462,Benton County,H1,A
+MO,29,17,758463,Bollinger County,H1,A
+MO,29,19,758464,Boone County,H1,A
+MO,29,21,758465,Buchanan County,H1,A
+MO,29,23,758466,Butler County,H1,A
+MO,29,25,758467,Caldwell County,H1,A
+MO,29,27,758468,Callaway County,H1,A
+MO,29,29,758469,Camden County,H1,A
+MO,29,31,758470,Cape Girardeau County,H1,A
+MO,29,33,758471,Carroll County,H1,A
+MO,29,35,758472,Carter County,H1,A
+MO,29,37,758473,Cass County,H1,A
+MO,29,39,758474,Cedar County,H1,A
+MO,29,41,758475,Chariton County,H1,A
+MO,29,43,758476,Christian County,H1,A
+MO,29,45,758477,Clark County,H1,A
+MO,29,47,758478,Clay County,H1,A
+MO,29,49,758479,Clinton County,H1,A
+MO,29,51,758480,Cole County,H1,A
+MO,29,53,758481,Cooper County,H1,A
+MO,29,55,758482,Crawford County,H1,A
+MO,29,57,758483,Dade County,H1,A
+MO,29,59,758484,Dallas County,H1,A
+MO,29,61,758485,Daviess County,H1,A
+MO,29,63,758486,DeKalb County,H1,A
+MO,29,65,758487,Dent County,H1,A
+MO,29,67,758488,Douglas County,H1,A
+MO,29,69,758489,Dunklin County,H1,A
+MO,29,71,758490,Franklin County,H1,A
+MO,29,73,758491,Gasconade County,H1,A
+MO,29,75,758492,Gentry County,H1,A
+MO,29,77,758493,Greene County,H1,A
+MO,29,79,758494,Grundy County,H1,A
+MO,29,81,758495,Harrison County,H1,A
+MO,29,83,758496,Henry County,H1,A
+MO,29,85,758497,Hickory County,H1,A
+MO,29,87,758498,Holt County,H1,A
+MO,29,89,758499,Howard County,H1,A
+MO,29,91,758500,Howell County,H1,A
+MO,29,93,758501,Iron County,H1,A
+MO,29,95,758502,Jackson County,H1,A
+MO,29,97,758503,Jasper County,H1,A
+MO,29,99,758504,Jefferson County,H1,A
+MO,29,101,758505,Johnson County,H1,A
+MO,29,103,758506,Knox County,H1,A
+MO,29,105,758507,Laclede County,H1,A
+MO,29,107,758508,Lafayette County,H1,A
+MO,29,109,758509,Lawrence County,H1,A
+MO,29,111,758510,Lewis County,H1,A
+MO,29,113,758511,Lincoln County,H1,A
+MO,29,115,758512,Linn County,H1,A
+MO,29,117,758513,Livingston County,H1,A
+MO,29,119,758518,McDonald County,H1,A
+MO,29,121,758514,Macon County,H1,A
+MO,29,123,758515,Madison County,H1,A
+MO,29,125,758516,Maries County,H1,A
+MO,29,127,758517,Marion County,H1,A
+MO,29,129,758519,Mercer County,H1,A
+MO,29,131,758520,Miller County,H1,A
+MO,29,133,758521,Mississippi County,H1,A
+MO,29,135,758522,Moniteau County,H1,A
+MO,29,137,758523,Monroe County,H1,A
+MO,29,139,758524,Montgomery County,H1,A
+MO,29,141,758525,Morgan County,H1,A
+MO,29,143,758526,New Madrid County,H1,A
+MO,29,145,758527,Newton County,H1,A
+MO,29,147,758528,Nodaway County,H1,A
+MO,29,149,758529,Oregon County,H1,A
+MO,29,151,758530,Osage County,H1,A
+MO,29,153,758531,Ozark County,H1,A
+MO,29,155,758532,Pemiscot County,H1,A
+MO,29,157,758533,Perry County,H1,A
+MO,29,159,758534,Pettis County,H1,A
+MO,29,161,758535,Phelps County,H1,A
+MO,29,163,758536,Pike County,H1,A
+MO,29,165,758537,Platte County,H1,A
+MO,29,167,758538,Polk County,H1,A
+MO,29,169,758539,Pulaski County,H1,A
+MO,29,171,758540,Putnam County,H1,A
+MO,29,173,758541,Ralls County,H1,A
+MO,29,175,758542,Randolph County,H1,A
+MO,29,177,758543,Ray County,H1,A
+MO,29,179,758544,Reynolds County,H1,A
+MO,29,181,758545,Ripley County,H1,A
+MO,29,183,758546,St. Charles County,H1,A
+MO,29,185,758547,St. Clair County,H1,A
+MO,29,186,765806,Ste. Genevieve County,H1,A
+MO,29,187,758548,St. Francois County,H1,A
+MO,29,189,758549,St. Louis County,H1,A
+MO,29,195,758550,Saline County,H1,A
+MO,29,197,758551,Schuyler County,H1,A
+MO,29,199,758552,Scotland County,H1,A
+MO,29,201,758553,Scott County,H1,A
+MO,29,203,758554,Shannon County,H1,A
+MO,29,205,758555,Shelby County,H1,A
+MO,29,207,758556,Stoddard County,H1,A
+MO,29,209,758557,Stone County,H1,A
+MO,29,211,758558,Sullivan County,H1,A
+MO,29,213,758559,Taney County,H1,A
+MO,29,215,758560,Texas County,H1,A
+MO,29,217,758561,Vernon County,H1,A
+MO,29,219,758562,Warren County,H1,A
+MO,29,221,758563,Washington County,H1,A
+MO,29,223,758564,Wayne County,H1,A
+MO,29,225,758565,Webster County,H1,A
+MO,29,227,758566,Worth County,H1,A
+MO,29,229,758567,Wright County,H1,A
+MO,29,510,767557,St. Louis city,C7,F
+MT,30,1,1719627,Beaverhead County,H1,A
+MT,30,3,1716521,Big Horn County,H1,A
+MT,30,5,1722470,Blaine County,H1,A
+MT,30,7,1719625,Broadwater County,H1,A
+MT,30,9,1720111,Carbon County,H1,A
+MT,30,11,1688746,Carter County,H1,A
+MT,30,13,1719622,Cascade County,H1,A
+MT,30,15,1719619,Chouteau County,H1,A
+MT,30,17,1720109,Custer County,H1,A
+MT,30,19,1637566,Daniels County,H1,A
+MT,30,21,1715632,Dawson County,H1,A
+MT,30,23,1719617,Deer Lodge County,H6,C
+MT,30,25,1688745,Fallon County,H1,A
+MT,30,27,1719614,Fergus County,H1,A
+MT,30,29,1719611,Flathead County,H1,A
+MT,30,31,1720053,Gallatin County,H1,A
+MT,30,33,1716058,Garfield County,H1,A
+MT,30,35,1719607,Glacier County,H1,A
+MT,30,37,1723078,Golden Valley County,H1,A
+MT,30,39,1719605,Granite County,H1,A
+MT,30,41,1720048,Hill County,H1,A
+MT,30,43,1720045,Jefferson County,H1,A
+MT,30,45,1719602,Judith Basin County,H1,A
+MT,30,47,1796943,Lake County,H1,A
+MT,30,49,793926,Lewis and Clark County,H1,A
+MT,30,51,1720036,Liberty County,H1,A
+MT,30,53,1720038,Lincoln County,H1,A
+MT,30,55,1716270,McCone County,H1,A
+MT,30,57,1720033,Madison County,H1,A
+MT,30,59,1719600,Meagher County,H1,A
+MT,30,61,1720030,Mineral County,H1,A
+MT,30,63,1719596,Missoula County,H1,A
+MT,30,65,1719593,Musselshell County,H1,A
+MT,30,67,1720027,Park County,H1,A
+MT,30,69,1719590,Petroleum County,H1,A
+MT,30,71,1720023,Phillips County,H1,A
+MT,30,73,1719587,Pondera County,H1,A
+MT,30,75,1719584,Powder River County,H1,A
+MT,30,77,1720021,Powell County,H1,A
+MT,30,79,1715847,Prairie County,H1,A
+MT,30,81,1719582,Ravalli County,H1,A
+MT,30,83,1715338,Richland County,H1,A
+MT,30,85,1669428,Roosevelt County,H1,A
+MT,30,87,1716795,Rosebud County,H1,A
+MT,30,89,1719578,Sanders County,H1,A
+MT,30,91,1677577,Sheridan County,H1,A
+MT,30,93,1719575,Silver Bow County,H6,C
+MT,30,95,1719572,Stillwater County,H1,A
+MT,30,97,1719569,Sweet Grass County,H1,A
+MT,30,99,1720018,Teton County,H1,A
+MT,30,101,1719566,Toole County,H1,A
+MT,30,103,1719563,Treasure County,H1,A
+MT,30,105,1678923,Valley County,H1,A
+MT,30,107,1719560,Wheatland County,H1,A
+MT,30,109,1696629,Wibaux County,H1,A
+MT,30,111,1719558,Yellowstone County,H1,A
+NE,31,1,835823,Adams County,H1,A
+NE,31,3,835824,Antelope County,H1,A
+NE,31,5,835825,Arthur County,H1,A
+NE,31,7,835826,Banner County,H1,A
+NE,31,9,835827,Blaine County,H1,A
+NE,31,11,835828,Boone County,H1,A
+NE,31,13,835991,Box Butte County,H1,A
+NE,31,15,835829,Boyd County,H1,A
+NE,31,17,835830,Brown County,H1,A
+NE,31,19,835831,Buffalo County,H1,A
+NE,31,21,835832,Burt County,H1,A
+NE,31,23,835833,Butler County,H1,A
+NE,31,25,835834,Cass County,H1,A
+NE,31,27,835835,Cedar County,H1,A
+NE,31,29,835836,Chase County,H1,A
+NE,31,31,835837,Cherry County,H1,A
+NE,31,33,835838,Cheyenne County,H1,A
+NE,31,35,835839,Clay County,H1,A
+NE,31,37,835840,Colfax County,H1,A
+NE,31,39,835841,Cuming County,H1,A
+NE,31,41,835842,Custer County,H1,A
+NE,31,43,835843,Dakota County,H1,A
+NE,31,45,835844,Dawes County,H1,A
+NE,31,47,835845,Dawson County,H1,A
+NE,31,49,835846,Deuel County,H1,A
+NE,31,51,835847,Dixon County,H1,A
+NE,31,53,835848,Dodge County,H1,A
+NE,31,55,835849,Douglas County,H1,A
+NE,31,57,835850,Dundy County,H1,A
+NE,31,59,835851,Fillmore County,H1,A
+NE,31,61,835852,Franklin County,H1,A
+NE,31,63,835853,Frontier County,H1,A
+NE,31,65,835854,Furnas County,H1,A
+NE,31,67,835855,Gage County,H1,A
+NE,31,69,835856,Garden County,H1,A
+NE,31,71,835857,Garfield County,H1,A
+NE,31,73,835858,Gosper County,H1,A
+NE,31,75,835859,Grant County,H1,A
+NE,31,77,835860,Greeley County,H1,A
+NE,31,79,835861,Hall County,H1,A
+NE,31,81,835862,Hamilton County,H1,A
+NE,31,83,835863,Harlan County,H1,A
+NE,31,85,835864,Hayes County,H1,A
+NE,31,87,835865,Hitchcock County,H1,A
+NE,31,89,835866,Holt County,H1,A
+NE,31,91,835867,Hooker County,H1,A
+NE,31,93,835868,Howard County,H1,A
+NE,31,95,835869,Jefferson County,H1,A
+NE,31,97,835870,Johnson County,H1,A
+NE,31,99,835871,Kearney County,H1,A
+NE,31,101,835872,Keith County,H1,A
+NE,31,103,835873,Keya Paha County,H1,A
+NE,31,105,835874,Kimball County,H1,A
+NE,31,107,835875,Knox County,H1,A
+NE,31,109,835876,Lancaster County,H1,A
+NE,31,111,835877,Lincoln County,H1,A
+NE,31,113,835878,Logan County,H1,A
+NE,31,115,835879,Loup County,H1,A
+NE,31,117,835881,McPherson County,H1,A
+NE,31,119,835880,Madison County,H1,A
+NE,31,121,835882,Merrick County,H1,A
+NE,31,123,835883,Morrill County,H1,A
+NE,31,125,835884,Nance County,H1,A
+NE,31,127,835885,Nemaha County,H1,A
+NE,31,129,835886,Nuckolls County,H1,A
+NE,31,131,835887,Otoe County,H1,A
+NE,31,133,835888,Pawnee County,H1,A
+NE,31,135,835889,Perkins County,H1,A
+NE,31,137,835890,Phelps County,H1,A
+NE,31,139,835891,Pierce County,H1,A
+NE,31,141,835892,Platte County,H1,A
+NE,31,143,835893,Polk County,H1,A
+NE,31,145,835894,Red Willow County,H1,A
+NE,31,147,835895,Richardson County,H1,A
+NE,31,149,835896,Rock County,H1,A
+NE,31,151,835897,Saline County,H1,A
+NE,31,153,835898,Sarpy County,H1,A
+NE,31,155,835899,Saunders County,H1,A
+NE,31,157,835900,Scotts Bluff County,H1,A
+NE,31,159,835901,Seward County,H1,A
+NE,31,161,835902,Sheridan County,H1,A
+NE,31,163,835903,Sherman County,H1,A
+NE,31,165,835904,Sioux County,H1,A
+NE,31,167,835905,Stanton County,H1,A
+NE,31,169,835906,Thayer County,H1,A
+NE,31,171,835907,Thomas County,H1,A
+NE,31,173,835908,Thurston County,H1,A
+NE,31,175,835909,Valley County,H1,A
+NE,31,177,835910,Washington County,H1,A
+NE,31,179,835911,Wayne County,H1,A
+NE,31,181,835912,Webster County,H1,A
+NE,31,183,835913,Wheeler County,H1,A
+NE,31,185,835914,York County,H1,A
+NV,32,1,858638,Churchill County,H1,A
+NV,32,3,863600,Clark County,H1,A
+NV,32,5,858643,Douglas County,H1,A
+NV,32,7,857664,Elko County,H1,A
+NV,32,9,858641,Esmeralda County,H1,A
+NV,32,11,858640,Eureka County,H1,A
+NV,32,13,863310,Humboldt County,H1,A
+NV,32,15,863368,Lander County,H1,A
+NV,32,17,863992,Lincoln County,H1,A
+NV,32,19,858645,Lyon County,H1,A
+NV,32,21,858644,Mineral County,H1,A
+NV,32,23,863599,Nye County,H1,A
+NV,32,27,858646,Pershing County,H1,A
+NV,32,29,858642,Storey County,H1,A
+NV,32,31,858608,Washoe County,H1,A
+NV,32,33,858615,White Pine County,H1,A
+NV,32,510,863219,Carson City,C7,F
+NH,33,1,873174,Belknap County,H1,A
+NH,33,3,873175,Carroll County,H1,A
+NH,33,5,873176,Cheshire County,H1,A
+NH,33,7,873177,Coos County,H1,A
+NH,33,9,873178,Grafton County,H1,A
+NH,33,11,873179,Hillsborough County,H1,A
+NH,33,13,873180,Merrimack County,H1,A
+NH,33,15,873181,Rockingham County,H1,A
+NH,33,17,873182,Strafford County,H1,A
+NH,33,19,873183,Sullivan County,H1,A
+NJ,34,1,882270,Atlantic County,H1,A
+NJ,34,3,882271,Bergen County,H1,A
+NJ,34,5,882272,Burlington County,H1,A
+NJ,34,7,882273,Camden County,H1,A
+NJ,34,9,882274,Cape May County,H1,A
+NJ,34,11,882275,Cumberland County,H1,A
+NJ,34,13,882276,Essex County,H1,A
+NJ,34,15,882277,Gloucester County,H1,A
+NJ,34,17,882278,Hudson County,H1,A
+NJ,34,19,882228,Hunterdon County,H1,A
+NJ,34,21,882229,Mercer County,H1,A
+NJ,34,23,882230,Middlesex County,H1,A
+NJ,34,25,882910,Monmouth County,H1,A
+NJ,34,27,882231,Morris County,H1,A
+NJ,34,29,882279,Ocean County,H1,A
+NJ,34,31,882232,Passaic County,H1,A
+NJ,34,33,882233,Salem County,H1,A
+NJ,34,35,882234,Somerset County,H1,A
+NJ,34,37,882236,Sussex County,H1,A
+NJ,34,39,882235,Union County,H1,A
+NJ,34,41,882237,Warren County,H1,A
+NM,35,1,1702363,Bernalillo County,H1,A
+NM,35,3,929108,Catron County,H1,A
+NM,35,5,1702364,Chaves County,H1,A
+NM,35,6,933051,Cibola County,H1,A
+NM,35,7,929117,Colfax County,H1,A
+NM,35,9,933053,Curry County,H1,A
+NM,35,11,933054,De Baca County,H1,A
+NM,35,13,929109,Doña Ana County,H1,A
+NM,35,15,936829,Eddy County,H1,A
+NM,35,17,915980,Grant County,H1,A
+NM,35,19,929111,Guadalupe County,H1,A
+NM,35,21,933055,Harding County,H1,A
+NM,35,23,929106,Hidalgo County,H1,A
+NM,35,25,1702365,Lea County,H1,A
+NM,35,27,929116,Lincoln County,H1,A
+NM,35,28,1702366,Los Alamos County,H1,A
+NM,35,29,933057,Luna County,H1,A
+NM,35,31,929107,McKinley County,H1,A
+NM,35,33,1702367,Mora County,H1,A
+NM,35,35,929104,Otero County,H1,A
+NM,35,37,929110,Quay County,H1,A
+NM,35,39,1702368,Rio Arriba County,H1,A
+NM,35,41,1702369,Roosevelt County,H1,A
+NM,35,43,929113,Sandoval County,H1,A
+NM,35,45,936844,San Juan County,H1,A
+NM,35,47,929114,San Miguel County,H1,A
+NM,35,49,933322,Santa Fe County,H1,A
+NM,35,51,1702370,Sierra County,H1,A
+NM,35,53,1702371,Socorro County,H1,A
+NM,35,55,933056,Taos County,H1,A
+NM,35,57,929112,Torrance County,H1,A
+NM,35,59,929115,Union County,H1,A
+NM,35,61,933052,Valencia County,H1,A
+NY,36,1,974099,Albany County,H1,A
+NY,36,3,974100,Allegany County,H1,A
+NY,36,5,974101,Bronx County,H6,C
+NY,36,7,974102,Broome County,H1,A
+NY,36,9,974103,Cattaraugus County,H1,A
+NY,36,11,974104,Cayuga County,H1,A
+NY,36,13,974105,Chautauqua County,H1,A
+NY,36,15,974106,Chemung County,H1,A
+NY,36,17,974107,Chenango County,H1,A
+NY,36,19,974108,Clinton County,H1,A
+NY,36,21,974109,Columbia County,H1,A
+NY,36,23,974110,Cortland County,H1,A
+NY,36,25,974111,Delaware County,H1,A
+NY,36,27,974112,Dutchess County,H1,A
+NY,36,29,974113,Erie County,H1,A
+NY,36,31,974114,Essex County,H1,A
+NY,36,33,974115,Franklin County,H1,A
+NY,36,35,974116,Fulton County,H1,A
+NY,36,37,974117,Genesee County,H1,A
+NY,36,39,974118,Greene County,H1,A
+NY,36,41,974119,Hamilton County,H1,A
+NY,36,43,974120,Herkimer County,H1,A
+NY,36,45,974121,Jefferson County,H1,A
+NY,36,47,974122,Kings County,H6,C
+NY,36,49,974123,Lewis County,H1,A
+NY,36,51,974124,Livingston County,H1,A
+NY,36,53,974125,Madison County,H1,A
+NY,36,55,974126,Monroe County,H1,A
+NY,36,57,974127,Montgomery County,H1,A
+NY,36,59,974128,Nassau County,H1,A
+NY,36,61,974129,New York County,H6,C
+NY,36,63,974130,Niagara County,H1,A
+NY,36,65,974131,Oneida County,H1,A
+NY,36,67,974132,Onondaga County,H1,A
+NY,36,69,974133,Ontario County,H1,A
+NY,36,71,974134,Orange County,H1,A
+NY,36,73,974135,Orleans County,H1,A
+NY,36,75,974136,Oswego County,H1,A
+NY,36,77,974137,Otsego County,H1,A
+NY,36,79,974138,Putnam County,H1,A
+NY,36,81,974139,Queens County,H6,C
+NY,36,83,974140,Rensselaer County,H1,A
+NY,36,85,974141,Richmond County,H6,C
+NY,36,87,974142,Rockland County,H1,A
+NY,36,89,977309,St. Lawrence County,H1,A
+NY,36,91,974143,Saratoga County,H1,A
+NY,36,93,974144,Schenectady County,H1,A
+NY,36,95,974145,Schoharie County,H1,A
+NY,36,97,974146,Schuyler County,H1,A
+NY,36,99,974147,Seneca County,H1,A
+NY,36,101,974148,Steuben County,H1,A
+NY,36,103,974149,Suffolk County,H1,A
+NY,36,105,974150,Sullivan County,H1,A
+NY,36,107,974151,Tioga County,H1,A
+NY,36,109,974152,Tompkins County,H1,A
+NY,36,111,974153,Ulster County,H1,A
+NY,36,113,974154,Warren County,H1,A
+NY,36,115,974155,Washington County,H1,A
+NY,36,117,974156,Wayne County,H1,A
+NY,36,119,974157,Westchester County,H1,A
+NY,36,121,974158,Wyoming County,H1,A
+NY,36,123,974159,Yates County,H1,A
+NC,37,1,1008531,Alamance County,H1,A
+NC,37,3,1008532,Alexander County,H1,A
+NC,37,5,1008533,Alleghany County,H1,A
+NC,37,7,1008534,Anson County,H1,A
+NC,37,9,1008535,Ashe County,H1,A
+NC,37,11,1008536,Avery County,H1,A
+NC,37,13,1026333,Beaufort County,H1,A
+NC,37,15,1026334,Bertie County,H1,A
+NC,37,17,1026336,Bladen County,H1,A
+NC,37,19,1026340,Brunswick County,H1,A
+NC,37,21,1008538,Buncombe County,H1,A
+NC,37,23,1008539,Burke County,H1,A
+NC,37,25,1008540,Cabarrus County,H1,A
+NC,37,27,1008541,Caldwell County,H1,A
+NC,37,29,1026335,Camden County,H1,A
+NC,37,31,1026337,Carteret County,H1,A
+NC,37,33,1008542,Caswell County,H1,A
+NC,37,35,1008543,Catawba County,H1,A
+NC,37,37,1008544,Chatham County,H1,A
+NC,37,39,1008545,Cherokee County,H1,A
+NC,37,41,1025822,Chowan County,H1,A
+NC,37,43,1008546,Clay County,H1,A
+NC,37,45,1008547,Cleveland County,H1,A
+NC,37,47,1026339,Columbus County,H1,A
+NC,37,49,1026123,Craven County,H1,A
+NC,37,51,1026124,Cumberland County,H1,A
+NC,37,53,1026125,Currituck County,H1,A
+NC,37,55,1026133,Dare County,H1,A
+NC,37,57,1008548,Davidson County,H1,A
+NC,37,59,1008549,Davie County,H1,A
+NC,37,61,1026132,Duplin County,H1,A
+NC,37,63,1008550,Durham County,H1,A
+NC,37,65,1008551,Edgecombe County,H1,A
+NC,37,67,1008552,Forsyth County,H1,A
+NC,37,69,1008553,Franklin County,H1,A
+NC,37,71,1008554,Gaston County,H1,A
+NC,37,73,1026126,Gates County,H1,A
+NC,37,75,1008555,Graham County,H1,A
+NC,37,77,1008556,Granville County,H1,A
+NC,37,79,1008557,Greene County,H1,A
+NC,37,81,1008558,Guilford County,H1,A
+NC,37,83,1008559,Halifax County,H1,A
+NC,37,85,1008560,Harnett County,H1,A
+NC,37,87,1008561,Haywood County,H1,A
+NC,37,89,1008562,Henderson County,H1,A
+NC,37,91,1026127,Hertford County,H1,A
+NC,37,93,1008563,Hoke County,H1,A
+NC,37,95,1008564,Hyde County,H1,A
+NC,37,97,1008565,Iredell County,H1,A
+NC,37,99,1008566,Jackson County,H1,A
+NC,37,101,1026327,Johnston County,H1,A
+NC,37,103,1026338,Jones County,H1,A
+NC,37,105,1008567,Lee County,H1,A
+NC,37,107,1008568,Lenoir County,H1,A
+NC,37,109,1008569,Lincoln County,H1,A
+NC,37,111,1025836,McDowell County,H1,A
+NC,37,113,1025833,Macon County,H1,A
+NC,37,115,1025834,Madison County,H1,A
+NC,37,117,1026328,Martin County,H1,A
+NC,37,119,1008570,Mecklenburg County,H1,A
+NC,37,121,1008571,Mitchell County,H1,A
+NC,37,123,1008572,Montgomery County,H1,A
+NC,37,125,1008573,Moore County,H1,A
+NC,37,127,1008574,Nash County,H1,A
+NC,37,129,1026329,New Hanover County,H1,A
+NC,37,131,1008575,Northampton County,H1,A
+NC,37,133,1026341,Onslow County,H1,A
+NC,37,135,1008576,Orange County,H1,A
+NC,37,137,1023355,Pamlico County,H1,A
+NC,37,139,1026330,Pasquotank County,H1,A
+NC,37,141,1026129,Pender County,H1,A
+NC,37,143,1025841,Perquimans County,H1,A
+NC,37,145,1008577,Person County,H1,A
+NC,37,147,1008578,Pitt County,H1,A
+NC,37,149,1008579,Polk County,H1,A
+NC,37,151,1008580,Randolph County,H1,A
+NC,37,153,1008581,Richmond County,H1,A
+NC,37,155,1026130,Robeson County,H1,A
+NC,37,157,1008582,Rockingham County,H1,A
+NC,37,159,1008583,Rowan County,H1,A
+NC,37,161,1008584,Rutherford County,H1,A
+NC,37,163,1026131,Sampson County,H1,A
+NC,37,165,1008585,Scotland County,H1,A
+NC,37,167,1025844,Stanly County,H1,A
+NC,37,169,1008586,Stokes County,H1,A
+NC,37,171,1008587,Surry County,H1,A
+NC,37,173,1008588,Swain County,H1,A
+NC,37,175,1008589,Transylvania County,H1,A
+NC,37,177,1026332,Tyrrell County,H1,A
+NC,37,179,1008590,Union County,H1,A
+NC,37,181,1008591,Vance County,H1,A
+NC,37,183,1008592,Wake County,H1,A
+NC,37,185,1008593,Warren County,H1,A
+NC,37,187,1026331,Washington County,H1,A
+NC,37,189,1008594,Watauga County,H1,A
+NC,37,191,1026128,Wayne County,H1,A
+NC,37,193,1008595,Wilkes County,H1,A
+NC,37,195,1008596,Wilson County,H1,A
+NC,37,197,1008597,Yadkin County,H1,A
+NC,37,199,1008598,Yancey County,H1,A
+ND,38,1,1034210,Adams County,H1,A
+ND,38,3,1034225,Barnes County,H1,A
+ND,38,5,1034216,Benson County,H1,A
+ND,38,7,1035616,Billings County,H1,A
+ND,38,9,1034227,Bottineau County,H1,A
+ND,38,11,1035619,Bowman County,H1,A
+ND,38,13,1034235,Burke County,H1,A
+ND,38,15,1035614,Burleigh County,H1,A
+ND,38,17,1034226,Cass County,H1,A
+ND,38,19,1035304,Cavalier County,H1,A
+ND,38,21,1035622,Dickey County,H1,A
+ND,38,23,1034234,Divide County,H1,A
+ND,38,25,1035623,Dunn County,H1,A
+ND,38,27,1034222,Eddy County,H1,A
+ND,38,29,1035615,Emmons County,H1,A
+ND,38,31,1034223,Foster County,H1,A
+ND,38,33,1035617,Golden Valley County,H1,A
+ND,38,35,1034218,Grand Forks County,H1,A
+ND,38,37,1035300,Grant County,H1,A
+ND,38,39,1034219,Griggs County,H1,A
+ND,38,41,1034209,Hettinger County,H1,A
+ND,38,43,1034206,Kidder County,H1,A
+ND,38,45,1035621,LaMoure County,H1,A
+ND,38,47,1035620,Logan County,H1,A
+ND,38,49,1035305,McHenry County,H1,A
+ND,38,51,1034211,McIntosh County,H1,A
+ND,38,53,1035299,McKenzie County,H1,A
+ND,38,55,1034232,McLean County,H1,A
+ND,38,57,1034233,Mercer County,H1,A
+ND,38,59,1034207,Morton County,H1,A
+ND,38,61,1035307,Mountrail County,H1,A
+ND,38,63,1034217,Nelson County,H1,A
+ND,38,65,1034205,Oliver County,H1,A
+ND,38,67,1034212,Pembina County,H1,A
+ND,38,69,1034230,Pierce County,H1,A
+ND,38,71,1034215,Ramsey County,H1,A
+ND,38,73,1035301,Ransom County,H1,A
+ND,38,75,1034229,Renville County,H1,A
+ND,38,77,1035302,Richland County,H1,A
+ND,38,79,1034228,Rolette County,H1,A
+ND,38,81,1035303,Sargent County,H1,A
+ND,38,83,1034203,Sheridan County,H1,A
+ND,38,85,1034208,Sioux County,H1,A
+ND,38,87,1035896,Slope County,H1,A
+ND,38,89,1035618,Stark County,H1,A
+ND,38,91,1034220,Steele County,H1,A
+ND,38,93,1034224,Stutsman County,H1,A
+ND,38,95,1034213,Towner County,H1,A
+ND,38,97,1034221,Traill County,H1,A
+ND,38,99,1034214,Walsh County,H1,A
+ND,38,101,1034231,Ward County,H1,A
+ND,38,103,1034204,Wells County,H1,A
+ND,38,105,1035306,Williams County,H1,A
+OH,39,1,1074014,Adams County,H1,A
+OH,39,3,1074015,Allen County,H1,A
+OH,39,5,1074016,Ashland County,H1,A
+OH,39,7,1074017,Ashtabula County,H1,A
+OH,39,9,1074018,Athens County,H1,A
+OH,39,11,1074019,Auglaize County,H1,A
+OH,39,13,1065575,Belmont County,H1,A
+OH,39,15,1074020,Brown County,H1,A
+OH,39,17,1074021,Butler County,H1,A
+OH,39,19,1074022,Carroll County,H1,A
+OH,39,21,1074023,Champaign County,H1,A
+OH,39,23,1074024,Clark County,H1,A
+OH,39,25,1074025,Clermont County,H1,A
+OH,39,27,1074026,Clinton County,H1,A
+OH,39,29,1074027,Columbiana County,H1,A
+OH,39,31,1074028,Coshocton County,H1,A
+OH,39,33,1074029,Crawford County,H1,A
+OH,39,35,1074030,Cuyahoga County,H1,A
+OH,39,37,1074031,Darke County,H1,A
+OH,39,39,1074032,Defiance County,H1,A
+OH,39,41,1074033,Delaware County,H1,A
+OH,39,43,1074034,Erie County,H1,A
+OH,39,45,1074035,Fairfield County,H1,A
+OH,39,47,1074036,Fayette County,H1,A
+OH,39,49,1074037,Franklin County,H1,A
+OH,39,51,1074038,Fulton County,H1,A
+OH,39,53,1074039,Gallia County,H1,A
+OH,39,55,1074040,Geauga County,H1,A
+OH,39,57,1074041,Greene County,H1,A
+OH,39,59,1074042,Guernsey County,H1,A
+OH,39,61,1074043,Hamilton County,H1,A
+OH,39,63,1074044,Hancock County,H1,A
+OH,39,65,1074045,Hardin County,H1,A
+OH,39,67,1074046,Harrison County,H1,A
+OH,39,69,1074047,Henry County,H1,A
+OH,39,71,1074048,Highland County,H1,A
+OH,39,73,1074049,Hocking County,H1,A
+OH,39,75,1074050,Holmes County,H1,A
+OH,39,77,1074051,Huron County,H1,A
+OH,39,79,1074052,Jackson County,H1,A
+OH,39,81,1074053,Jefferson County,H1,A
+OH,39,83,1074054,Knox County,H1,A
+OH,39,85,1074055,Lake County,H1,A
+OH,39,87,1074056,Lawrence County,H1,A
+OH,39,89,1074057,Licking County,H1,A
+OH,39,91,1074058,Logan County,H1,A
+OH,39,93,1074059,Lorain County,H1,A
+OH,39,95,1074060,Lucas County,H1,A
+OH,39,97,1074061,Madison County,H1,A
+OH,39,99,1074062,Mahoning County,H1,A
+OH,39,101,1074063,Marion County,H1,A
+OH,39,103,1074064,Medina County,H1,A
+OH,39,105,1074065,Meigs County,H1,A
+OH,39,107,1074066,Mercer County,H1,A
+OH,39,109,1074067,Miami County,H1,A
+OH,39,111,1074068,Monroe County,H1,A
+OH,39,113,1074069,Montgomery County,H1,A
+OH,39,115,1074070,Morgan County,H1,A
+OH,39,117,1074071,Morrow County,H1,A
+OH,39,119,1074072,Muskingum County,H1,A
+OH,39,121,1074073,Noble County,H1,A
+OH,39,123,1074074,Ottawa County,H1,A
+OH,39,125,1074075,Paulding County,H1,A
+OH,39,127,1074076,Perry County,H1,A
+OH,39,129,1074077,Pickaway County,H1,A
+OH,39,131,1074078,Pike County,H1,A
+OH,39,133,1074079,Portage County,H1,A
+OH,39,135,1074080,Preble County,H1,A
+OH,39,137,1074081,Putnam County,H1,A
+OH,39,139,1074082,Richland County,H1,A
+OH,39,141,1074083,Ross County,H1,A
+OH,39,143,1074084,Sandusky County,H1,A
+OH,39,145,1074085,Scioto County,H1,A
+OH,39,147,1074086,Seneca County,H1,A
+OH,39,149,1074087,Shelby County,H1,A
+OH,39,151,1065576,Stark County,H1,A
+OH,39,153,1074088,Summit County,H1,A
+OH,39,155,1074089,Trumbull County,H1,A
+OH,39,157,1074090,Tuscarawas County,H1,A
+OH,39,159,1074091,Union County,H1,A
+OH,39,161,1074092,Van Wert County,H1,A
+OH,39,163,1074093,Vinton County,H1,A
+OH,39,165,1074094,Warren County,H1,A
+OH,39,167,1074095,Washington County,H1,A
+OH,39,169,1074096,Wayne County,H1,A
+OH,39,171,1074097,Williams County,H1,A
+OH,39,173,1074098,Wood County,H1,A
+OH,39,175,1074099,Wyandot County,H1,A
+OK,40,1,1101788,Adair County,H1,A
+OK,40,3,1101789,Alfalfa County,H1,A
+OK,40,5,1101790,Atoka County,H1,A
+OK,40,7,1101791,Beaver County,H1,A
+OK,40,9,1101792,Beckham County,H1,A
+OK,40,11,1101793,Blaine County,H1,A
+OK,40,13,1101794,Bryan County,H1,A
+OK,40,15,1101795,Caddo County,H1,A
+OK,40,17,1101796,Canadian County,H1,A
+OK,40,19,1101797,Carter County,H1,A
+OK,40,21,1101798,Cherokee County,H1,A
+OK,40,23,1101799,Choctaw County,H1,A
+OK,40,25,1101800,Cimarron County,H1,A
+OK,40,27,1101801,Cleveland County,H1,A
+OK,40,29,1101802,Coal County,H1,A
+OK,40,31,1101803,Comanche County,H1,A
+OK,40,33,1101804,Cotton County,H1,A
+OK,40,35,1101805,Craig County,H1,A
+OK,40,37,1101806,Creek County,H1,A
+OK,40,39,1101807,Custer County,H1,A
+OK,40,41,1101808,Delaware County,H1,A
+OK,40,43,1101809,Dewey County,H1,A
+OK,40,45,1101810,Ellis County,H1,A
+OK,40,47,1101811,Garfield County,H1,A
+OK,40,49,1101812,Garvin County,H1,A
+OK,40,51,1101813,Grady County,H1,A
+OK,40,53,1101814,Grant County,H1,A
+OK,40,55,1101815,Greer County,H1,A
+OK,40,57,1101816,Harmon County,H1,A
+OK,40,59,1101817,Harper County,H1,A
+OK,40,61,1101818,Haskell County,H1,A
+OK,40,63,1101819,Hughes County,H1,A
+OK,40,65,1101820,Jackson County,H1,A
+OK,40,67,1101821,Jefferson County,H1,A
+OK,40,69,1101822,Johnston County,H1,A
+OK,40,71,1101823,Kay County,H1,A
+OK,40,73,1101824,Kingfisher County,H1,A
+OK,40,75,1101825,Kiowa County,H1,A
+OK,40,77,1101826,Latimer County,H1,A
+OK,40,79,1101827,Le Flore County,H1,A
+OK,40,81,1101828,Lincoln County,H1,A
+OK,40,83,1101829,Logan County,H1,A
+OK,40,85,1101830,Love County,H1,A
+OK,40,87,1101834,McClain County,H1,A
+OK,40,89,1101835,McCurtain County,H1,A
+OK,40,91,1101836,McIntosh County,H1,A
+OK,40,93,1101831,Major County,H1,A
+OK,40,95,1101832,Marshall County,H1,A
+OK,40,97,1101833,Mayes County,H1,A
+OK,40,99,1101837,Murray County,H1,A
+OK,40,101,1101838,Muskogee County,H1,A
+OK,40,103,1101839,Noble County,H1,A
+OK,40,105,1101840,Nowata County,H1,A
+OK,40,107,1101841,Okfuskee County,H1,A
+OK,40,109,1101842,Oklahoma County,H1,A
+OK,40,111,1101843,Okmulgee County,H1,A
+OK,40,113,1101844,Osage County,H1,A
+OK,40,115,1101845,Ottawa County,H1,A
+OK,40,117,1101846,Pawnee County,H1,A
+OK,40,119,1101847,Payne County,H1,A
+OK,40,121,1101848,Pittsburg County,H1,A
+OK,40,123,1101849,Pontotoc County,H1,A
+OK,40,125,1101850,Pottawatomie County,H1,A
+OK,40,127,1101851,Pushmataha County,H1,A
+OK,40,129,1101852,Roger Mills County,H1,A
+OK,40,131,1101853,Rogers County,H1,A
+OK,40,133,1101854,Seminole County,H1,A
+OK,40,135,1101855,Sequoyah County,H1,A
+OK,40,137,1101856,Stephens County,H1,A
+OK,40,139,1101857,Texas County,H1,A
+OK,40,141,1101858,Tillman County,H1,A
+OK,40,143,1101859,Tulsa County,H1,A
+OK,40,145,1101860,Wagoner County,H1,A
+OK,40,147,1101861,Washington County,H1,A
+OK,40,149,1101862,Washita County,H1,A
+OK,40,151,1101863,Woods County,H1,A
+OK,40,153,1101864,Woodward County,H1,A
+OR,41,1,1135845,Baker County,H1,A
+OR,41,3,1155126,Benton County,H1,A
+OR,41,5,1155127,Clackamas County,H1,A
+OR,41,7,1135846,Clatsop County,H1,A
+OR,41,9,1135847,Columbia County,H1,A
+OR,41,11,1135848,Coos County,H1,A
+OR,41,13,1155128,Crook County,H1,A
+OR,41,15,1155129,Curry County,H1,A
+OR,41,17,1155130,Deschutes County,H1,A
+OR,41,19,1135849,Douglas County,H1,A
+OR,41,21,1135850,Gilliam County,H1,A
+OR,41,23,1135851,Grant County,H1,A
+OR,41,25,1135852,Harney County,H1,A
+OR,41,27,1155131,Hood River County,H1,A
+OR,41,29,1135853,Jackson County,H1,A
+OR,41,31,1155132,Jefferson County,H1,A
+OR,41,33,1155133,Josephine County,H1,A
+OR,41,35,1155134,Klamath County,H1,A
+OR,41,37,1135854,Lake County,H1,A
+OR,41,39,1135855,Lane County,H1,A
+OR,41,41,1135856,Lincoln County,H1,A
+OR,41,43,1135857,Linn County,H1,A
+OR,41,45,1135858,Malheur County,H1,A
+OR,41,47,1135859,Marion County,H1,A
+OR,41,49,1135860,Morrow County,H1,A
+OR,41,51,1135861,Multnomah County,H1,A
+OR,41,53,1135862,Polk County,H1,A
+OR,41,55,1135863,Sherman County,H1,A
+OR,41,57,1135864,Tillamook County,H1,A
+OR,41,59,1156673,Umatilla County,H1,A
+OR,41,61,1164165,Union County,H1,A
+OR,41,63,1155135,Wallowa County,H1,A
+OR,41,65,1155136,Wasco County,H1,A
+OR,41,67,1155137,Washington County,H1,A
+OR,41,69,1135865,Wheeler County,H1,A
+OR,41,71,1135866,Yamhill County,H1,A
+PA,42,1,1213656,Adams County,H1,A
+PA,42,3,1213657,Allegheny County,H1,A
+PA,42,5,1213658,Armstrong County,H1,A
+PA,42,7,1214112,Beaver County,H1,A
+PA,42,9,1209171,Bedford County,H1,A
+PA,42,11,1209172,Berks County,H1,A
+PA,42,13,1213659,Blair County,H1,A
+PA,42,15,1213660,Bradford County,H1,A
+PA,42,17,1209173,Bucks County,H1,A
+PA,42,19,1213661,Butler County,H1,A
+PA,42,21,1213662,Cambria County,H1,A
+PA,42,23,1213663,Cameron County,H1,A
+PA,42,25,1213664,Carbon County,H1,A
+PA,42,27,1214720,Centre County,H1,A
+PA,42,29,1209174,Chester County,H1,A
+PA,42,31,1210234,Clarion County,H1,A
+PA,42,33,1209175,Clearfield County,H1,A
+PA,42,35,1214721,Clinton County,H1,A
+PA,42,37,1213665,Columbia County,H1,A
+PA,42,39,1213666,Crawford County,H1,A
+PA,42,41,1209176,Cumberland County,H1,A
+PA,42,43,1213667,Dauphin County,H1,A
+PA,42,45,1209177,Delaware County,H1,A
+PA,42,47,1213668,Elk County,H1,A
+PA,42,49,1209178,Erie County,H1,A
+PA,42,51,1209179,Fayette County,H1,A
+PA,42,53,1213669,Forest County,H1,A
+PA,42,55,1213670,Franklin County,H1,A
+PA,42,57,1213671,Fulton County,H1,A
+PA,42,59,1214033,Greene County,H1,A
+PA,42,61,1213672,Huntingdon County,H1,A
+PA,42,63,1213673,Indiana County,H1,A
+PA,42,65,1213674,Jefferson County,H1,A
+PA,42,67,1209180,Juniata County,H1,A
+PA,42,69,1213675,Lackawanna County,H1,A
+PA,42,71,1209181,Lancaster County,H1,A
+PA,42,73,1213676,Lawrence County,H1,A
+PA,42,75,1214034,Lebanon County,H1,A
+PA,42,77,1209182,Lehigh County,H1,A
+PA,42,79,1209183,Luzerne County,H1,A
+PA,42,81,1213677,Lycoming County,H1,A
+PA,42,83,1210235,McKean County,H1,A
+PA,42,85,1213678,Mercer County,H1,A
+PA,42,87,1213679,Mifflin County,H1,A
+PA,42,89,1209184,Monroe County,H1,A
+PA,42,91,1213680,Montgomery County,H1,A
+PA,42,93,1213681,Montour County,H1,A
+PA,42,95,1209185,Northampton County,H1,A
+PA,42,97,1209186,Northumberland County,H1,A
+PA,42,99,1213682,Perry County,H1,A
+PA,42,101,1209187,Philadelphia County,H6,C
+PA,42,103,1213683,Pike County,H1,A
+PA,42,105,1213684,Potter County,H1,A
+PA,42,107,1213685,Schuylkill County,H1,A
+PA,42,109,1213686,Snyder County,H1,A
+PA,42,111,1209188,Somerset County,H1,A
+PA,42,113,1213687,Sullivan County,H1,A
+PA,42,115,1213688,Susquehanna County,H1,A
+PA,42,117,1209189,Tioga County,H1,A
+PA,42,119,1213689,Union County,H1,A
+PA,42,121,1213690,Venango County,H1,A
+PA,42,123,1213691,Warren County,H1,A
+PA,42,125,1209190,Washington County,H1,A
+PA,42,127,1213692,Wayne County,H1,A
+PA,42,129,1209191,Westmoreland County,H1,A
+PA,42,131,1209192,Wyoming County,H1,A
+PA,42,133,1209193,York County,H1,A
+RI,44,1,1219777,Bristol County,H4,N
+RI,44,3,1219778,Kent County,H4,N
+RI,44,5,1219779,Newport County,H4,N
+RI,44,7,1219781,Providence County,H4,N
+RI,44,9,1219782,Washington County,H4,N
+SC,45,1,1245666,Abbeville County,H1,A
+SC,45,3,1247979,Aiken County,H1,A
+SC,45,5,1247980,Allendale County,H1,A
+SC,45,7,1247981,Anderson County,H1,A
+SC,45,9,1247982,Bamberg County,H1,A
+SC,45,11,1247983,Barnwell County,H1,A
+SC,45,13,1247984,Beaufort County,H1,A
+SC,45,15,1247985,Berkeley County,H1,A
+SC,45,17,1247986,Calhoun County,H1,A
+SC,45,19,1252740,Charleston County,H1,A
+SC,45,21,1247987,Cherokee County,H1,A
+SC,45,23,1247988,Chester County,H1,A
+SC,45,25,1247989,Chesterfield County,H1,A
+SC,45,27,1247990,Clarendon County,H1,A
+SC,45,29,1247991,Colleton County,H1,A
+SC,45,31,1247992,Darlington County,H1,A
+SC,45,33,1247993,Dillon County,H1,A
+SC,45,35,1247998,Dorchester County,H1,A
+SC,45,37,1247999,Edgefield County,H1,A
+SC,45,39,1248000,Fairfield County,H1,A
+SC,45,41,1248001,Florence County,H1,A
+SC,45,43,1248002,Georgetown County,H1,A
+SC,45,45,1245489,Greenville County,H1,A
+SC,45,47,1248003,Greenwood County,H1,A
+SC,45,49,1248004,Hampton County,H1,A
+SC,45,51,1248005,Horry County,H1,A
+SC,45,53,1248006,Jasper County,H1,A
+SC,45,55,1248007,Kershaw County,H1,A
+SC,45,57,1248008,Lancaster County,H1,A
+SC,45,59,1248009,Laurens County,H1,A
+SC,45,61,1245652,Lee County,H1,A
+SC,45,63,1244251,Lexington County,H1,A
+SC,45,65,1248012,McCormick County,H1,A
+SC,45,67,1248010,Marion County,H1,A
+SC,45,69,1248011,Marlboro County,H1,A
+SC,45,71,1248013,Newberry County,H1,A
+SC,45,73,1244832,Oconee County,H1,A
+SC,45,75,1248014,Orangeburg County,H1,A
+SC,45,77,1248015,Pickens County,H1,A
+SC,45,79,1249299,Richland County,H1,A
+SC,45,81,1248016,Saluda County,H1,A
+SC,45,83,1248592,Spartanburg County,H1,A
+SC,45,85,1244717,Sumter County,H1,A
+SC,45,87,1248017,Union County,H1,A
+SC,45,89,1248018,Williamsburg County,H1,A
+SC,45,91,1248019,York County,H1,A
+SD,46,3,1266983,Aurora County,H1,A
+SD,46,5,1266986,Beadle County,H1,A
+SD,46,7,1265812,Bennett County,H1,A
+SD,46,9,1266997,Bon Homme County,H1,A
+SD,46,11,1265779,Brookings County,H1,A
+SD,46,13,1265785,Brown County,H1,A
+SD,46,15,1265768,Brule County,H1,A
+SD,46,17,1265767,Buffalo County,H1,A
+SD,46,19,1265793,Butte County,H1,A
+SD,46,21,1266974,Campbell County,H1,A
+SD,46,23,1266976,Charles Mix County,H1,A
+SD,46,25,1266985,Clark County,H1,A
+SD,46,27,1265764,Clay County,H1,A
+SD,46,29,1265791,Codington County,H1,A
+SD,46,31,1265811,Corson County,H1,A
+SD,46,33,1265788,Custer County,H1,A
+SD,46,35,1266980,Davison County,H1,A
+SD,46,37,1265790,Day County,H1,A
+SD,46,39,1265781,Deuel County,H1,A
+SD,46,41,1266994,Dewey County,H1,A
+SD,46,43,1266978,Douglas County,H1,A
+SD,46,45,1266972,Edmunds County,H1,A
+SD,46,47,1265787,Fall River County,H1,A
+SD,46,49,1265760,Faulk County,H1,A
+SD,46,51,1265782,Grant County,H1,A
+SD,46,53,1265765,Gregory County,H1,A
+SD,46,55,1266988,Haakon County,H1,A
+SD,46,57,1265766,Hamlin County,H1,A
+SD,46,59,1265783,Hand County,H1,A
+SD,46,61,1266979,Hanson County,H1,A
+SD,46,63,1265792,Harding County,H1,A
+SD,46,65,1265774,Hughes County,H1,A
+SD,46,67,1265771,Hutchinson County,H1,A
+SD,46,69,1266984,Hyde County,H1,A
+SD,46,71,1265794,Jackson County,H1,A
+SD,46,73,1265778,Jerauld County,H1,A
+SD,46,75,1266981,Jones County,H1,A
+SD,46,77,1265780,Kingsbury County,H1,A
+SD,46,79,1265776,Lake County,H1,A
+SD,46,81,1266996,Lawrence County,H1,A
+SD,46,83,1265769,Lincoln County,H1,A
+SD,46,85,1265789,Lyman County,H1,A
+SD,46,87,1265773,McCook County,H1,A
+SD,46,89,1265762,McPherson County,H1,A
+SD,46,91,1265759,Marshall County,H1,A
+SD,46,93,1266991,Meade County,H1,A
+SD,46,95,1267177,Mellette County,H1,A
+SD,46,97,1265777,Miner County,H1,A
+SD,46,99,1265772,Minnehaha County,H1,A
+SD,46,101,1265775,Moody County,H1,A
+SD,46,102,1266992,Oglala Lakota County,H1,A
+SD,46,103,1266995,Pennington County,H1,A
+SD,46,105,1266990,Perkins County,H1,A
+SD,46,107,1265761,Potter County,H1,A
+SD,46,109,1265786,Roberts County,H1,A
+SD,46,111,1266977,Sanborn County,H1,A
+SD,46,115,1266987,Spink County,H1,A
+SD,46,117,1266998,Stanley County,H1,A
+SD,46,119,1266982,Sully County,H1,A
+SD,46,121,1266989,Todd County,H1,A
+SD,46,123,1265784,Tripp County,H1,A
+SD,46,125,1265770,Turner County,H1,A
+SD,46,127,1265763,Union County,H1,A
+SD,46,129,1266973,Walworth County,H1,A
+SD,46,135,1266975,Yankton County,H1,A
+SD,46,137,1266993,Ziebach County,H1,A
+TN,47,1,1639722,Anderson County,H1,A
+TN,47,3,1639723,Bedford County,H1,A
+TN,47,5,1639724,Benton County,H1,A
+TN,47,7,1639725,Bledsoe County,H1,A
+TN,47,9,1639726,Blount County,H1,A
+TN,47,11,1639727,Bradley County,H1,A
+TN,47,13,1639728,Campbell County,H1,A
+TN,47,15,1648572,Cannon County,H1,A
+TN,47,17,1639729,Carroll County,H1,A
+TN,47,19,1639730,Carter County,H1,A
+TN,47,21,1639731,Cheatham County,H1,A
+TN,47,23,1639732,Chester County,H1,A
+TN,47,25,1639733,Claiborne County,H1,A
+TN,47,27,1648573,Clay County,H1,A
+TN,47,29,1639734,Cocke County,H1,A
+TN,47,31,1639735,Coffee County,H1,A
+TN,47,33,1648574,Crockett County,H1,A
+TN,47,35,1639736,Cumberland County,H1,A
+TN,47,37,1639737,Davidson County,H6,C
+TN,47,39,1639739,Decatur County,H1,A
+TN,47,41,1639738,DeKalb County,H1,A
+TN,47,43,1639740,Dickson County,H1,A
+TN,47,45,1639741,Dyer County,H1,A
+TN,47,47,1639742,Fayette County,H1,A
+TN,47,49,1639743,Fentress County,H1,A
+TN,47,51,1639744,Franklin County,H1,A
+TN,47,53,1639745,Gibson County,H1,A
+TN,47,55,1639746,Giles County,H1,A
+TN,47,57,1648575,Grainger County,H1,A
+TN,47,59,1639747,Greene County,H1,A
+TN,47,61,1639748,Grundy County,H1,A
+TN,47,63,1648576,Hamblen County,H1,A
+TN,47,65,1639749,Hamilton County,H1,A
+TN,47,67,1648577,Hancock County,H1,A
+TN,47,69,1639750,Hardeman County,H1,A
+TN,47,71,1639751,Hardin County,H1,A
+TN,47,73,1639752,Hawkins County,H1,A
+TN,47,75,1639753,Haywood County,H1,A
+TN,47,77,1639754,Henderson County,H1,A
+TN,47,79,1639755,Henry County,H1,A
+TN,47,81,1639756,Hickman County,H1,A
+TN,47,83,1648578,Houston County,H1,A
+TN,47,85,1639757,Humphreys County,H1,A
+TN,47,87,1639758,Jackson County,H1,A
+TN,47,89,1639759,Jefferson County,H1,A
+TN,47,91,1639760,Johnson County,H1,A
+TN,47,93,1639761,Knox County,H1,A
+TN,47,95,1639762,Lake County,H1,A
+TN,47,97,1639763,Lauderdale County,H1,A
+TN,47,99,1639764,Lawrence County,H1,A
+TN,47,101,1639765,Lewis County,H1,A
+TN,47,103,1639766,Lincoln County,H1,A
+TN,47,105,1639767,Loudon County,H1,A
+TN,47,107,1639773,McMinn County,H1,A
+TN,47,109,1639774,McNairy County,H1,A
+TN,47,111,1639768,Macon County,H1,A
+TN,47,113,1639769,Madison County,H1,A
+TN,47,115,1639770,Marion County,H1,A
+TN,47,117,1639771,Marshall County,H1,A
+TN,47,119,1639772,Maury County,H1,A
+TN,47,121,1639775,Meigs County,H1,A
+TN,47,123,1639776,Monroe County,H1,A
+TN,47,125,1639777,Montgomery County,H1,A
+TN,47,127,1648579,Moore County,H6,C
+TN,47,129,1639778,Morgan County,H1,A
+TN,47,131,1639779,Obion County,H1,A
+TN,47,133,1639780,Overton County,H1,A
+TN,47,135,1639781,Perry County,H1,A
+TN,47,137,1648580,Pickett County,H1,A
+TN,47,139,1639782,Polk County,H1,A
+TN,47,141,1639783,Putnam County,H1,A
+TN,47,143,1639784,Rhea County,H1,A
+TN,47,145,1639785,Roane County,H1,A
+TN,47,147,1639786,Robertson County,H1,A
+TN,47,149,1639787,Rutherford County,H1,A
+TN,47,151,1639788,Scott County,H1,A
+TN,47,153,1652643,Sequatchie County,H1,A
+TN,47,155,1639789,Sevier County,H1,A
+TN,47,157,1639790,Shelby County,H1,A
+TN,47,159,1639791,Smith County,H1,A
+TN,47,161,1639792,Stewart County,H1,A
+TN,47,163,1639793,Sullivan County,H1,A
+TN,47,165,1639794,Sumner County,H1,A
+TN,47,167,1639795,Tipton County,H1,A
+TN,47,169,1648581,Trousdale County,H6,C
+TN,47,171,1648582,Unicoi County,H1,A
+TN,47,173,1648583,Union County,H1,A
+TN,47,175,1648584,Van Buren County,H1,A
+TN,47,177,1639796,Warren County,H1,A
+TN,47,179,1639797,Washington County,H1,A
+TN,47,181,1639798,Wayne County,H1,A
+TN,47,183,1639799,Weakley County,H1,A
+TN,47,185,1639800,White County,H1,A
+TN,47,187,1639801,Williamson County,H1,A
+TN,47,189,1639802,Wilson County,H1,A
+TX,48,1,1383786,Anderson County,H1,A
+TX,48,3,1383787,Andrews County,H1,A
+TX,48,5,1383788,Angelina County,H1,A
+TX,48,7,1383789,Aransas County,H1,A
+TX,48,9,1383790,Archer County,H1,A
+TX,48,11,1383791,Armstrong County,H1,A
+TX,48,13,1383792,Atascosa County,H1,A
+TX,48,15,1383793,Austin County,H1,A
+TX,48,17,1383794,Bailey County,H1,A
+TX,48,19,1383795,Bandera County,H1,A
+TX,48,21,1383796,Bastrop County,H1,A
+TX,48,23,1383797,Baylor County,H1,A
+TX,48,25,1383798,Bee County,H1,A
+TX,48,27,1383799,Bell County,H1,A
+TX,48,29,1383800,Bexar County,H1,A
+TX,48,31,1383801,Blanco County,H1,A
+TX,48,33,1383802,Borden County,H1,A
+TX,48,35,1383803,Bosque County,H1,A
+TX,48,37,1383804,Bowie County,H1,A
+TX,48,39,1383805,Brazoria County,H1,A
+TX,48,41,1383806,Brazos County,H1,A
+TX,48,43,1383807,Brewster County,H1,A
+TX,48,45,1383808,Briscoe County,H1,A
+TX,48,47,1383809,Brooks County,H1,A
+TX,48,49,1383810,Brown County,H1,A
+TX,48,51,1383811,Burleson County,H1,A
+TX,48,53,1383812,Burnet County,H1,A
+TX,48,55,1383813,Caldwell County,H1,A
+TX,48,57,1383814,Calhoun County,H1,A
+TX,48,59,1383815,Callahan County,H1,A
+TX,48,61,1383816,Cameron County,H1,A
+TX,48,63,1383817,Camp County,H1,A
+TX,48,65,1383818,Carson County,H1,A
+TX,48,67,1383819,Cass County,H1,A
+TX,48,69,1383820,Castro County,H1,A
+TX,48,71,1383821,Chambers County,H1,A
+TX,48,73,1383822,Cherokee County,H1,A
+TX,48,75,1383823,Childress County,H1,A
+TX,48,77,1383824,Clay County,H1,A
+TX,48,79,1383825,Cochran County,H1,A
+TX,48,81,1383826,Coke County,H1,A
+TX,48,83,1383827,Coleman County,H1,A
+TX,48,85,1383828,Collin County,H1,A
+TX,48,87,1383829,Collingsworth County,H1,A
+TX,48,89,1383830,Colorado County,H1,A
+TX,48,91,1383831,Comal County,H1,A
+TX,48,93,1383832,Comanche County,H1,A
+TX,48,95,1383833,Concho County,H1,A
+TX,48,97,1383834,Cooke County,H1,A
+TX,48,99,1383835,Coryell County,H1,A
+TX,48,101,1383836,Cottle County,H1,A
+TX,48,103,1383837,Crane County,H1,A
+TX,48,105,1383838,Crockett County,H1,A
+TX,48,107,1383839,Crosby County,H1,A
+TX,48,109,1383840,Culberson County,H1,A
+TX,48,111,1383841,Dallam County,H1,A
+TX,48,113,1383842,Dallas County,H1,A
+TX,48,115,1383843,Dawson County,H1,A
+TX,48,117,1383845,Deaf Smith County,H1,A
+TX,48,119,1383846,Delta County,H1,A
+TX,48,121,1383847,Denton County,H1,A
+TX,48,123,1383844,DeWitt County,H1,A
+TX,48,125,1383848,Dickens County,H1,A
+TX,48,127,1383849,Dimmit County,H1,A
+TX,48,129,1383850,Donley County,H1,A
+TX,48,131,1383851,Duval County,H1,A
+TX,48,133,1383852,Eastland County,H1,A
+TX,48,135,1383853,Ector County,H1,A
+TX,48,137,1383854,Edwards County,H1,A
+TX,48,139,1383856,Ellis County,H1,A
+TX,48,141,1383855,El Paso County,H1,A
+TX,48,143,1383857,Erath County,H1,A
+TX,48,145,1383858,Falls County,H1,A
+TX,48,147,1383859,Fannin County,H1,A
+TX,48,149,1383860,Fayette County,H1,A
+TX,48,151,1383861,Fisher County,H1,A
+TX,48,153,1383862,Floyd County,H1,A
+TX,48,155,1383863,Foard County,H1,A
+TX,48,157,1383864,Fort Bend County,H1,A
+TX,48,159,1383865,Franklin County,H1,A
+TX,48,161,1383866,Freestone County,H1,A
+TX,48,163,1383867,Frio County,H1,A
+TX,48,165,1383868,Gaines County,H1,A
+TX,48,167,1383869,Galveston County,H1,A
+TX,48,169,1383870,Garza County,H1,A
+TX,48,171,1383871,Gillespie County,H1,A
+TX,48,173,1383872,Glasscock County,H1,A
+TX,48,175,1383873,Goliad County,H1,A
+TX,48,177,1383874,Gonzales County,H1,A
+TX,48,179,1383875,Gray County,H1,A
+TX,48,181,1383876,Grayson County,H1,A
+TX,48,183,1383877,Gregg County,H1,A
+TX,48,185,1383878,Grimes County,H1,A
+TX,48,187,1383879,Guadalupe County,H1,A
+TX,48,189,1383880,Hale County,H1,A
+TX,48,191,1383881,Hall County,H1,A
+TX,48,193,1383882,Hamilton County,H1,A
+TX,48,195,1383883,Hansford County,H1,A
+TX,48,197,1383884,Hardeman County,H1,A
+TX,48,199,1383885,Hardin County,H1,A
+TX,48,201,1383886,Harris County,H1,A
+TX,48,203,1383887,Harrison County,H1,A
+TX,48,205,1383888,Hartley County,H1,A
+TX,48,207,1383889,Haskell County,H1,A
+TX,48,209,1383890,Hays County,H1,A
+TX,48,211,1383891,Hemphill County,H1,A
+TX,48,213,1383892,Henderson County,H1,A
+TX,48,215,1383893,Hidalgo County,H1,A
+TX,48,217,1383894,Hill County,H1,A
+TX,48,219,1383895,Hockley County,H1,A
+TX,48,221,1383896,Hood County,H1,A
+TX,48,223,1383897,Hopkins County,H1,A
+TX,48,225,1383898,Houston County,H1,A
+TX,48,227,1383899,Howard County,H1,A
+TX,48,229,1383900,Hudspeth County,H1,A
+TX,48,231,1383901,Hunt County,H1,A
+TX,48,233,1383902,Hutchinson County,H1,A
+TX,48,235,1383903,Irion County,H1,A
+TX,48,237,1383904,Jack County,H1,A
+TX,48,239,1383905,Jackson County,H1,A
+TX,48,241,1383906,Jasper County,H1,A
+TX,48,243,1383907,Jeff Davis County,H1,A
+TX,48,245,1383908,Jefferson County,H1,A
+TX,48,247,1383909,Jim Hogg County,H1,A
+TX,48,249,1383910,Jim Wells County,H1,A
+TX,48,251,1383911,Johnson County,H1,A
+TX,48,253,1383912,Jones County,H1,A
+TX,48,255,1383913,Karnes County,H1,A
+TX,48,257,1383914,Kaufman County,H1,A
+TX,48,259,1383915,Kendall County,H1,A
+TX,48,261,1383916,Kenedy County,H1,A
+TX,48,263,1383917,Kent County,H1,A
+TX,48,265,1383918,Kerr County,H1,A
+TX,48,267,1383919,Kimble County,H1,A
+TX,48,269,1383920,King County,H1,A
+TX,48,271,1383921,Kinney County,H1,A
+TX,48,273,1383922,Kleberg County,H1,A
+TX,48,275,1383923,Knox County,H1,A
+TX,48,277,1383925,Lamar County,H1,A
+TX,48,279,1383926,Lamb County,H1,A
+TX,48,281,1383927,Lampasas County,H1,A
+TX,48,283,1383924,La Salle County,H1,A
+TX,48,285,1383928,Lavaca County,H1,A
+TX,48,287,1383929,Lee County,H1,A
+TX,48,289,1383930,Leon County,H1,A
+TX,48,291,1383931,Liberty County,H1,A
+TX,48,293,1383932,Limestone County,H1,A
+TX,48,295,1383933,Lipscomb County,H1,A
+TX,48,297,1383934,Live Oak County,H1,A
+TX,48,299,1383935,Llano County,H1,A
+TX,48,301,1383936,Loving County,H1,A
+TX,48,303,1383937,Lubbock County,H1,A
+TX,48,305,1383938,Lynn County,H1,A
+TX,48,307,1383945,McCulloch County,H1,A
+TX,48,309,1383946,McLennan County,H1,A
+TX,48,311,1383947,McMullen County,H1,A
+TX,48,313,1383939,Madison County,H1,A
+TX,48,315,1383940,Marion County,H1,A
+TX,48,317,1383941,Martin County,H1,A
+TX,48,319,1383942,Mason County,H1,A
+TX,48,321,1383943,Matagorda County,H1,A
+TX,48,323,1383944,Maverick County,H1,A
+TX,48,325,1383948,Medina County,H1,A
+TX,48,327,1383949,Menard County,H1,A
+TX,48,329,1383950,Midland County,H1,A
+TX,48,331,1383951,Milam County,H1,A
+TX,48,333,1383952,Mills County,H1,A
+TX,48,335,1383953,Mitchell County,H1,A
+TX,48,337,1383954,Montague County,H1,A
+TX,48,339,1383955,Montgomery County,H1,A
+TX,48,341,1383956,Moore County,H1,A
+TX,48,343,1383957,Morris County,H1,A
+TX,48,345,1383958,Motley County,H1,A
+TX,48,347,1383959,Nacogdoches County,H1,A
+TX,48,349,1383960,Navarro County,H1,A
+TX,48,351,1383961,Newton County,H1,A
+TX,48,353,1383962,Nolan County,H1,A
+TX,48,355,1383963,Nueces County,H1,A
+TX,48,357,1383964,Ochiltree County,H1,A
+TX,48,359,1383965,Oldham County,H1,A
+TX,48,361,1383966,Orange County,H1,A
+TX,48,363,1383967,Palo Pinto County,H1,A
+TX,48,365,1383968,Panola County,H1,A
+TX,48,367,1383969,Parker County,H1,A
+TX,48,369,1383970,Parmer County,H1,A
+TX,48,371,1383971,Pecos County,H1,A
+TX,48,373,1383972,Polk County,H1,A
+TX,48,375,1383973,Potter County,H1,A
+TX,48,377,1383974,Presidio County,H1,A
+TX,48,379,1383975,Rains County,H1,A
+TX,48,381,1383976,Randall County,H1,A
+TX,48,383,1383977,Reagan County,H1,A
+TX,48,385,1383978,Real County,H1,A
+TX,48,387,1383979,Red River County,H1,A
+TX,48,389,1383980,Reeves County,H1,A
+TX,48,391,1383981,Refugio County,H1,A
+TX,48,393,1383982,Roberts County,H1,A
+TX,48,395,1383983,Robertson County,H1,A
+TX,48,397,1383984,Rockwall County,H1,A
+TX,48,399,1383985,Runnels County,H1,A
+TX,48,401,1383986,Rusk County,H1,A
+TX,48,403,1383987,Sabine County,H1,A
+TX,48,405,1383988,San Augustine County,H1,A
+TX,48,407,1383989,San Jacinto County,H1,A
+TX,48,409,1383990,San Patricio County,H1,A
+TX,48,411,1383991,San Saba County,H1,A
+TX,48,413,1383992,Schleicher County,H1,A
+TX,48,415,1383993,Scurry County,H1,A
+TX,48,417,1383994,Shackelford County,H1,A
+TX,48,419,1383995,Shelby County,H1,A
+TX,48,421,1383996,Sherman County,H1,A
+TX,48,423,1383997,Smith County,H1,A
+TX,48,425,1383998,Somervell County,H1,A
+TX,48,427,1383999,Starr County,H1,A
+TX,48,429,1384000,Stephens County,H1,A
+TX,48,431,1384001,Sterling County,H1,A
+TX,48,433,1384002,Stonewall County,H1,A
+TX,48,435,1384003,Sutton County,H1,A
+TX,48,437,1384004,Swisher County,H1,A
+TX,48,439,1384005,Tarrant County,H1,A
+TX,48,441,1384006,Taylor County,H1,A
+TX,48,443,1384007,Terrell County,H1,A
+TX,48,445,1384008,Terry County,H1,A
+TX,48,447,1384009,Throckmorton County,H1,A
+TX,48,449,1384010,Titus County,H1,A
+TX,48,451,1384011,Tom Green County,H1,A
+TX,48,453,1384012,Travis County,H1,A
+TX,48,455,1384013,Trinity County,H1,A
+TX,48,457,1384014,Tyler County,H1,A
+TX,48,459,1384015,Upshur County,H1,A
+TX,48,461,1384016,Upton County,H1,A
+TX,48,463,1384017,Uvalde County,H1,A
+TX,48,465,1384018,Val Verde County,H1,A
+TX,48,467,1384019,Van Zandt County,H1,A
+TX,48,469,1384020,Victoria County,H1,A
+TX,48,471,1384021,Walker County,H1,A
+TX,48,473,1384022,Waller County,H1,A
+TX,48,475,1384023,Ward County,H1,A
+TX,48,477,1384024,Washington County,H1,A
+TX,48,479,1384025,Webb County,H1,A
+TX,48,481,1384026,Wharton County,H1,A
+TX,48,483,1384027,Wheeler County,H1,A
+TX,48,485,1384028,Wichita County,H1,A
+TX,48,487,1384029,Wilbarger County,H1,A
+TX,48,489,1384030,Willacy County,H1,A
+TX,48,491,1384031,Williamson County,H1,A
+TX,48,493,1384032,Wilson County,H1,A
+TX,48,495,1384033,Winkler County,H1,A
+TX,48,497,1384034,Wise County,H1,A
+TX,48,499,1384035,Wood County,H1,A
+TX,48,501,1384036,Yoakum County,H1,A
+TX,48,503,1384037,Young County,H1,A
+TX,48,505,1384038,Zapata County,H1,A
+TX,48,507,1384039,Zavala County,H1,A
+UT,49,1,1448015,Beaver County,H1,A
+UT,49,3,1455966,Box Elder County,H1,A
+UT,49,5,1448017,Cache County,H1,A
+UT,49,7,1448018,Carbon County,H1,A
+UT,49,9,1448019,Daggett County,H1,A
+UT,49,11,1448020,Davis County,H1,A
+UT,49,13,1448021,Duchesne County,H1,A
+UT,49,15,1448022,Emery County,H1,A
+UT,49,17,1448023,Garfield County,H1,A
+UT,49,19,1448024,Grand County,H1,A
+UT,49,21,1448025,Iron County,H1,A
+UT,49,23,1455158,Juab County,H1,A
+UT,49,25,1448026,Kane County,H1,A
+UT,49,27,1448027,Millard County,H1,A
+UT,49,29,1448028,Morgan County,H1,A
+UT,49,31,1448029,Piute County,H1,A
+UT,49,33,1448030,Rich County,H1,A
+UT,49,35,1448031,Salt Lake County,H1,A
+UT,49,37,1448032,San Juan County,H1,A
+UT,49,39,1448033,Sanpete County,H1,A
+UT,49,41,1448034,Sevier County,H1,A
+UT,49,43,1448035,Summit County,H1,A
+UT,49,45,1448036,Tooele County,H1,A
+UT,49,47,1448037,Uintah County,H1,A
+UT,49,49,1448038,Utah County,H1,A
+UT,49,51,1448039,Wasatch County,H1,A
+UT,49,53,1448040,Washington County,H1,A
+UT,49,55,1448041,Wayne County,H1,A
+UT,49,57,1448042,Weber County,H1,A
+VT,50,1,1461757,Addison County,H1,A
+VT,50,3,1461758,Bennington County,H1,A
+VT,50,5,1461759,Caledonia County,H1,A
+VT,50,7,1461760,Chittenden County,H1,A
+VT,50,9,1461761,Essex County,H1,A
+VT,50,11,1461762,Franklin County,H1,A
+VT,50,13,1461763,Grand Isle County,H1,A
+VT,50,15,1461764,Lamoille County,H1,A
+VT,50,17,1461765,Orange County,H1,A
+VT,50,19,1461766,Orleans County,H1,A
+VT,50,21,1461767,Rutland County,H1,A
+VT,50,23,1461768,Washington County,H1,A
+VT,50,25,1461769,Windham County,H1,A
+VT,50,27,1461770,Windsor County,H1,A
+VA,51,1,1480091,Accomack County,H1,A
+VA,51,3,1675170,Albemarle County,H1,A
+VA,51,5,1673675,Alleghany County,H1,A
+VA,51,7,1497770,Amelia County,H1,A
+VA,51,9,1480095,Amherst County,H1,A
+VA,51,11,1497238,Appomattox County,H1,A
+VA,51,13,1480097,Arlington County,H1,A
+VA,51,15,1480098,Augusta County,H1,A
+VA,51,17,1673638,Bath County,H1,A
+VA,51,19,1674818,Bedford County,H1,A
+VA,51,21,1494613,Bland County,H1,A
+VA,51,23,1674418,Botetourt County,H1,A
+VA,51,25,1493961,Brunswick County,H1,A
+VA,51,27,1497431,Buchanan County,H1,A
+VA,51,29,1501502,Buckingham County,H1,A
+VA,51,31,1674917,Campbell County,H1,A
+VA,51,33,1480107,Caroline County,H1,A
+VA,51,35,1480108,Carroll County,H1,A
+VA,51,36,1480109,Charles City County,H1,A
+VA,51,37,1492442,Charlotte County,H1,A
+VA,51,41,1480111,Chesterfield County,H1,A
+VA,51,43,1690562,Clarke County,H1,A
+VA,51,45,1673664,Craig County,H1,A
+VA,51,47,1497831,Culpeper County,H1,A
+VA,51,49,1494109,Cumberland County,H1,A
+VA,51,51,1497376,Dickenson County,H1,A
+VA,51,53,1690739,Dinwiddie County,H1,A
+VA,51,57,1480118,Essex County,H1,A
+VA,51,59,1480119,Fairfax County,H1,A
+VA,51,61,1480120,Fauquier County,H1,A
+VA,51,63,1494354,Floyd County,H1,A
+VA,51,65,1494061,Fluvanna County,H1,A
+VA,51,67,1494434,Franklin County,H1,A
+VA,51,69,1480124,Frederick County,H1,A
+VA,51,71,1494551,Giles County,H1,A
+VA,51,73,1480126,Gloucester County,H1,A
+VA,51,75,1674271,Goochland County,H1,A
+VA,51,77,1501196,Grayson County,H1,A
+VA,51,79,1480129,Greene County,H1,A
+VA,51,81,1501122,Greensville County,H1,A
+VA,51,83,1496705,Halifax County,H1,A
+VA,51,85,1480132,Hanover County,H1,A
+VA,51,87,1480133,Henrico County,H1,A
+VA,51,89,1502770,Henry County,H1,A
+VA,51,91,1673774,Highland County,H1,A
+VA,51,93,1702378,Isle of Wight County,H1,A
+VA,51,95,1480135,James City County,H1,A
+VA,51,97,1480136,King and Queen County,H1,A
+VA,51,99,1480137,King George County,H1,A
+VA,51,101,1480138,King William County,H1,A
+VA,51,103,1480139,Lancaster County,H1,A
+VA,51,105,1496538,Lee County,H1,A
+VA,51,107,1480141,Loudoun County,H1,A
+VA,51,109,1494707,Louisa County,H1,A
+VA,51,111,1492239,Lunenburg County,H1,A
+VA,51,113,1494590,Madison County,H1,A
+VA,51,115,1480145,Mathews County,H1,A
+VA,51,117,1500747,Mecklenburg County,H1,A
+VA,51,119,1480147,Middlesex County,H1,A
+VA,51,121,1674630,Montgomery County,H1,A
+VA,51,125,1500936,Nelson County,H1,A
+VA,51,127,1480150,New Kent County,H1,A
+VA,51,131,1480151,Northampton County,H1,A
+VA,51,133,1480152,Northumberland County,H1,A
+VA,51,135,1493928,Nottoway County,H1,A
+VA,51,137,1494761,Orange County,H1,A
+VA,51,139,1500440,Page County,H1,A
+VA,51,141,1497999,Patrick County,H1,A
+VA,51,143,1480157,Pittsylvania County,H1,A
+VA,51,145,1674225,Powhatan County,H1,A
+VA,51,147,1492359,Prince Edward County,H1,A
+VA,51,149,1480160,Prince George County,H1,A
+VA,51,153,1480161,Prince William County,H1,A
+VA,51,155,1501465,Pulaski County,H1,A
+VA,51,157,1673976,Rappahannock County,H1,A
+VA,51,159,1480164,Richmond County,H1,A
+VA,51,161,1480165,Roanoke County,H1,A
+VA,51,163,1480166,Rockbridge County,H1,A
+VA,51,165,1488970,Rockingham County,H1,A
+VA,51,167,1497573,Russell County,H1,A
+VA,51,169,1498172,Scott County,H1,A
+VA,51,171,1673918,Shenandoah County,H1,A
+VA,51,173,1500545,Smyth County,H1,A
+VA,51,175,1480171,Southampton County,H1,A
+VA,51,177,1480172,Spotsylvania County,H1,A
+VA,51,179,1480173,Stafford County,H1,A
+VA,51,181,1480174,Surry County,H1,A
+VA,51,183,1690257,Sussex County,H1,A
+VA,51,185,1497748,Tazewell County,H1,A
+VA,51,187,1674058,Warren County,H1,A
+VA,51,191,1498359,Washington County,H1,A
+VA,51,193,1480179,Westmoreland County,H1,A
+VA,51,195,1496656,Wise County,H1,A
+VA,51,197,1501379,Wythe County,H1,A
+VA,51,199,1480182,York County,H1,A
+VA,51,510,1498415,Alexandria city,C7,F
+VA,51,520,1498416,Bristol city,C7,F
+VA,51,530,1498417,Buena Vista city,C7,F
+VA,51,540,1789068,Charlottesville city,C7,F
+VA,51,550,1498558,Chesapeake city,C7,F
+VA,51,570,1498420,Colonial Heights city,C7,F
+VA,51,580,1789069,Covington city,C7,F
+VA,51,590,1498421,Danville city,C7,F
+VA,51,595,1498422,Emporia city,C7,F
+VA,51,600,1789070,Fairfax city,C7,F
+VA,51,610,1498423,Falls Church city,C7,F
+VA,51,620,1498424,Franklin city,C7,F
+VA,51,630,1498425,Fredericksburg city,C7,F
+VA,51,640,1498426,Galax city,C7,F
+VA,51,650,1498554,Hampton city,C7,F
+VA,51,660,1498563,Harrisonburg city,C7,F
+VA,51,670,1498428,Hopewell city,C7,F
+VA,51,678,1789071,Lexington city,C7,F
+VA,51,680,1498429,Lynchburg city,C7,F
+VA,51,683,1498430,Manassas city,C7,F
+VA,51,685,1498431,Manassas Park city,C7,F
+VA,51,690,1789072,Martinsville city,C7,F
+VA,51,700,1498555,Newport News city,C7,F
+VA,51,710,1498557,Norfolk city,C7,F
+VA,51,720,1498434,Norton city,C7,F
+VA,51,730,1498435,Petersburg city,C7,F
+VA,51,735,1498436,Poquoson city,C7,F
+VA,51,740,1498556,Portsmouth city,C7,F
+VA,51,750,1498438,Radford city,C7,F
+VA,51,760,1789073,Richmond city,C7,F
+VA,51,770,1498439,Roanoke city,C7,F
+VA,51,775,1789074,Salem city,C7,F
+VA,51,790,1789075,Staunton city,C7,F
+VA,51,800,1498560,Suffolk city,C7,F
+VA,51,810,1498559,Virginia Beach city,C7,F
+VA,51,820,1498443,Waynesboro city,C7,F
+VA,51,830,1789076,Williamsburg city,C7,F
+VA,51,840,1789077,Winchester city,C7,F
+WA,53,1,1531601,Adams County,H1,A
+WA,53,3,1533502,Asotin County,H1,A
+WA,53,5,1513302,Benton County,H1,A
+WA,53,7,1531932,Chelan County,H1,A
+WA,53,9,1531341,Clallam County,H1,A
+WA,53,11,1531820,Clark County,H1,A
+WA,53,13,1513273,Columbia County,H1,A
+WA,53,15,1529156,Cowlitz County,H1,A
+WA,53,17,1531818,Douglas County,H1,A
+WA,53,19,1531821,Ferry County,H1,A
+WA,53,21,1531822,Franklin County,H1,A
+WA,53,23,1533500,Garfield County,H1,A
+WA,53,25,1531924,Grant County,H1,A
+WA,53,27,1531823,Grays Harbor County,H1,A
+WA,53,29,1513272,Island County,H1,A
+WA,53,31,1531936,Jefferson County,H1,A
+WA,53,33,1531933,King County,H1,A
+WA,53,35,1529223,Kitsap County,H1,A
+WA,53,37,1531926,Kittitas County,H1,A
+WA,53,39,1529219,Klickitat County,H1,A
+WA,53,41,1531927,Lewis County,H1,A
+WA,53,43,1514052,Lincoln County,H1,A
+WA,53,45,1529221,Mason County,H1,A
+WA,53,47,1531928,Okanogan County,H1,A
+WA,53,49,1513274,Pacific County,H1,A
+WA,53,51,1529157,Pend Oreille County,H1,A
+WA,53,53,1529159,Pierce County,H1,A
+WA,53,55,1531931,San Juan County,H1,A
+WA,53,57,1531402,Skagit County,H1,A
+WA,53,59,1529220,Skamania County,H1,A
+WA,53,61,1529222,Snohomish County,H1,A
+WA,53,63,1529225,Spokane County,H1,A
+WA,53,65,1531930,Stevens County,H1,A
+WA,53,67,1529226,Thurston County,H1,A
+WA,53,69,1513275,Wahkiakum County,H1,A
+WA,53,71,1531819,Walla Walla County,H1,A
+WA,53,73,1529224,Whatcom County,H1,A
+WA,53,75,1533501,Whitman County,H1,A
+WA,53,77,1531929,Yakima County,H1,A
+WV,54,1,1696996,Barbour County,H1,A
+WV,54,3,1550008,Berkeley County,H1,A
+WV,54,5,1550009,Boone County,H1,A
+WV,54,7,1550010,Braxton County,H1,A
+WV,54,9,1558262,Brooke County,H1,A
+WV,54,11,1550012,Cabell County,H1,A
+WV,54,13,1696945,Calhoun County,H1,A
+WV,54,15,1550014,Clay County,H1,A
+WV,54,17,1689476,Doddridge County,H1,A
+WV,54,19,1560095,Fayette County,H1,A
+WV,54,21,1689673,Gilmer County,H1,A
+WV,54,23,1697238,Grant County,H1,A
+WV,54,25,1557518,Greenbrier County,H1,A
+WV,54,27,1718644,Hampshire County,H1,A
+WV,54,29,1560574,Hancock County,H1,A
+WV,54,31,1718557,Hardy County,H1,A
+WV,54,33,1718426,Harrison County,H1,A
+WV,54,35,1559963,Jackson County,H1,A
+WV,54,37,1550025,Jefferson County,H1,A
+WV,54,39,1550026,Kanawha County,H1,A
+WV,54,41,1550027,Lewis County,H1,A
+WV,54,43,1550028,Lincoln County,H1,A
+WV,54,45,1550029,Logan County,H1,A
+WV,54,47,1689162,McDowell County,H1,A
+WV,54,49,1718173,Marion County,H1,A
+WV,54,51,1717539,Marshall County,H1,A
+WV,54,53,1560254,Mason County,H1,A
+WV,54,55,1557645,Mercer County,H1,A
+WV,54,57,1717890,Mineral County,H1,A
+WV,54,59,1550036,Mingo County,H1,A
+WV,54,61,1550037,Monongalia County,H1,A
+WV,54,63,1557702,Monroe County,H1,A
+WV,54,65,1557950,Morgan County,H1,A
+WV,54,67,1550040,Nicholas County,H1,A
+WV,54,69,1717163,Ohio County,H1,A
+WV,54,71,1550042,Pendleton County,H1,A
+WV,54,73,1678557,Pleasants County,H1,A
+WV,54,75,1550044,Pocahontas County,H1,A
+WV,54,77,1558642,Preston County,H1,A
+WV,54,79,1550046,Putnam County,H1,A
+WV,54,81,1550047,Raleigh County,H1,A
+WV,54,83,1550048,Randolph County,H1,A
+WV,54,85,1689049,Ritchie County,H1,A
+WV,54,87,1550050,Roane County,H1,A
+WV,54,89,1557805,Summers County,H1,A
+WV,54,91,1689856,Taylor County,H1,A
+WV,54,93,1689423,Tucker County,H1,A
+WV,54,95,1678706,Tyler County,H1,A
+WV,54,97,1696800,Upshur County,H1,A
+WV,54,99,1550056,Wayne County,H1,A
+WV,54,101,1550057,Webster County,H1,A
+WV,54,103,1717836,Wetzel County,H1,A
+WV,54,105,1678877,Wirt County,H1,A
+WV,54,107,1560558,Wood County,H1,A
+WV,54,109,1550061,Wyoming County,H1,A
+WI,55,1,1581060,Adams County,H1,A
+WI,55,3,1581061,Ashland County,H1,A
+WI,55,5,1581062,Barron County,H1,A
+WI,55,7,1581063,Bayfield County,H1,A
+WI,55,9,1581064,Brown County,H1,A
+WI,55,11,1581065,Buffalo County,H1,A
+WI,55,13,1581066,Burnett County,H1,A
+WI,55,15,1581067,Calumet County,H1,A
+WI,55,17,1581068,Chippewa County,H1,A
+WI,55,19,1581069,Clark County,H1,A
+WI,55,21,1581070,Columbia County,H1,A
+WI,55,23,1581071,Crawford County,H1,A
+WI,55,25,1581072,Dane County,H1,A
+WI,55,27,1581073,Dodge County,H1,A
+WI,55,29,1581074,Door County,H1,A
+WI,55,31,1581075,Douglas County,H1,A
+WI,55,33,1581076,Dunn County,H1,A
+WI,55,35,1581077,Eau Claire County,H1,A
+WI,55,37,1581078,Florence County,H1,A
+WI,55,39,1581079,Fond du Lac County,H1,A
+WI,55,41,1581080,Forest County,H1,A
+WI,55,43,1581081,Grant County,H1,A
+WI,55,45,1581082,Green County,H1,A
+WI,55,47,1581083,Green Lake County,H1,A
+WI,55,49,1581084,Iowa County,H1,A
+WI,55,51,1581085,Iron County,H1,A
+WI,55,53,1581086,Jackson County,H1,A
+WI,55,55,1581087,Jefferson County,H1,A
+WI,55,57,1581088,Juneau County,H1,A
+WI,55,59,1581089,Kenosha County,H1,A
+WI,55,61,1581090,Kewaunee County,H1,A
+WI,55,63,1581091,La Crosse County,H1,A
+WI,55,65,1581092,Lafayette County,H1,A
+WI,55,67,1581093,Langlade County,H1,A
+WI,55,69,1581094,Lincoln County,H1,A
+WI,55,71,1581095,Manitowoc County,H1,A
+WI,55,73,1581096,Marathon County,H1,A
+WI,55,75,1581097,Marinette County,H1,A
+WI,55,77,1581098,Marquette County,H1,A
+WI,55,78,1581099,Menominee County,H1,A
+WI,55,79,1581100,Milwaukee County,H1,A
+WI,55,81,1581101,Monroe County,H1,A
+WI,55,83,1581102,Oconto County,H1,A
+WI,55,85,1581103,Oneida County,H1,A
+WI,55,87,1581104,Outagamie County,H1,A
+WI,55,89,1581105,Ozaukee County,H1,A
+WI,55,91,1581106,Pepin County,H1,A
+WI,55,93,1581107,Pierce County,H1,A
+WI,55,95,1581108,Polk County,H1,A
+WI,55,97,1581109,Portage County,H1,A
+WI,55,99,1581110,Price County,H1,A
+WI,55,101,1581111,Racine County,H1,A
+WI,55,103,1581112,Richland County,H1,A
+WI,55,105,1581113,Rock County,H1,A
+WI,55,107,1581114,Rusk County,H1,A
+WI,55,109,1581833,St. Croix County,H1,A
+WI,55,111,1581115,Sauk County,H1,A
+WI,55,113,1581116,Sawyer County,H1,A
+WI,55,115,1581117,Shawano County,H1,A
+WI,55,117,1581118,Sheboygan County,H1,A
+WI,55,119,1581119,Taylor County,H1,A
+WI,55,121,1581120,Trempealeau County,H1,A
+WI,55,123,1581121,Vernon County,H1,A
+WI,55,125,1581122,Vilas County,H1,A
+WI,55,127,1581123,Walworth County,H1,A
+WI,55,129,1581124,Washburn County,H1,A
+WI,55,131,1581125,Washington County,H1,A
+WI,55,133,1581126,Waukesha County,H1,A
+WI,55,135,1581127,Waupaca County,H1,A
+WI,55,137,1581128,Waushara County,H1,A
+WI,55,139,1581129,Winnebago County,H1,A
+WI,55,141,1581130,Wood County,H1,A
+WY,56,1,1605066,Albany County,H1,A
+WY,56,3,1605067,Big Horn County,H1,A
+WY,56,5,1605068,Campbell County,H1,A
+WY,56,7,1605069,Carbon County,H1,A
+WY,56,9,1605070,Converse County,H1,A
+WY,56,11,1605071,Crook County,H1,A
+WY,56,13,1605072,Fremont County,H1,A
+WY,56,15,1605073,Goshen County,H1,A
+WY,56,17,1605074,Hot Springs County,H1,A
+WY,56,19,1609191,Johnson County,H1,A
+WY,56,21,1605075,Laramie County,H1,A
+WY,56,23,1605076,Lincoln County,H1,A
+WY,56,25,1605077,Natrona County,H1,A
+WY,56,27,1605078,Niobrara County,H1,A
+WY,56,29,1605079,Park County,H1,A
+WY,56,31,1605080,Platte County,H1,A
+WY,56,33,1605081,Sheridan County,H1,A
+WY,56,35,1605082,Sublette County,H1,A
+WY,56,37,1609192,Sweetwater County,H1,A
+WY,56,39,1605083,Teton County,H1,A
+WY,56,41,1605084,Uinta County,H1,A
+WY,56,43,1605085,Washakie County,H1,A
+WY,56,45,1605086,Weston County,H1,A
+AS,60,10,1805240,Eastern District,H1,A
+AS,60,20,1805242,Manu'a District,H1,A
+AS,60,30,1805243,Rose Island,H4,N
+AS,60,40,1805244,Swains Island,H4,N
+AS,60,50,1805241,Western District,H1,A
+GU,66,10,1802705,Guam,H4,F
+MP,69,85,1805245,Northern Islands Municipality,H1,B
+MP,69,100,1805246,Rota Municipality,H1,A
+MP,69,110,1805247,Saipan Municipality,H1,A
+MP,69,120,1805248,Tinian Municipality,H1,A
+PR,72,1,1804480,Adjuntas Municipio,H1,A
+PR,72,3,1804481,Aguada Municipio,H1,A
+PR,72,5,1804482,Aguadilla Municipio,H1,A
+PR,72,7,1804483,Aguas Buenas Municipio,H1,A
+PR,72,9,1804484,Aibonito Municipio,H1,A
+PR,72,11,1804485,Añasco Municipio,H1,A
+PR,72,13,1804486,Arecibo Municipio,H1,A
+PR,72,15,1804487,Arroyo Municipio,H1,A
+PR,72,17,1804488,Barceloneta Municipio,H1,A
+PR,72,19,1804489,Barranquitas Municipio,H1,A
+PR,72,21,1804490,Bayamón Municipio,H1,A
+PR,72,23,1804491,Cabo Rojo Municipio,H1,A
+PR,72,25,1804492,Caguas Municipio,H1,A
+PR,72,27,1804493,Camuy Municipio,H1,A
+PR,72,29,1804494,Canóvanas Municipio,H1,A
+PR,72,31,1804495,Carolina Municipio,H1,A
+PR,72,33,1804496,Cataño Municipio,H1,A
+PR,72,35,1804497,Cayey Municipio,H1,A
+PR,72,37,1804498,Ceiba Municipio,H1,A
+PR,72,39,1804499,Ciales Municipio,H1,A
+PR,72,41,1804500,Cidra Municipio,H1,A
+PR,72,43,1804501,Coamo Municipio,H1,A
+PR,72,45,1804502,ComerÃÂo Municipio,H1,A
+PR,72,47,1804503,Corozal Municipio,H1,A
+PR,72,49,1804504,Culebra Municipio,H1,A
+PR,72,51,1804505,Dorado Municipio,H1,A
+PR,72,53,1804506,Fajardo Municipio,H1,A
+PR,72,54,1804507,Florida Municipio,H1,A
+PR,72,55,1804508,Guánica Municipio,H1,A
+PR,72,57,1804509,Guayama Municipio,H1,A
+PR,72,59,1804510,Guayanilla Municipio,H1,A
+PR,72,61,1804511,Guaynabo Municipio,H1,A
+PR,72,63,1804512,Gurabo Municipio,H1,A
+PR,72,65,1804513,Hatillo Municipio,H1,A
+PR,72,67,1804514,Hormigueros Municipio,H1,A
+PR,72,69,1804515,Humacao Municipio,H1,A
+PR,72,71,1804516,Isabela Municipio,H1,A
+PR,72,73,1804517,Jayuya Municipio,H1,A
+PR,72,75,1804518,Juana DÃÂaz Municipio,H1,A
+PR,72,77,1804519,Juncos Municipio,H1,A
+PR,72,79,1804520,Lajas Municipio,H1,A
+PR,72,81,1804521,Lares Municipio,H1,A
+PR,72,83,1804522,Las MarÃÂas Municipio,H1,A
+PR,72,85,1804523,Las Piedras Municipio,H1,A
+PR,72,87,1804524,LoÃÂza Municipio,H1,A
+PR,72,89,1804525,Luquillo Municipio,H1,A
+PR,72,91,1804526,ManatàMunicipio,H1,A
+PR,72,93,1804527,Maricao Municipio,H1,A
+PR,72,95,1804528,Maunabo Municipio,H1,A
+PR,72,97,1804529,Mayagüez Municipio,H1,A
+PR,72,99,1804530,Moca Municipio,H1,A
+PR,72,101,1804531,Morovis Municipio,H1,A
+PR,72,103,1804532,Naguabo Municipio,H1,A
+PR,72,105,1804533,Naranjito Municipio,H1,A
+PR,72,107,1804534,Orocovis Municipio,H1,A
+PR,72,109,1804535,Patillas Municipio,H1,A
+PR,72,111,1804536,Peñuelas Municipio,H1,A
+PR,72,113,1804537,Ponce Municipio,H1,A
+PR,72,115,1804538,Quebradillas Municipio,H1,A
+PR,72,117,1804539,Rincón Municipio,H1,A
+PR,72,119,1804540,RÃÂo Grande Municipio,H1,A
+PR,72,121,1804541,Sabana Grande Municipio,H1,A
+PR,72,123,1804542,Salinas Municipio,H1,A
+PR,72,125,1804543,San Germán Municipio,H1,A
+PR,72,127,1804544,San Juan Municipio,H1,A
+PR,72,129,1804545,San Lorenzo Municipio,H1,A
+PR,72,131,1804546,San Sebastián Municipio,H1,A
+PR,72,133,1804547,Santa Isabel Municipio,H1,A
+PR,72,135,1804548,Toa Alta Municipio,H1,A
+PR,72,137,1804549,Toa Baja Municipio,H1,A
+PR,72,139,1804550,Trujillo Alto Municipio,H1,A
+PR,72,141,1804551,Utuado Municipio,H1,A
+PR,72,143,1804552,Vega Alta Municipio,H1,A
+PR,72,145,1804553,Vega Baja Municipio,H1,A
+PR,72,147,1804554,Vieques Municipio,H1,A
+PR,72,149,1804555,Villalba Municipio,H1,A
+PR,72,151,1804556,Yabucoa Municipio,H1,A
+PR,72,153,1804557,Yauco Municipio,H1,A
+UM,74,300,1802699,Midway Islands,H4,N
+VI,78,10,2378248,St. Croix Island,H4,N
+VI,78,20,2378249,St. John Island,H4,N
+VI,78,30,2378250,St. Thomas Island,H4,N
diff --git a/hydromt_fiat/data/temp_database/config.ini b/hydromt_fiat/data/temp_database/config.ini
deleted file mode 100644
index ab39fed0..00000000
--- a/hydromt_fiat/data/temp_database/config.ini
+++ /dev/null
@@ -1,24 +0,0 @@
-[setup_config]
-site_name = ""
-scenario_name = ""
-output_dir = ""
-crs = ""
-
-[setup_hazard]
-hazard_map_fn = ""
-hazard_type = ""
-no_data = -9999
-chunks = "auto"
-
-[setup_vulnerability]
-vulnerability_fn = ""
-link_table = ""
-units = "meter"
-
-[setup_exposure_vector]
-asset_locations = ""
-occupancy_type = ""
-max_potential_damage = "-999"
-ground_floor_height = "-999"
-gfh_units = "meter"
-extraction_method = "centroid"
diff --git a/hydromt_fiat/data/temp_database/data_catalog.yml b/hydromt_fiat/data/temp_database/data_catalog.yml
deleted file mode 100644
index 03a4203d..00000000
--- a/hydromt_fiat/data/temp_database/data_catalog.yml
+++ /dev/null
@@ -1,40 +0,0 @@
-meta:
- root: C:\Users\peiter\Projects\hydromt_fiat\hydromt_fiat\data\temp_database
-NSI:
- data_type: GeoDataFrame
- path: https://nsi.sec.usace.army.mil/nsiapi/structures?fmt=fc
- driver: vector
- filesystem: local
- kwargs:
- translation_fn: C:\python\hydromt_fiat\local_test_database\Social_Vulnerability\attribute_linking\NSI_attributes_to_FIAT.json
- crs: 4326
- meta:
- category: exposure
-hazus_max_potential_damages:
- data_type: DataFrame
- path: C:\python\hydromt_fiat\local_test_database\Social_Vulnerability\max_potential_damages\damage_values_fema_hazus-inventory-technical-manual-4.2.3.xlsx
- driver: xlsx
- filesystem: local
- meta:
- category: vulnerability
-hazus_vulnerability_curves:
- data_type: DataFrame
- path: C:\python\hydromt_fiat\local_test_database\Social_Vulnerability\damage_functions\flooding\AllDDF_HAZUS.xlsx
- driver: xlsx
- filesystem: local
- meta:
- category: vulnerability
-jrc_vulnerability_curves:
- data_type: DataFrame
- path: C:\python\hydromt_fiat\local_test_database\Social_Vulnerability\damage_functions\flooding\JRC_damage_functions.xlsx
- driver: xlsx
- filesystem: local
- meta:
- category: vulnerability
-social_vulnerability:
- data_type: DataFrame
- path: C:\python\hydromt_fiat\local_test_database\Social_Vulnerability\census_vulnerability_data_codebook.xlsx
- driver: xlsx
- filesystem: local
- meta:
- category: social_vulnerability
diff --git a/hydromt_fiat/data/vulnerability_linking/JRC_damage_functions_linking.csv b/hydromt_fiat/data/vulnerability_linking/JRC_damage_functions_linking.csv
new file mode 100644
index 00000000..76b2fe31
--- /dev/null
+++ b/hydromt_fiat/data/vulnerability_linking/JRC_damage_functions_linking.csv
@@ -0,0 +1,10 @@
+FIAT Damage Function Name,Exposure Link,Damage Type,type
+residential,residential,total,residential
+commercial,commercial,total,commercial
+industrial,industrial,total,industrial
+residential,residential,structure,residential
+commercial,commercial,structure,commercial
+industrial,industrial,structure,industrial
+residential,residential,content,residential
+commercial,commercial,content,commercial
+industrial,industrial,content,industrial
diff --git a/hydromt_fiat/data/vulnerability_linking/default_hazus_iwr_curve_linking.csv b/hydromt_fiat/data/vulnerability_linking/default_hazus_iwr_curve_linking.csv
new file mode 100644
index 00000000..aa598db8
--- /dev/null
+++ b/hydromt_fiat/data/vulnerability_linking/default_hazus_iwr_curve_linking.csv
@@ -0,0 +1,77 @@
+FIAT Damage Function Name,Exposure Link,Damage Type,ID,Occupancy,Description,Source
+AGR1,AGR1,structure,616,AGR1,"Average Agriculture, structure",USACE - Galveston
+COM1,COM1,structure,217,COM1,"Average Retail, Structure",USACE - Galveston
+COM10,COM10,structure,543,COM10,"Garage, structure",USACE - Galveston
+COM2,COM2,structure,341,COM2,"Average wholesale, Structure",USACE - Galveston
+COM3,COM3,structure,32,COM3-S-MostLikely,Average Personal & Repair Services,USACE - IWR
+COM4,COM4,structure,35,COM4-S-MostLikely,Average Prof/Tech Services,USACE - IWR
+COM5,COM5,structure,467,COM5,"Bank, Structure",USACE - Galveston
+COM6,COM6,structure,38,COM6-S-MostLikely,Hospital,USACE - IWR
+COM7,COM7,structure,475,COM7,"Average Medical Office, Structure",USACE - Galveston
+COM8,COM8,structure,493,COM8,"Average Entertainment/Recreation, Structure",USACE - Galveston
+COM9,COM9,structure,532,COM9,"Average theatre, Structure",USACE - Galveston
+EDU1,EDU1,structure,53,EDU1-S-MostLikely,Average school,USACE - IWR
+EDU2,EDU2,structure,652,EDU2,"Average college/university, structure",USACE - Galveston
+GOV1,GOV1,structure,631,GOV1,"Average government services, structure",USACE - Galveston
+GOV2,GOV2,structure,640,GOV2,"Average emergency response, Structure",USACE - Galveston
+IND1,IND1,structure,545,IND1,"Average heavy industrial, Structure",USACE - Galveston
+IND2,IND2,structure,44,IND2-S-MostLikely,Average light industrial,USACE - IWR
+IND3,IND3,structure,575,IND3,"Average Food/Drug/Chem, Structure",USACE - Galveston
+IND4,IND4,structure,586,IND4,"Average Metals/Minerals processing, structure",USACE - Galveston
+IND5,IND5,structure,591,IND5,"Average High Technology, structure",USACE - Galveston
+IND6,IND6,structure,592,IND6,"Average Construction, structure",USACE - Galveston
+REL1,REL1,structure,47,REL1-S-MostLikely,Church,USACE - IWR
+RES1-1SNB,RES1-1SNB,structure,2,RES1-1SNB-S-MostLikely,"Res 1, 1 Story no Basement",USACE - IWR
+RES1-1SWB,RES1-1SWB,structure,5,RES1-1SWB-S-MostLikely,"Res 1, 1 Story w/ Basement",USACE - IWR
+RES1-2SNB,RES1-2SNB,structure,8,RES1-2SNB-S-MostLikely,"Res 1, 2 Story no Basement",USACE - IWR
+RES1-2SWB,RES1-2SWB,structure,11,RES1-2SWB-S-MostLikely,"Res 1, 2 Story w/ Basement",USACE - IWR
+RES1-3SNB,RES1-3SNB,structure,117,RES1,"three or more floors, no basement, Structure, V-Zone",FIA
+RES1-3SWB,RES1-3SWB,structure,127,RES1,"three or more floors, w/ basement, Structure, V-Zone",FIA
+RES2,RES2,structure,20,RES2-S-MostLikely,Mobile home,USACE - IWR
+RES3A,RES3A,structure,23,RES3-S-MostLikely,"Condominium, living area on multiple floors",USACE - IWR
+RES3B,RES3B,structure,23,RES3-S-MostLikely,"Condominium, living area on multiple floors",USACE - IWR
+RES3C,RES3C,structure,23,RES3-S-MostLikely,"Condominium, living area on multiple floors",USACE - IWR
+RES3D,RES3D,structure,23,RES3-S-MostLikely,"Condominium, living area on multiple floors",USACE - IWR
+RES3E,RES3E,structure,23,RES3-S-MostLikely,"Condominium, living area on multiple floors",USACE - IWR
+RES3F,RES3F,structure,23,RES3-S-MostLikely,"Condominium, living area on multiple floors",USACE - IWR
+RES4,RES4,structure,26,RES4-S-MostLikely,"Average Hotel, & Motel",USACE - IWR
+RES5,RES5,structure,50,RES5-PRO-SVC-S-MostLikely,Nursing Home,USACE - IWR
+RES6,RES6,structure,215,RES6,"Nursing Home, structure",USACE - Galveston
+AGR1,AGR1,content,460,AGR1 ,"Average Agriculture, Contents (Equipment/Inventory)",USACE - Galveston
+COM1,COM1,content,90,COM1 ,Average retail trade contents (Inventory/Equipment),USACE - Galveston
+COM10,COM10,content,357,COM10,"Garage, Contents (Inventory)",USACE - Galveston
+COM2,COM2,content,195,COM2 ,"Average wholesale trade, Contents",USACE - Galveston
+COM3,COM3,content,92,COM3-C-MostLikely,Average Personal & Repair Services,USACE - IWR
+COM4,COM4,content,95,COM4-C-MostLikely,Average Prof/Tech Services,USACE - IWR
+COM5,COM5,content,304,COM5 ,"Bank, Contents",USACE - Galveston
+COM6,COM6,content,98,COM6-C-MostLikely,Hospital,USACE - IWR
+COM7,COM7,content,312,COM7 ,"Average Medical Offic/Clinic, Contents (Equipment/Inventory)",USACE - Galveston
+COM8,COM8,content,322,COM8 ,"Average Entertainment/Recreation, Contents (Equipment/Inventory)",USACE - Galveston
+COM9,COM9,content,352,COM9 ,"Theater, Contents (Equipment)",USACE - Galveston
+EDU1,EDU1,content,113,EDU1-C-MostLikely,Average school,USACE - IWR
+EDU2,EDU2,content,485,EDU2 ,"Average College/University, Contents (Equipment/Inventory)",USACE - Galveston
+GOV1,GOV1,content,472,GOV1 ,"Average Govt Services, contents (Equipment)",USACE - Galveston
+GOV2,GOV2,content,477,GOV2 ,"Average emergency response, contents (Equipment/Inventory)",USACE - Galveston
+IND1,IND1,content,358,IND1 ,"Average Heavy Industrial, Contents (Equipment/Inventory)",USACE - Galveston
+IND2,IND2,content,104,IND2-C-MostLikely,Average light industrial,USACE - IWR
+IND3,IND3,content,408,IND3 ,"Average Food/Drugs/Chemicals, Contents (Equipment/Inventory)",USACE - Galveston
+IND4,IND4,content,433,IND4 ,"Average Metals/Minerals Processing, Contents (Equipment/Inventory)",USACE - Galveston
+IND5,IND5,content,442,IND5 ,"Average High Technology, contents (Equipment/Inventory)",USACE - Galveston
+IND6,IND6,content,443,IND6 ,"Average Construction, contents (Equipment/Inventory)",USACE - Galveston
+REL1,REL1,content,107,REL1-C-MostLikely,Church,USACE - IWR
+RES1-1SNB,RES1-1SNB,content,62,RES1-1SNB-C-MostLikely,"Res 1, 1 Story no Basement",USACE - IWR
+RES1-1SWB,RES1-1SWB,content,65,RES1-1SWB-C-MostLikely,"Res 1, 1 Story w/ Basement",USACE - IWR
+RES1-2SNB,RES1-2SNB,content,68,RES1-2SNB-C-MostLikely,"Res 1, 2 Story no Basement",USACE - IWR
+RES1-2SWB,RES1-2SWB,content,71,RES1-2SWB-C-MostLikely,"Res 1, 2 Story w/ Basement",USACE - IWR
+RES1-3SNB,RES1-3SNB,content,33,RES1 ,"three or more floors, no basement, Contents, V-Zone",FIA (MOD.)
+RES1-3SWB,RES1-3SWB,content,34,RES1 ,"three or more floors, w/ basement, Contents, V-Zone",FIA (MOD.)
+RES2,RES2,content,80,RES2-C-MostLikely,Mobile home,USACE - IWR
+RES3A,RES3A,content,83,RES3-C-MostLikely,"Condominium, living area on multiple floors",USACE - IWR
+RES3B,RES3B,content,83,RES3-C-MostLikely,"Condominium, living area on multiple floors",USACE - IWR
+RES3C,RES3C,content,83,RES3-C-MostLikely,"Condominium, living area on multiple floors",USACE - IWR
+RES3D,RES3D,content,83,RES3-C-MostLikely,"Condominium, living area on multiple floors",USACE - IWR
+RES3E,RES3E,content,83,RES3-C-MostLikely,"Condominium, living area on multiple floors",USACE - IWR
+RES3F,RES3F,content,83,RES3-C-MostLikely,"Condominium, living area on multiple floors",USACE - IWR
+RES4,RES4,content,86,RES4-C-MostLikely,"Average Hotel, & Motel",USACE - IWR
+RES5,RES5,content,110,RES5-PRO-SVC-C-MostLikely,Nursing Home,USACE - IWR
+RES6,RES6,content,89,RES6 ,"Nursing Home, contents (Equipment)",USACE - Galveston
diff --git a/hydromt_fiat/data/vulnerability_linking/hazus_iwr_occupancy_classes.csv b/hydromt_fiat/data/vulnerability_linking/hazus_iwr_occupancy_classes.csv
new file mode 100644
index 00000000..acab1452
--- /dev/null
+++ b/hydromt_fiat/data/vulnerability_linking/hazus_iwr_occupancy_classes.csv
@@ -0,0 +1,40 @@
+Occupancy Class,Class Description,Stories,Basement
+RES1,Single-family Dwelling,,
+RES1-1SNB,"Single-family Dwelling, one-story no basement",1,No
+RES1-1SWB,"Single-family Dwelling, one-story with basement",1,Yes
+RES1-2SNB,"Single-family Dwelling, two-story no basement",2,No
+RES1-2SWB,"Single-family Dwelling, two-story with basement",2,Yes
+RES1-3SNB,"Single-family Dwelling, three-story no basement",3,No
+RES1-3SWB,"Single-family Dwelling, three-story with basement",3,Yes
+RES2,Mobile Home,,
+RES3A,Multi-Family Dwelling – Duplex,,
+RES3B,Multi-Family Dwelling – 3-4 Units,,
+RES3C,Multi-Family Dwelling – 5-9 Units,,
+RES3D,Multi-Family Dwelling – 10-19 Units,,
+RES3E,Multi-Family Dwelling – 20-49 Units,,
+RES3F,Multi-Family Dwelling – 50+ Units,,
+RES4,Temporary Lodging,,
+RES5,Institutional Dormitory,,
+RES6,Nursing Home,,
+COM1,Retail Trade,,
+COM2,Wholesale Trade,,
+COM3,Personal and Repair Services,,
+COM4,Business/Professional/Technical Services,,
+COM5,Depository Institutions (Banks),,
+COM6,Hospital,,
+COM7,Medical Office/Clinic,,
+COM8,Entertainment & Recreation,,
+COM9,Theaters,,
+COM10,Parking,,
+IND1,Heavy,,
+IND2,Light,,
+IND3,Food/Drugs/Chemicals,,
+IND4,Metals/Minerals Processing,,
+IND5,High Technology,,
+IND6,Construction,,
+AGR1,Agriculture,,
+REL1,Church/Non-Profit,,
+GOV1,General Services,,
+GOV2,Emergency Response,,
+EDU1,Schools/Libraries,,
+EDU2,Colleges/Universities,,
diff --git a/hydromt_fiat/data_apis/open_street_maps.py b/hydromt_fiat/data_apis/open_street_maps.py
index a1d4347b..de09c87e 100644
--- a/hydromt_fiat/data_apis/open_street_maps.py
+++ b/hydromt_fiat/data_apis/open_street_maps.py
@@ -1,16 +1,17 @@
import osmnx as ox
import logging
from shapely.geometry import Polygon
+import geopandas as gpd
+from typing import Union, List
-def get_assets_from_osm(polygon: Polygon):
+def get_assets_from_osm(polygon: Polygon) -> gpd.GeoDataFrame:
tags = {"building": True} # this is the tag we use to find the correct OSM data
footprints = ox.features.features_from_polygon(
polygon, tags
) # then we query the data
if footprints.empty:
- logging.warning("No buildings found from OSM")
return None
logging.info(f"Total number of buildings found from OSM: {len(footprints)}")
@@ -25,12 +26,41 @@ def get_assets_from_osm(polygon: Polygon):
return footprints
-def get_landuse_from_osm(polygon: Polygon):
- tags = {"landuse": True} # this is the tag we use to find the correct OSM data
- landuse = ox.features.features_from_polygon(
- polygon, tags
+def get_roads_from_osm(
+ polygon: Polygon,
+ road_types: Union[str, List[str], bool] = True,
+) -> gpd.GeoDataFrame:
+ if isinstance(road_types, str):
+ road_types = [road_types]
+
+ tag = {
+ "highway": road_types
+ } # this is the tag we use to find the correct OSM data
+
+ roads = ox.features.features_from_polygon(
+ polygon, tags=tag
) # then we query the data
+ if roads.empty:
+ return None
+
+ logging.info(f"Total number of roads found from OSM: {len(roads)}")
+
+ # Not sure if this is needed here and maybe filter for the columns that we need
+ roads = roads.loc[
+ (roads.geometry.type == "LineString")
+ | (roads.geometry.type == "MultiLineString")
+ ]
+ roads = roads.reset_index(drop=True)
+ roads = roads.loc[:, ["highway", "name", "lanes", "geometry"]]
+
+ return roads
+
+
+def get_landuse_from_osm(polygon: Polygon) -> gpd.GeoDataFrame:
+ tags = {"landuse": True} # this is the tag we use to find the correct OSM data
+ landuse = ox.features.features_from_polygon(polygon, tags) # then we query the data
+
if landuse.empty:
logging.warning("No land use data found from OSM")
return None
@@ -47,18 +77,6 @@ def get_landuse_from_osm(polygon: Polygon):
return landuse
-if __name__ == "__main__":
- _polygon = Polygon(
- [
- [-80.21997289327112, 25.83897611793664],
- [-80.21997289327112, 25.86427542636784],
- [-80.24609330530801, 25.86427542636784],
- [-80.24609330530801, 25.83897611793664],
- [-80.21997289327112, 25.83897611793664],
- ]
- )
- get_landuse_from_osm(_polygon)
-
# # Do a spatial join to connect the buildings with the classes
# # (here we use a buffer area of 100 m around the classes in case there buildings falling completely out of the classes)
# # a more comprehensive spatial join to account for overlapping area can be used as well
diff --git a/hydromt_fiat/fiat.py b/hydromt_fiat/fiat.py
index 7a0c5daf..8207bb11 100644
--- a/hydromt_fiat/fiat.py
+++ b/hydromt_fiat/fiat.py
@@ -1,26 +1,41 @@
"""Implement fiat model class"""
import csv
-import glob
import logging
import os
-from os.path import basename, join
from pathlib import Path
from typing import List, Optional, Union
-
+import xarray as xr
import geopandas as gpd
import hydromt
import pandas as pd
from hydromt.models.model_grid import GridModel
-from hydromt_sfincs import SfincsModel
from shapely.geometry import box
-
-from . import DATADIR
-from .config import Config
-from .workflows.exposure_vector import ExposureVector
-from .workflows.hazard import *
-from .workflows.social_vulnerability_index import SocialVulnerabilityIndex
-from .workflows.vulnerability import Vulnerability
+import os
+import shutil
+
+from hydromt_fiat import DATADIR
+from hydromt_fiat.config import Config
+from hydromt_fiat.workflows.exposure_vector import ExposureVector
+from hydromt_fiat.workflows.hazard import (
+ create_lists,
+ check_lists_size,
+ read_maps,
+ check_maps_metadata,
+ check_maps_rp,
+ check_map_uniqueness,
+ create_risk_dataset,
+)
+from hydromt_fiat.workflows.equity_data import EquityData
+from hydromt_fiat.workflows.social_vulnerability_index import (
+ SocialVulnerabilityIndex,
+ list_of_states,
+)
+from hydromt_fiat.workflows.vulnerability import Vulnerability
+from hydromt_fiat.workflows.aggregation_areas import join_exposure_aggregation_areas
+from hydromt_fiat.workflows.building_footprints import join_exposure_building_footprints
+from hydromt_fiat.workflows.gis import locate_from_bounding_box
+from hydromt_fiat.workflows.utils import get_us_county_numbers
__all__ = ["FiatModel"]
@@ -35,7 +50,7 @@ class FiatModel(GridModel):
_GEOMS = {} # FIXME Mapping from hydromt names to model specific names
_MAPS = {} # FIXME Mapping from hydromt names to model specific names
_FOLDERS = ["hazard", "exposure", "vulnerability", "output"]
- _CLI_ARGS = {"region": "setup_basemaps"}
+ _CLI_ARGS = {"region": "setup_region"}
_DATADIR = DATADIR
def __init__(
@@ -56,9 +71,20 @@ def __init__(
self._tables = dict() # Dictionary of tables to write
self.exposure = None
self.vulnerability = None
- self.vulnerability_metadata = list()
+ self.vf_ids_and_linking_df = pd.DataFrame()
+ self.additional_attributes_fn = (
+ "" # Path or paths to the additional attributes dataset(s)
+ )
+ self.building_footprint_fn = "" # Path to the building footprints dataset
- def setup_global_settings(self, crs: str):
+ def setup_global_settings(
+ self,
+ crs: str = None,
+ gdal_cache: int = None,
+ keep_temp_files: bool = None,
+ thread: int = None,
+ chunk: List[int] = None,
+ ) -> None:
"""Setup Delft-FIAT global settings.
Parameters
@@ -66,7 +92,16 @@ def setup_global_settings(self, crs: str):
crs : str
The CRS of the model.
"""
- self.set_config("global.crs", crs)
+ if crs:
+ self.set_config("global.crs", crs)
+ if gdal_cache:
+ self.set_config("global.gdal_cache", gdal_cache)
+ if keep_temp_files:
+ self.set_config("global.keep_temp_files", keep_temp_files)
+ if thread:
+ self.set_config("global.thread", thread)
+ if chunk:
+ self.set_config("global.grid.chunk", chunk)
def setup_output(
self,
@@ -92,12 +127,11 @@ def setup_output(
for i, name in enumerate(output_vector_name):
self.set_config(f"output.geom.name{str(i+1)}", name)
- def setup_basemaps(
+ def setup_region(
self,
region,
**kwargs,
):
- # FIXME Mario will update this function according to the one in Habitat
"""Define the model domain that is used to clip the raster layers.
Adds model layer:
@@ -167,11 +201,9 @@ def setup_vulnerability(
df_vulnerability = self.data_catalog.get_dataframe(vulnerability_fn)
# Read the vulnerability linking table
- vf_ids_and_linking_df = self.data_catalog.get_dataframe(
+ self.vf_ids_and_linking_df = self.data_catalog.get_dataframe(
vulnerability_identifiers_and_linking_fn
)
- # Add the vulnerability linking table to the tables object
- self.set_tables(df=vf_ids_and_linking_df, name="vulnerability_identifiers")
# If the JRC vulnerability curves are used, the continent needs to be specified
if (
@@ -181,7 +213,7 @@ def setup_vulnerability(
assert (
continent is not None
), "Please specify the continent when using the JRC vulnerability curves."
- vf_ids_and_linking_df["continent"] = continent.lower()
+ self.vf_ids_and_linking_df["continent"] = continent.lower()
# Process the vulnerability data
self.vulnerability = Vulnerability(
@@ -193,7 +225,7 @@ def setup_vulnerability(
# vulnerability curves file for Delft-FIAT.
self.vulnerability.get_vulnerability_functions_from_one_file(
df_source=df_vulnerability,
- df_identifiers_linking=vf_ids_and_linking_df,
+ df_identifiers_linking=self.vf_ids_and_linking_df,
continent=continent,
)
@@ -223,13 +255,35 @@ def setup_vulnerability_from_csv(self, csv_fn: Union[str, Path], unit: str) -> N
(e.g. meter).
"""
# Process the vulnerability data
- self.vulnerability = Vulnerability(
- unit,
- self.logger,
- )
+ if not self.vulnerability:
+ self.vulnerability = Vulnerability(
+ unit,
+ self.logger,
+ )
self.vulnerability.from_csv(csv_fn)
- def setup_exposure_vector(
+ def setup_road_vulnerability(
+ self,
+ vertical_unit: str,
+ threshold_value: float = 0.6,
+ min_hazard_value: float = 0,
+ max_hazard_value: float = 10,
+ step_hazard_value: float = 1.0,
+ ):
+ if not self.vulnerability:
+ self.vulnerability = Vulnerability(
+ vertical_unit,
+ self.logger,
+ )
+ self.vulnerability.create_step_function(
+ "roads",
+ threshold_value,
+ min_hazard_value,
+ max_hazard_value,
+ step_hazard_value,
+ )
+
+ def setup_exposure_buildings(
self,
asset_locations: Union[str, Path],
occupancy_type: Union[str, Path],
@@ -238,10 +292,11 @@ def setup_exposure_vector(
unit: str,
occupancy_type_field: Union[str, None] = None,
extraction_method: str = "centroid",
- damage_types: Union[List[str], None] = ["structure", "content"],
+ damage_types: List[str] = ["structure", "content"],
country: Union[str, None] = None,
+ ground_elevation_file: Union[int, float, str, Path, None] = None,
) -> None:
- """Setup vector exposure data for Delft-FIAT.
+ """Setup building exposure (vector) data for Delft-FIAT.
Parameters
----------
@@ -265,19 +320,30 @@ def setup_exposure_vector(
extraction_method : str, optional
The method that should be used to extract the hazard values from the
hazard maps, by default "centroid".
+ damage_types : Union[List[str], None], optional
+ The damage types that should be used for the exposure data, by default
+ ["structure", "content"]. The damage types are used to link the
+ vulnerability functions to the exposure data.
+ country : Union[str, None], optional
+ The country that is used for the exposure data, by default None. This is
+ only required when using the JRC vulnerability curves.
"""
self.exposure = ExposureVector(self.data_catalog, self.logger, self.region)
if asset_locations == occupancy_type == max_potential_damage:
# The source for the asset locations, occupancy type and maximum potential
# damage is the same, use one source to create the exposure data.
- self.exposure.setup_from_single_source(
- asset_locations, ground_floor_height, extraction_method
+ self.exposure.setup_buildings_from_single_source(
+ asset_locations,
+ ground_floor_height,
+ extraction_method,
+ ground_elevation_file=ground_elevation_file,
)
+
else:
# The source for the asset locations, occupancy type and maximum potential
# damage is different, use three sources to create the exposure data.
- self.exposure.setup_from_multiple_sources(
+ self.exposure.setup_buildings_from_multiple_sources(
asset_locations,
occupancy_type,
max_potential_damage,
@@ -286,6 +352,7 @@ def setup_exposure_vector(
occupancy_type_field,
damage_types=damage_types,
country=country,
+ ground_elevation_file=ground_elevation_file,
)
# Link the damage functions to assets
@@ -294,7 +361,7 @@ def setup_exposure_vector(
except AssertionError:
logging.error(
"Please call the 'setup_vulnerability' function before "
- "the 'setup_exposure_vector' function. Error message: {e}"
+ "the 'setup_exposure_buildings' function. Error message: {e}"
)
self.exposure.link_exposure_vulnerability(
self.vf_ids_and_linking_df, damage_types
@@ -302,10 +369,76 @@ def setup_exposure_vector(
self.exposure.check_required_columns()
# Update the other config settings
- self.set_config("exposure.geom.csv", "./exposure/exposure.csv")
+ self.set_config("exposure.csv.file", "./exposure/exposure.csv")
self.set_config("exposure.geom.crs", self.exposure.crs)
self.set_config("exposure.geom.unit", unit)
+ def setup_exposure_roads(
+ self,
+ roads_fn: Union[str, Path],
+ road_damage: Union[str, Path, int],
+ road_types: Union[str, List[str], bool] = True,
+ unit: str = "m",
+ ):
+ """Setup road exposure data for Delft-FIAT.
+
+ Parameters
+ ----------
+ roads_fn : Union[str, Path]
+ Path to the road network source (e.g., OSM) or file.
+ road_types : Union[str, List[str], bool], optional
+ List of road types to include in the exposure data, by default True
+ """
+ if not self.exposure:
+ self.exposure = ExposureVector(
+ self.data_catalog, self.logger, self.region, unit=unit
+ )
+ self.exposure.setup_roads(roads_fn, road_damage, road_types)
+
+ # Link to vulnerability curves
+
+ # Combine the exposure database with pre-existing exposure data if available
+
+ def update_ground_floor_height(
+ self,
+ source: Union[int, float, str, Path, None],
+ attr_name: Union[str, List[str], None] = None,
+ method: Union[str, List[str], None] = "nearest",
+ max_dist: float = 10,
+ ):
+ if self.exposure:
+ self.exposure.setup_ground_floor_height(source, attr_name, method, max_dist)
+
+ def update_max_potential_damage(
+ self,
+ source: Union[
+ int, float, str, Path, List[str], List[Path], pd.DataFrame
+ ] = None,
+ damage_types: Union[List[str], str, None] = None,
+ country: Union[str, None] = None,
+ target_attribute: Union[str, List[str], None] = None,
+ attr_name: Union[str, List[str], None] = None,
+ method: Union[str, List[str], None] = "nearest",
+ max_dist: float = 10,
+ ):
+ if self.exposure:
+ self.exposure.setup_max_potential_damage(
+ max_potential_damage=source,
+ damage_types=damage_types,
+ country=country,
+ target_attribute=target_attribute,
+ attr_name=attr_name,
+ method=method,
+ max_dist=max_dist,
+ )
+
+ def update_ground_elevation(
+ self,
+ ground_elevation: Union[int, float, None, str, Path],
+ ):
+ if self.exposure:
+ self.exposure.setup_ground_elevation(ground_elevation)
+
def setup_exposure_raster(self):
"""Setup raster exposure data for Delft-FIAT.
This function will be implemented at a later stage.
@@ -325,7 +458,8 @@ def setup_hazard(
risk_output: bool = False,
unit_conversion_factor: float = 1.0,
) -> None:
- """Set up hazard maps. This component integrates multiple checks for the maps
+ """Set up hazard maps. This component integrates multiple checks for the hazard
+ maps.
Parameters
----------
@@ -362,58 +496,33 @@ def setup_hazard(
risk_output : bool, optional
The parameter that defines if a risk analysis is required, by default False
"""
- # check parameters types and size, and existance of provided files of maps
-
- params = check_parameters_type(map_fn, map_type, rp, crs, nodata, var, chunks)
- check_parameters_size(params)
- check_files(params, self.root)
+ # create lists of maps and their parameters to be able to iterate over them
+ params = create_lists(map_fn, map_type, rp, crs, nodata, var, chunks)
+ check_lists_size(params)
rp_list = []
map_name_lst = []
- # retrieve maps information from parameters and datacatalog
- # load maps in memory and check them and save the with st_map function
for idx, da_map_fn in enumerate(params["map_fn_lst"]):
- da_map_fn, da_name, da_type = read_floodmaps(params, da_map_fn, idx)
-
- # load flood maps to memory
- # da = load_floodmaps(self.data_catalog, self.region,da_map_fn,da_name,name_catalog)
- # reading from path
- if isinstance(da_map_fn, Path):
- if da_map_fn.stem == "sfincs_map":
- sfincs_root = os.path.dirname(da_map_fn)
- sfincs_model = SfincsModel(
- sfincs_root, mode="r", logger=self.logger
- )
- sfincs_model.read_results()
- # save sfincs map as GeoTIFF
- # result_list = list(sfincs_model.results.keys())
- # sfincs_model.write_raster("results.zsmax", compress="LZW")
- da = sfincs_model.results["zsmax"]
- # da = da.squeeze('timemax').drop('timemax')
- da = da.isel(timemax=0).drop("timemax")
-
-
- else:
- if not self.region.empty:
- # da = self.data_catalog.get_rasterdataset(
- # da_map_fn, geom=self.region
- # )
- da = self.data_catalog.get_rasterdataset(da_map_fn)
- else:
- da = self.data_catalog.get_rasterdataset(da_map_fn)
- # Convert to units of the exposure data if required
- # reading from the datacatalog
+ # read maps and retrieve their attributes
+ if isinstance(da_map_fn, os.PathLike):
+ # if path is provided read and load it as xarray
+ da_map_fn, da_name, da_type = read_maps(params, da_map_fn, idx)
+ da = self.data_catalog.get_rasterdataset(
+ da_map_fn
+ ) # removed geom=self.region because it is not always there
+ elif isinstance(da_map_fn, xr.DataArray):
+ # if xarray is provided directly assign that
+ da = da_map_fn
+ da_name = 'hazard_map'
+ da_type = map_type
else:
- if not self.region.empty:
- # da = self.data_catalog.get_rasterdataset(
- # name_catalog, variables=da_name, geom=self.region
- # )
- da = self.data_catalog.get_rasterdataset(map_fn, variables=da_name)
- else:
- da = self.data_catalog.get_rasterdataset(map_fn, variables=da_name)
- if self.exposure.unit != da.units:
- da = da * unit_conversion_factor
+ raise ValueError("The hazard map provided should be a path like object or an DataArray")
+ # Convert to units of the exposure data if required
+ if self.exposure: # change to be sure that the unit information is available from the exposure dataset
+ if self.exposure.unit != da.units:
+ da = da * unit_conversion_factor
+
da.encoding["_FillValue"] = None
da = da.raster.gdal_compliant()
@@ -423,74 +532,72 @@ def setup_hazard(
# check maps return periods
da_rp = check_maps_rp(params, da, da_name, idx, risk_output)
- # chek if maps are unique
- # TODO: check if providing methods like self.get_config can be used
- # TODO: create a new funtion to check uniqueness trhough files names
- # check_maps_uniquenes(self.get_config,self.staticmaps,params,da,da_map_fn,da_name,da_type,da_rp,idx)
+ if risk_output and da_map_fn.stem == "sfincs_map":
+ da_name = da_name + f"_{str(da_rp)}"
post = f"(rp {da_rp})" if risk_output else ""
self.logger.info(f"Added {hazard_type} hazard map: {da_name} {post}")
rp_list.append(da_rp)
-
- # If a risk calculation is required and the map comes from sfincs, they
- # have the same name so give another name
- if risk_output and da_map_fn.stem == "sfincs_map":
- da_name = da_name + f"_{str(da_rp)}"
map_name_lst.append(da_name)
+
+ da.attrs = {
+ "returnperiod": str(da_rp),
+ "type": da_type,
+ "name": da_name,
+ "analysis": "event",
+ }
+
+ da = da.to_dataset(name=da_name)
+
self.set_maps(da, da_name)
check_map_uniqueness(map_name_lst)
- # in case risk_output is required maps are put in a netcdf with a raster with
- # an extra dimension 'rp' accounting for return period
- # select first risk maps
+
+ # in case of risk analysis, create a single netcdf with multibans per rp
if risk_output:
- list_keys = list(self.maps.keys())
- first_map = self.maps[list_keys[0]].rename("risk_datarray")
- list_keys.pop(0)
-
- # add additional risk maps
- for idx, x in enumerate(list_keys):
- key_name = list_keys[idx]
- layer = self.maps[key_name]
- first_map = xr.concat([first_map, layer], dim="rp")
-
- # convert to a dataset to be able to write attributes when writing the maps
- # in the ouput folders. If datarray is provided attributes will not be
- # shown in the output netcdf dataset
- da = first_map.to_dataset(name="risk_maps")
- da.attrs = {
- "returnperiod": list(rp_list),
- "type": params["map_type_lst"],
- "name": map_name_lst,
- "Analysis": "risk",
+ da, sorted_rp, sorted_names = create_risk_dataset(
+ params, rp_list, map_name_lst, self.maps
+ )
+
+ self.set_grid(da)
+
+ self.grid.attrs = {
+ "rp": sorted_rp,
+ "type": params[
+ "map_type_lst"
+ ], # TODO: This parameter has to be changed in case that a list with different hazard types per map is provided
+ "name": sorted_names,
+ "analysis": "risk",
}
- # load merged map into self.maps
- self.set_maps(da)
+
list_maps = list(self.maps.keys())
- # erase individual maps from self.maps keeping the merged map
- for item in list_maps[:-1]:
+ for item in list_maps[:]:
self.maps.pop(item)
- self.set_config("hazard.return_periods", rp_list)
+ # set configuration .toml file
+ self.set_config(
+ "hazard.return_periods", str(da_rp) if not risk_output else sorted_rp
+ )
- # the metadata of the hazard maps is saved in the configuration toml files
- # this component was modified to provided the element [0] od the list
- # in case multiple maps are required then remove [0]
self.set_config(
"hazard.file",
[
str(Path("hazard") / (hazard_map + ".nc"))
for hazard_map in self.maps.keys()
- ][0],
+ ][0]
+ if not risk_output
+ else [str(Path("hazard") / ("risk_map" + ".nc"))][0],
)
self.set_config(
"hazard.crs",
[
- "EPSG:" + str((self.maps[hazard_map].rio.crs.to_epsg()))
+ "EPSG:" + str((self.maps[hazard_map].raster.crs.to_epsg()))
for hazard_map in self.maps.keys()
- ][0],
+ ][0]
+ if not risk_output
+ else ["EPSG:" + str((self.crs.to_epsg()))][0],
)
self.set_config(
@@ -500,7 +607,9 @@ def setup_hazard(
# Set the configurations for a multiband netcdf
self.set_config(
"hazard.settings.subset",
- [(self.maps[hazard_map].name) for hazard_map in self.maps.keys()][0],
+ [(self.maps[hazard_map].name) for hazard_map in self.maps.keys()][0]
+ if not risk_output
+ else sorted_names,
)
self.set_config(
@@ -517,12 +626,12 @@ def setup_social_vulnerability_index(
self,
census_key: str,
codebook_fn: Union[str, Path],
- state_abbreviation: str,
- user_dataset_fn: str = None,
- blockgroup_fn: str = None,
+ year_data: int = None,
+ save_all: bool = False,
):
"""Setup the social vulnerability index for the vector exposure data for
- Delft-FIAT.
+ Delft-FIAT. This method has so far only been tested with US Census data
+ but aims to be generalized to other datasets as well.
Parameters
----------
@@ -532,66 +641,208 @@ def setup_social_vulnerability_index(
The user's unique Census key that they got from the census.gov website
(https://api.census.gov/data/key_signup.html) to be able to download the
Census data
- path : Union[str, Path]
+ codebook_fn : Union[str, Path]
The path to the codebook excel
- state_abbreviation : str
- The abbreviation of the US state one would like to use in the analysis
+ year_data: int
+ The year of which the census data should be downloaded, 2020, 2021, or 2022
+ save_all: bool
+ If True, all (normalized) data variables are saved, if False, only the SVI
+ column is added to the FIAT exposure data. By default False.
"""
-
- # Create SVI object
- svi = SocialVulnerabilityIndex(self.data_catalog, self.logger)
-
- # Call functionalities of SVI
- # svi.read_dataset(user_dataset_fn)
- svi.set_up_census_key(census_key)
- svi.variable_code_csv_to_pd_df(codebook_fn)
- svi.set_up_download_codes()
- svi.set_up_state_code(state_abbreviation)
- svi.download_census_data()
- svi.rename_census_data("Census_code_withE", "Census_variable_name")
- svi.identify_no_data()
- svi.check_nan_variable_columns("Census_variable_name", "Indicator_code")
- svi.check_zeroes_variable_rows()
- translation_variable_to_indicator = svi.create_indicator_groups(
- "Census_variable_name", "Indicator_code"
- )
- svi.processing_svi_data(translation_variable_to_indicator)
- svi.normalization_svi_data()
- svi.domain_scores()
- svi.composite_scores()
- svi.match_geo_ID()
- svi.load_shp_geom(blockgroup_fn)
- svi.merge_svi_data_shp()
-
- # store the relevant tables coming out of the social vulnerability module
- self.set_tables(df=svi.pd_domain_scores_z, name="social_vulnerability_scores")
- # self.set_tables(df=svi.excluded_regions, name="social_vulnerability_nodataregions")
-
# Check if the exposure data exists
if self.exposure:
- # Link the SVI score to the exposure data
- self._tables["exposure"] # pd dataframe
- self.geoms["exposure"] # gpd dataframe
- self._tables["exposure"].sort_values("Object ID")
- self.geoms["exposure"].sort_values("Object ID")
- exposure_geoms = self._tables["exposure"].merge(
- self.geoms["exposure"], on="Object ID"
+ # First find the state(s) and county/counties where the exposure data is
+ # located in
+ us_states_counties = self.data_catalog.get_dataframe("us_states_counties")
+ counties, states = locate_from_bounding_box(self.exposure.bounding_box())
+ states_dict = list_of_states()
+ state_abbreviation = []
+ for state in states:
+ try:
+ state_abbreviation.append(states_dict[state])
+ except KeyError:
+ self.logger.warning(f"State {state} not found.")
+
+ county_numbers = get_us_county_numbers(counties, us_states_counties)
+
+ # Create SVI object
+ save_folder = str(Path(self.root) / "exposure" / "SVI")
+ svi = SocialVulnerabilityIndex(self.data_catalog, self.logger, save_folder)
+
+ # Call functionalities of SVI
+ svi.set_up_census_key(census_key)
+ svi.variable_code_csv_to_pd_df(codebook_fn)
+ svi.set_up_download_codes()
+ svi.set_up_state_code(state_abbreviation)
+ svi.download_census_data(year_data)
+ svi.rename_census_data("Census_code_withE", "Census_variable_name")
+ svi.identify_no_data()
+ svi.check_nan_variable_columns("Census_variable_name", "Indicator_code")
+ svi.check_zeroes_variable_rows()
+ translation_variable_to_indicator = svi.create_indicator_groups(
+ "Census_variable_name", "Indicator_code"
)
- exposure_geoms_gpd = gpd.GeoDataFrame(exposure_geoms)
+ svi.processing_svi_data(translation_variable_to_indicator)
+ svi.normalization_svi_data()
+ svi.domain_scores()
+ svi.composite_scores()
+ svi.match_geo_ID()
+ svi.download_shp_geom(year_data, county_numbers)
+ svi.merge_svi_data_shp()
+
+ # store the relevant tables coming out of the social vulnerability module
+ self.set_tables(df=svi.svi_data_shp, name="social_vulnerability_scores")
+ # TODO: Think about adding an indicator for missing data to the svi.svi_data_shp
+
+ # Link the SVI score to the exposure data
+ exposure_data = self.exposure.get_full_gdf(self.exposure.exposure_db)
+ exposure_data.sort_values("Object ID")
+
+ if svi.svi_data_shp.crs != exposure_data.crs:
+ svi.svi_data_shp.to_crs(crs=exposure_data.crs, inplace=True)
+
+ if save_all:
+ # Clean up the columns that are saved
+ cols_to_save = list(svi.svi_data_shp.columns)
+ cols_to_save.remove("GEO_ID")
+ cols_to_save.remove("GEOID_short")
+ cols_to_save.remove("NAME")
+ cols_to_save.remove("composite_SVI")
+ else:
+ # Only save the SVI_key_domain and composite_svi_z
+ cols_to_save = ["SVI_key_domain", "composite_svi_z", "geometry"]
+
svi_exp_joined = gpd.sjoin(
- exposure_geoms_gpd, svi.svi_data_shp, how="inner", op="within"
+ exposure_data, svi.svi_data_shp[cols_to_save], how="left"
)
- svi_exp_joined = svi_exp_joined.drop(columns="geometry")
+ svi_exp_joined.drop(columns=["geometry"], inplace=True)
svi_exp_joined = pd.DataFrame(svi_exp_joined)
- self._tables["exposure"] = svi_exp_joined
+ svi_exp_joined.rename(columns={"composite_svi_z": "SVI"}, inplace=True)
+ del svi_exp_joined["index_right"]
+ self.exposure.exposure_db = svi_exp_joined
+
+
+ def setup_equity_data(
+ self,
+ census_key: str,
+ year_data: int = None,
+ ):
+ """Setup the download procedure for equity data similarly to the SVI setup
- # exposure opnieuw opslaan in self._tables
+ Parameters
+ ----------
+ path_dataset : str
+ The path to a predefined dataset
+ census_key : str
+ The user's unique Census key that they got from the census.gov website
+ (https://api.census.gov/data/key_signup.html) to be able to download the
+ Census data
+ path : Union[str, Path]
+ The path to the codebook excel
+ """
+ # First find the state(s) and county/counties where the exposure data is
+ # located in
+ us_states_counties = self.data_catalog.get_dataframe("us_states_counties")
+ counties, states = locate_from_bounding_box(self.exposure.bounding_box())
+ states_dict = list_of_states()
+ state_abbreviation = []
+ for state in states:
+ try:
+ state_abbreviation.append(states_dict[state])
+ except KeyError:
+ self.logger.warning(f"State {state} not found.")
+
+ county_numbers = get_us_county_numbers(counties, us_states_counties)
+
+ # Create equity object
+ save_folder = str(Path(self.root) / "equity")
+ equity = EquityData(self.data_catalog, self.logger, save_folder)
+
+ # Call functionalities of equity
+ equity.set_up_census_key(census_key)
+ equity.variables_to_download()
+ equity.set_up_state_code(state_abbreviation)
+ equity.download_census_data(year_data)
+ equity.rename_census_data()
+ equity.match_geo_ID()
+ equity.download_shp_geom(year_data, county_numbers)
+ equity.merge_equity_data_shp()
+ equity.clean()
+
+ self.set_tables(df=equity.equity_data_shp, name="equity_data")
+
+ # Save the census block aggregation area data
+ block_groups = equity.get_block_groups()
+ self.set_geoms(block_groups, "aggregation_areas/block_groups")
+
+ # Update the aggregation label: census block
+ del self.exposure.exposure_db["Aggregation Label: Census Blockgroup"]
+ self.setup_aggregation_areas(
+ aggregation_area_fn=block_groups,
+ attribute_names="GEOID_short",
+ label_names="Aggregation Label: Census Blockgroup",
+ )
- # TODO: geometries toevoegen aan de dataset met API
- # we now use the shape download function by the census, the user needs to download their own shape data. They can download this from: https://www.census.gov/cgi-bin/geo/shapefiles/index.php
- # #wfs python get request -> geometries
+ def setup_aggregation_areas(
+ self,
+ aggregation_area_fn: Union[List[str], List[Path], List[gpd.GeoDataFrame], str, Path, gpd.GeoDataFrame],
+ attribute_names: Union[List[str], str],
+ label_names: Union[List[str], str],
+ ):
+ """_summary_
- # this link can be used: https://github.com/datamade/census
+ Parameters
+ ----------
+ aggregation_area_fn : Union[List[str], List[Path], str, Path]
+ Path(s) to the aggregation area(s).
+ attribute_names : Union[List[str], str]
+ Name of the attribute(s) to join.
+ label_names : Union[List[str], str]
+ The name that the new attribute will get in the exposure data.
+ """
+ exposure_gdf = self.exposure.get_full_gdf(self.exposure.exposure_db)
+ self.exposure.exposure_db = join_exposure_aggregation_areas(
+ exposure_gdf, aggregation_area_fn, attribute_names, label_names
+ )
+
+ # Set the additional_attributes_fn property to save the additional datasets
+ if isinstance(aggregation_area_fn, list):
+ the_type = type(aggregation_area_fn[0])
+ else:
+ the_type = type(aggregation_area_fn)
+ if the_type != gpd.GeoDataFrame:
+ # This copies data from one location to the root folder for the FIAT
+ # model, only use user-input data here (not the census blocks)
+ self.additional_attributes_fn = aggregation_area_fn
+
+ def setup_building_footprint(
+ self,
+ building_footprint_fn: Union[str, Path],
+ attribute_name: str,
+ ):
+ """_summary_
+
+ Parameters
+ ----------
+ exposure_gdf : gpd.GeoDataFrame
+ Exposure data to join the building footprints to as "BF_FID".
+ building_footprint_fn : Union[List[str], List[Path], str, Path]
+ Path(s) to the building footprint.
+ attribute_names : Union[List[str], str]
+ Name of the building footprint ID to join.
+ column_name: str = "BF_FID"
+ Name of building footprint in new exposure output
+ """
+
+ exposure_gdf = self.exposure.get_full_gdf(self.exposure.exposure_db)
+ self.exposure.exposure_db = join_exposure_building_footprints(
+ exposure_gdf,
+ building_footprint_fn,
+ attribute_name,
+ )
+
+ # Set the building_footprint_fn property to save the building footprints
+ self.building_footprint_fn = building_footprint_fn
# Update functions
def update_all(self):
@@ -607,21 +858,29 @@ def update_tables(self):
# Update the vulnerability data tables
if self.vulnerability:
- (
- df,
- self.vulnerability_metadata,
- ) = self.vulnerability.get_table_and_metadata()
- self.set_tables(df=df, name="vulnerability_curves")
+ self.set_tables(
+ df=self.vulnerability.get_table(), name="vulnerability_curves"
+ )
+
+ if not self.vf_ids_and_linking_df.empty:
+ # Add the vulnerability linking table to the tables object
+ self.set_tables(
+ df=self.vf_ids_and_linking_df, name="vulnerability_identifiers"
+ )
def update_geoms(self):
# Update the exposure data geoms
if self.exposure and "exposure" in self._tables:
- for i, geom in enumerate(self.exposure.exposure_geoms):
- file_suffix = i if i > 0 else ""
- self.set_geoms(geom=geom, name=f"exposure{file_suffix}")
+ for i, geom_and_name in enumerate(
+ zip(self.exposure.exposure_geoms, self.exposure.geom_names)
+ ):
+ geom = geom_and_name[0]
+ name = geom_and_name[1]
+
+ self.set_geoms(geom=geom, name=name)
self.set_config(
f"exposure.geom.file{str(i+1)}",
- f"./exposure/exposure{file_suffix}.gpkg",
+ f"./exposure/{name}.gpkg",
)
if not self.region.empty:
@@ -632,7 +891,8 @@ def update_maps(self):
# I/O
def read(self):
- """Method to read the complete model schematization and configuration from file."""
+ """Method to read the complete model schematization and configuration from
+ file."""
self.logger.info(f"Reading model data from {self.root}")
# Read the configuration file
@@ -654,13 +914,6 @@ def _configread(self, fn):
config = Config()
return config.load_file(fn)
- def check_path_exists(self, fn):
- """TODO: decide to use this or another function (check_file_exist in py)"""
- path = Path(fn)
- self.logger.debug(f"Reading file {str(path.name)}")
- if not fn.is_file():
- logging.warning(f"File {fn} does not exist!")
-
def read_tables(self):
"""Read the model tables for vulnerability and exposure data."""
if not self._write:
@@ -673,37 +926,23 @@ def read_tables(self):
if Path(vulnerability_fn).is_file():
self.logger.debug(f"Reading vulnerability table {vulnerability_fn}")
self.vulnerability = Vulnerability(fn=vulnerability_fn, logger=self.logger)
- (
- self._tables["vulnerability_curves"],
- _,
- ) = self.vulnerability.get_table_and_metadata()
else:
logging.warning(f"File {vulnerability_fn} does not exist!")
# Now with exposure
- exposure_fn = Path(self.root) / self.get_config("exposure.geom.csv")
+ exposure_fn = Path(self.root) / self.get_config("exposure.csv.file")
if Path(exposure_fn).is_file():
self.logger.debug(f"Reading exposure table {exposure_fn}")
self.exposure = ExposureVector(
crs=self.get_config("exposure.geom.crs"),
logger=self.logger,
unit=self.get_config("exposure.geom.unit"),
+ data_catalog=self.data_catalog, # TODO: See if this works also when no data catalog is provided
)
self.exposure.read_table(exposure_fn)
- self._tables["exposure"] = self.exposure.exposure_db
else:
logging.warning(f"File {exposure_fn} does not exist!")
- # If needed read other tables files like vulnerability identifiers
- # Comment if not needed - I usually use os rather than pathlib, change if you prefer
- fns = glob.glob(join(self.root, "*.csv"))
- if len(fns) > 0:
- for fn in fns:
- self.logger.info(f"Reading table {fn}")
- name = basename(fn).split(".")[0]
- tbl = pd.read_csv(fn)
- self.set_tables(tbl, name=name)
-
def read_geoms(self):
"""Read the geometries for the exposure data."""
if self.exposure:
@@ -717,13 +956,6 @@ def read_geoms(self):
]
self.exposure.read_geoms(exposure_fn)
- exposure_names = [f.stem for f in exposure_fn]
- for name, geom in zip(exposure_names, self.exposure.exposure_geoms):
- self.set_geoms(
- geom=geom,
- name=name,
- )
-
def write(self):
"""Method to write the complete model schematization and configuration to file."""
self.update_all()
@@ -733,10 +965,40 @@ def write(self):
self.write_config()
if self.maps:
self.write_maps(fn="hazard/{name}.nc")
+ if self.grid:
+ self.write_grid(fn="hazard/risk_map.nc")
if self.geoms:
self.write_geoms(fn="exposure/{name}.gpkg", driver="GPKG")
if self._tables:
self.write_tables()
+ if self.additional_attributes_fn:
+ folder = Path(self.root).joinpath("additional_attributes")
+ self.copy_datasets(self.additional_attributes_fn, folder)
+ if self.building_footprint_fn:
+ folder = Path(self.root).joinpath("exposure", "building_footprints")
+ self.copy_datasets(self.building_footprint_fn, folder)
+
+ def copy_datasets(
+ self, data: Union[list, str, Path], folder: Union[Path, str]
+ ) -> None:
+ """Copies datasets to another folder
+
+ Parameters
+ ----------
+ data : Union[list, str, Path]
+ _description_
+ folder : Union[Path, str]
+ _description_
+ """
+ # Create additional attributes folder in root
+ if not os.path.exists(folder):
+ os.makedirs(folder)
+
+ if isinstance(data, list):
+ for file in data:
+ shutil.copy2(file, folder)
+ elif isinstance(data, Path) or isinstance(data, str):
+ shutil.copy2(data, folder)
def write_tables(self) -> None:
if len(self._tables) == 0:
@@ -769,8 +1031,11 @@ def write_tables(self) -> None:
# The default location and save settings of the vulnerability curves
fn = "vulnerability/vulnerability_identifiers.csv"
kwargs = {"index": False}
- elif "social_vulnerability" in name:
- fn = f"exposure/{name}.csv"
+ elif name == "social_vulnerability_scores":
+ fn = f"exposure/SVI/{name}.csv"
+ kwargs = {"index": False}
+ elif name == "equity_data":
+ fn = f"equity/{name}.csv"
kwargs = {"index": False}
# Other, can also return an error or pass silently
@@ -798,20 +1063,19 @@ def _configwrite(self, fn):
@property
def vulnerability_curves(self) -> pd.DataFrame:
"""Returns a dataframe with the damage functions."""
- if "vulnerability_curves" in self._tables:
- vf = self._tables["vulnerability_curves"]
+ if self.vulnerability:
+ vf = self.vulnerability.get_table()
else:
vf = pd.DataFrame()
return vf
@property
- def vf_ids_and_linking_df(self) -> pd.DataFrame:
- """Returns a dataframe with the vulnerability identifiers and linking."""
- if "vulnerability_identifiers" in self._tables:
- vi = self._tables["vulnerability_identifiers"]
+ def vulnerability_metadata(self) -> List[str]:
+ if self.vulnerability:
+ vmeta = self.vulnerability.get_metadata()
else:
- vi = pd.DataFrame()
- return vi
+ vmeta = list()
+ return vmeta
def set_tables(self, df: pd.DataFrame, name: str) -> None:
"""Add to the tables variable.
diff --git a/hydromt_fiat/validation.py b/hydromt_fiat/validation.py
index 4507e46f..85bbfcdb 100644
--- a/hydromt_fiat/validation.py
+++ b/hydromt_fiat/validation.py
@@ -1,5 +1,6 @@
from pathlib import Path
+
def check_dir_exist(dir, name=None):
""" """
@@ -7,97 +8,8 @@ def check_dir_exist(dir, name=None):
raise TypeError(
f"The directory indicated by the '{name}' parameter does not exist."
)
-
-def check_file_exist(root, param_lst, name=None):
- root = Path(root)
- param_lst = [Path(p) for p in param_lst]
- for param_idx, param in enumerate(param_lst):
- if isinstance(param, dict):
- fn_lst = list(param.values())
- else:
- fn_lst = [param]
- for fn_idx, fn in enumerate(fn_lst):
- if not Path(fn).is_file():
- if root.joinpath(fn).is_file():
- if isinstance(param, dict):
- param_lst[param_idx][
- list(param.keys())[fn_idx]
- ] = root.joinpath(fn)
- else:
- param_lst[param_idx] = root.joinpath(fn)
- else:
- if isinstance(param, dict):
- param_lst[param_idx][list(param.keys())[fn_idx]] = Path(fn)
- else:
- param_lst[param_idx] = Path(fn)
- try:
- if isinstance(param, dict):
- assert isinstance(
- param_lst[param_idx][list(param.keys())[fn_idx]], Path
- )
- else:
- assert isinstance(param_lst[param_idx], Path)
- except AssertionError:
- raise TypeError(
- f"The file indicated by the '{name}' parameter does not"
- f" exist in the directory '{root}'."
- )
-
-#TODO: Improve this tool without calling model.get_congif(input_dir)
-# def check_file_exist(get_config, root, param_lst, name=None, input_dir=None):
-# root = Path(root)
-# param_lst = [Path(p) for p in param_lst]
-# for param_idx, param in enumerate(param_lst):
-# if isinstance(param, dict):
-# fn_lst = list(param.values())
-# else:
-# fn_lst = [param]
-# for fn_idx, fn in enumerate(fn_lst):
-# if not Path(fn).is_file():
-# if root.joinpath(fn).is_file():
-# if isinstance(param, dict):
-# param_lst[param_idx][
-# list(param.keys())[fn_idx]
-# ] = root.joinpath(fn)
-# else:
-# param_lst[param_idx] = root.joinpath(fn)
-# if input_dir is not None:
-# if get_config(input_dir).joinpath(fn).is_file():
-# if isinstance(param, dict):
-# param_lst[param_idx][
-# list(param.keys())[fn_idx]
-# ] = get_config(input_dir).joinpath(fn)
-# else:
-# param_lst[param_idx] = get_config(
-# input_dir
-# ).joinpath(fn)
-# else:
-# if isinstance(param, dict):
-# param_lst[param_idx][list(param.keys())[fn_idx]] = Path(fn)
-# else:
-# param_lst[param_idx] = Path(fn)
-# try:
-# if isinstance(param, dict):
-# assert isinstance(
-# param_lst[param_idx][list(param.keys())[fn_idx]], Path
-# )
-# else:
-# assert isinstance(param_lst[param_idx], Path)
-# except AssertionError:
-# if input_dir is None:
-# raise TypeError(
-# f"The file indicated by the '{name}' parameter does not"
-# f" exist in the directory '{root}'."
-# )
-# else:
-# raise TypeError(
-# f"The file indicated by the '{name}' parameter does not"
-# f" exist in either of the directories '{root}' or "
-# f"'{get_config(input_dir)}'."
-# )
def check_uniqueness(map_name_lst):
-
def check_duplicates(lst):
unique_elements = set()
for element in lst:
@@ -105,55 +17,11 @@ def check_duplicates(lst):
return True # Found a duplicate
unique_elements.add(element)
return False # No duplicates found
-
- check = check_duplicates(map_name_lst)
-
- if check:
- raise ValueError(
- f"The filenames of the hazard maps should be unique."
- )
-
-#TODO: Improve this tool without calling model. Just checking the maps names
-# def check_uniqueness(model, *args, file_type=None, filename=None):
-# """ """
-
-# args = list(args)
-# if len(args) == 1 and "." in args[0]:
-# args = args[0].split(".") + args[1:]
-# branch = args.pop(-1)
-# for key in args[::-1]:
-# branch = {key: branch}
-# if model.get_config(args[0], args[1]):
-# for key in model.staticmaps.data_vars:
-# if filename == key:
-# raise ValueError(
-# f"The filenames of the {file_type} maps should be unique."
-# )
-# if (
-# model.get_config(args[0], args[1], key)
-# == list(branch[args[0]][args[1]].values())[0]
-# ):
-# raise ValueError(f"Each model input layers must be unique.")
-
-def check_param_type(param, name=None, types=None):
- """ """
+ check = check_duplicates(map_name_lst)
- if not isinstance(param, list):
- raise TypeError(
- f"The '{name}_lst' parameter should be a of {list}, received a "
- f"{type(param)} instead."
- )
- for i in param:
- if not isinstance(i, types):
- if isinstance(types, tuple):
- types = " or ".join([str(j) for j in types])
- else:
- types = types
- raise TypeError(
- f"The '{name}' parameter should be a of {types}, received a "
- f"{type(i)} instead."
- )
+ if check:
+ raise ValueError(f"The filenames of the hazard maps should be unique.")
def get_param(param_lst, map_fn_lst, file_type, filename, i, param_name):
""" """
@@ -166,4 +34,4 @@ def get_param(param_lst, map_fn_lst, file_type, filename, i, param_name):
raise IndexError(
f"Could not derive the {param_name} parameter for {file_type} "
f"map: {filename}."
- )
\ No newline at end of file
+ )
diff --git a/hydromt_fiat/workflows/aggregation_areas.py b/hydromt_fiat/workflows/aggregation_areas.py
new file mode 100644
index 00000000..b9916942
--- /dev/null
+++ b/hydromt_fiat/workflows/aggregation_areas.py
@@ -0,0 +1,103 @@
+import geopandas as gpd
+from typing import List, Union
+from pathlib import Path
+
+def process_value(value):
+ if isinstance(value, list) and len(value) == 1:
+ return value[0]
+ elif isinstance(value, list) and len(value) > 1:
+ return ", ".join(value)
+ else:
+ return value
+
+def join_exposure_aggregation_multiple_areas(
+ exposure_gdf: gpd.GeoDataFrame,
+ aggregation_area_fn: Union[List[str], List[Path], List[gpd.GeoDataFrame]],
+ attribute_names: List[str],
+ label_names: List[str],
+) -> gpd.GeoDataFrame:
+ """_summary_
+
+ Parameters
+ ----------
+ exposure_gdf : gpd.GeoDataFrame
+ _description_
+ aggregation_area_fn : Union[List[str], List[Path]]
+ _description_
+ attribute_names : List[str]
+ _description_
+ label_names : List[str]
+ _description_
+
+ Returns
+ -------
+ gpd.GeoDataFrame
+ _description_
+ """
+ for file_path, attribute_name, label_name in zip(aggregation_area_fn, attribute_names, label_names):
+ if isinstance(file_path, str) or isinstance(file_path, Path):
+ aggregation_gdf = gpd.read_file(file_path)
+ else:
+ aggregation_gdf = file_path
+
+ ## check the projection of both gdf and if not match, reproject
+ if exposure_gdf.crs != aggregation_gdf.crs:
+ aggregation_gdf = aggregation_gdf.to_crs(exposure_gdf.crs)
+
+
+ assert attribute_name in aggregation_gdf.columns, f"Attribute {attribute_name} not found in {file_path}"
+
+
+ # If you overwrite the exposure_gdf with the joined data, you can append all
+ # aggregation areas to the same exposure_gdf
+ exposure_gdf = gpd.sjoin(
+ exposure_gdf,
+ aggregation_gdf[["geometry", attribute_name]],
+ predicate="intersects",
+ how="left",
+ )
+
+ assert exposure_gdf["Object ID"].is_unique, "Error! Aggregation polygons overlap! Please clean your aggregation data from overlapping features."
+
+ # Rename the 'aggregation_attribute' column to 'new_column_name'. Put in
+ # Documentation that the order the user put the label name must be the order of the gdf
+ exposure_gdf.rename(columns={attribute_name: label_name}, inplace=True)
+
+ ##remove the index_right column
+ if "index_right" in exposure_gdf.columns:
+ del exposure_gdf["index_right"]
+
+ return exposure_gdf
+
+
+def join_exposure_aggregation_areas(
+ exposure_gdf: gpd.GeoDataFrame,
+ aggregation_area_fn: Union[List[str], List[Path], List[gpd.GeoDataFrame], str, Path, gpd.GeoDataFrame],
+ attribute_names: Union[List[str], str],
+ label_names: Union[List[str], str],
+) -> gpd.GeoDataFrame:
+ """Join aggregation area labels to the exposure data.
+
+ Parameters
+ ----------
+ exposure_gdf : gpd.GeoDataFrame
+ Exposure data to join the aggregation areas to as `label_names`.
+ aggregation_area_fn : Union[List[str], List[Path], str, Path]
+ Path(s) to the aggregation area(s).
+ attribute_names : Union[List[str], str]
+ Name of the attribute(s) to join.
+ label_names : Union[List[str], str]
+ Name of the label(s) to join.
+ """
+ if isinstance(aggregation_area_fn, str) or isinstance(aggregation_area_fn, Path) or isinstance(aggregation_area_fn, gpd.GeoDataFrame):
+ aggregation_area_fn = [aggregation_area_fn]
+ if isinstance(attribute_names, str):
+ attribute_names = [attribute_names]
+ if isinstance(label_names, str):
+ label_names = [label_names]
+
+ exposure_gdf = join_exposure_aggregation_multiple_areas(exposure_gdf, aggregation_area_fn, attribute_names, label_names)
+
+ # Remove the geometry column from the exposure_gdf to return a dataframe
+ del exposure_gdf["geometry"]
+ return exposure_gdf
diff --git a/hydromt_fiat/workflows/building_footprints.py b/hydromt_fiat/workflows/building_footprints.py
new file mode 100644
index 00000000..a83d5a19
--- /dev/null
+++ b/hydromt_fiat/workflows/building_footprints.py
@@ -0,0 +1,78 @@
+import geopandas as gpd
+from typing import Union
+from pathlib import Path
+import math
+from hydromt import gis_utils
+
+
+def process_value(value):
+ if isinstance(value, list) and len(value) == 1:
+ return value[0]
+ elif isinstance(value, list) and len(value) > 1:
+ return ", ".join(value)
+ else:
+ return value
+
+def join_exposure_building_footprints(
+ exposure_gdf: gpd.GeoDataFrame,
+ building_footprint_fn: Union[str, Path],
+ attribute_name: str,
+ column_name: str = "BF_FID",
+) -> gpd.GeoDataFrame:
+ """Join building footprints to the exposure data.
+
+ Parameters
+ ----------
+ exposure_gdf : gpd.GeoDataFrame
+ Exposure data to join the building footprints to as "BF_FID".
+ building_footprint_fn : Union[str, Path]
+ Path(s) to the building footprint.
+ attribute_name : str
+ Name of the building footprint ID to join.
+ column_name : str = "BF_FID"
+ Name of building footprint in new exposure output
+
+
+ Returns
+ -------
+ gpd.GeoDataFrame
+ An updated exposure GeoDataFrame including the building footprints.
+ """
+ # Read the building footprints file
+ bf_gdf = gpd.read_file(building_footprint_fn)
+
+ # Check if the attribute is in the building footprint file
+ assert attribute_name in bf_gdf.columns, f"Attribute {attribute_name} not found in {building_footprint_fn}"
+
+ # Check for unique identifier attribute
+ assert bf_gdf[attribute_name].is_unique, "Building footprint ID returns duplicates. Building footprint ID (attribute_name) should be unique."
+
+ # Change the column type to be an integer
+ bf_gdf[attribute_name] = bf_gdf[attribute_name].astype("int")
+
+ # check the projection of both gdf and if not match, reproject
+ if exposure_gdf.crs != bf_gdf.crs:
+ bf_gdf = bf_gdf.to_crs(exposure_gdf.crs)
+
+ # Spatially join the exposure and building footprint data
+ joined_gdf = gpd.sjoin(
+ exposure_gdf,
+ bf_gdf[["geometry", attribute_name]],
+ predicate="intersects",
+ how="right",
+ )
+
+ # Aggregate the data if duplicates exist
+ aggregated = (
+ joined_gdf.groupby("Object ID")[attribute_name].agg(list).reset_index()
+ )
+ exposure_gdf = exposure_gdf.merge(aggregated, on="Object ID", how = 'left')
+
+ # Create a string from the list of values in the duplicated aggregation area
+ # column
+ exposure_gdf[attribute_name] = exposure_gdf[attribute_name].apply(process_value)
+
+ # Rename the 'aggregation_attribute' column to 'new_column_name'
+ exposure_gdf.rename(columns={attribute_name: column_name}, inplace=True)
+
+ return exposure_gdf
diff --git a/hydromt_fiat/workflows/equity_data.py b/hydromt_fiat/workflows/equity_data.py
new file mode 100644
index 00000000..1d494cf6
--- /dev/null
+++ b/hydromt_fiat/workflows/equity_data.py
@@ -0,0 +1,213 @@
+from census import Census # install in your environment using pip install Census
+from us import states # install in your environment using pip install us
+from hydromt.data_catalog import DataCatalog
+from logging import Logger
+import pandas as pd
+import geopandas as gpd
+from urllib.request import urlopen
+from io import BytesIO
+from zipfile import ZipFile
+from pathlib import Path
+from typing import List
+import shutil
+from hydromt_fiat.workflows.social_vulnerability_index import list_of_states
+
+
+class EquityData:
+ def __init__(self, data_catalog: DataCatalog, logger: Logger, save_folder: str):
+ self.data_catalog = data_catalog
+ self.save_folder = save_folder
+ self.census_key = Census
+ self.download_codes = {}
+ self.state_fips = []
+ self.pd_census_data = pd.DataFrame()
+ self.codebook = pd.DataFrame()
+ self.indicator_groups = {}
+ self.processed_census_data = pd.DataFrame()
+
+ self.pd_domain_scores_geo = pd.DataFrame()
+ self.logger = logger
+ self.equity_data_shp = gpd.GeoDataFrame()
+ self.block_groups = gpd.GeoDataFrame()
+
+ def set_up_census_key(self, census_key: str):
+ """The Census key can be inputted in the ini file.
+ This is a unique key that every user needs to specify to download census data
+
+ Parameters
+ ----------
+ census_key : str
+ The unique key a user gets from the census download website (an API token basically)
+ """
+
+ self.census_key = Census(census_key)
+ self.logger.info(
+ f"your census key {census_key} is used to download data from the Census website "
+ )
+
+ def set_up_state_code(self, state_abbreviation: List[str]):
+ """download census data for a state
+
+ Parameters
+ ----------
+ state_abbreviation : str
+ Abbreviation of the state for which you want to set up the census data download
+ """
+ states_done = []
+ for state in state_abbreviation:
+ if state not in states_done:
+ self.logger.info(f"The states for which census data will be downloaded is: {list_of_states(inverted=False)[state]}")
+ state_obj = getattr(states, state)
+ self.state_fips.append(state_obj.fips)
+ states_done.append(state)
+
+ def variables_to_download(self):
+ self.download_variables = ['B01003_001E', 'B19301_001E', 'NAME', 'GEO_ID'] # TODO: later make this a user input?
+
+ def download_census_data(self, year_data):
+ """download the census data
+ it is possible to also make the county, tract and blockgroup flexible so that a user could specify exactly what to download
+ """
+ dfs = []
+ for sf in self.state_fips:
+ download_census_codes = self.census_key.acs5.state_county_blockgroup(
+ fields=self.download_variables,
+ state_fips=sf,
+ county_fips="*",
+ tract="*",
+ blockgroup="*",
+ year=year_data
+ )
+ dfs.append(pd.DataFrame(download_census_codes))
+
+ self.pd_census_data = pd.concat(dfs)
+ self.logger.info(
+ "The equity data was succesfully downloaded from the Census website"
+ )
+
+ def rename_census_data(self):
+ """renaming the columns so that they have variable names instead of variable codes as their headers
+
+ Parameters
+ ----------
+ from_name : str
+ The name that you want to replace
+ to_name : str
+ The name to replace with
+ """
+ name_dict = {'B01003_001E': "TotalPopulationBG", 'B19301_001E': "PerCapitaIncomeBG"} # TODO: Later make this a user input?
+ self.pd_census_data = self.pd_census_data.rename(columns=name_dict)
+
+ def match_geo_ID(self):
+ """Matches GEO_IDs for the block groups"""
+ self.pd_domain_scores_geo = self.pd_census_data.copy()
+ self.pd_domain_scores_geo[
+ "GEO_ID"
+ ] = None # Create a new column 'GEO_ID' with initial values set to None
+
+ for index, value in enumerate(self.pd_domain_scores_geo["NAME"]):
+ if value in self.pd_census_data["NAME"].values:
+ matching_row = self.pd_census_data.loc[
+ self.pd_census_data["NAME"] == value
+ ]
+ geo_id = matching_row["GEO_ID"].values[
+ 0
+ ] # Assuming there's only one matching row, extract the GEO_ID value
+ self.pd_domain_scores_geo.at[
+ index, "GEO_ID"
+ ] = geo_id # Assign the GEO_ID value to the corresponding row in self.pd_domain_scores_geo
+ self.pd_domain_scores_geo["GEOID_short"] = (
+ self.pd_domain_scores_geo["GEO_ID"].str.split("US").str[1]
+ )
+
+ def download_and_unzip(self, url, extract_to='.'):
+ """function to download the shapefile data from census tiger website
+
+ Parameters
+ ----------
+ url : webpage
+ URL to census website (TIGER) to download shapefiles for visualisation
+ extract_to : str, optional
+ _description_, by default '.'
+ """
+
+ try:
+ http_response = urlopen(url)
+ zipfile = ZipFile(BytesIO(http_response.read()))
+ zipfile.extractall(path=extract_to)
+ except Exception as e:
+ self.logger.warning(f"Error during download and unzip: {e}")
+
+ def download_shp_geom(self, year_data: int, counties: List[str]):
+ """Downloading the shapefiles from the government Tiger website
+
+ Parameters
+ ----------
+ year_data : int
+ The year for which you want to download the census data and the corresponding shapefiles (for geometry)
+ counties : List[str]
+ A list of county codes in which your area of interest lies
+ """
+ block_groups_list = []
+ for sf, county in zip(self.state_fips, counties):
+ # Download shapefile of blocks
+ if year_data == 2022:
+ url = f"https://www2.census.gov/geo/tiger/TIGER_RD18/LAYER/FACES/tl_rd22_{sf}{county}_faces.zip"
+ elif year_data == 2021:
+ url = f"https://www2.census.gov/geo/tiger/TIGER2021/FACES/tl_2021_{sf}{county}_faces.zip"
+ elif year_data == 2020:
+ url = f"https://www2.census.gov/geo/tiger/TIGER2020PL/LAYER/FACES/tl_2020_{sf}{county}_faces.zip"
+ else:
+ self.logger.warning(f"Year {year_data} not available from 'https://www2.census.gov/geo/tiger'")
+ return
+
+ # Save shapefiles
+ folder = Path(self.save_folder) / "shapefiles" / (sf + county) / str(year_data)
+ self.logger.info(f"Downloading the county shapefile for {str(year_data)}")
+ self.download_and_unzip(url, folder)
+ shapefiles = list(Path(folder).glob("*.shp"))
+ if shapefiles:
+ shp = gpd.read_file(shapefiles[0])
+ self.logger.info("The shapefile was downloaded")
+ else:
+ self.logger.warning("No county shapefile found in the directory.")
+
+ # Dissolve shapefile based on block groups
+ code = "20"
+ attrs = ["STATEFP", "COUNTYFP", "TRACTCE", "BLKGRPCE"]
+ attrs = [attr + code for attr in attrs]
+
+ block_groups_shp = shp.dissolve(by=attrs, as_index=False)
+ block_groups_shp = block_groups_shp[attrs + ["geometry"]]
+ block_groups_shp["GEO_ID"] = "1500000US" + block_groups_shp['STATEFP' + code].astype(str) + block_groups_shp['COUNTYFP' + code].astype(str) + block_groups_shp['TRACTCE' + code].astype(str) + block_groups_shp['BLKGRPCE' + code].astype(str)
+ block_groups_shp["GEOID_short"] = block_groups_shp["GEO_ID"].str.split("US").str[1]
+ block_groups_list.append(block_groups_shp)
+
+ self.block_groups = gpd.GeoDataFrame(pd.concat(block_groups_list))
+
+ # Delete the shapefile, that is not used anymore
+ shp_folder = Path(self.save_folder) / "shapefiles"
+ try:
+ shutil.rmtree(shp_folder)
+ except Exception as e:
+ self.logger.warning(f"Folder {shp_folder} cannot be removed: {e}")
+
+ def merge_equity_data_shp(self):
+ """Merges the geometry data with the equity_data downloaded"""
+ self.equity_data_shp = self.pd_domain_scores_geo.merge(self.block_groups[["GEO_ID", "geometry"]], on="GEO_ID", how="left")
+ self.equity_data_shp = gpd.GeoDataFrame(self.equity_data_shp)
+
+ # Delete the rows that do not have a geometry column
+ self.equity_data_shp = self.equity_data_shp.loc[self.equity_data_shp["geometry"].notnull()]
+
+ self.equity_data_shp = self.equity_data_shp.to_crs(epsg=4326)
+ self.logger.info(
+ "The geometry information was successfully added to the equity information"
+ )
+
+ def get_block_groups(self):
+ return self.block_groups[["GEOID_short", "geometry"]]
+
+ def clean(self):
+ """Removes unnecessary columns"""
+ self.equity_data_shp = self.equity_data_shp[["GEOID_short", "TotalPopulationBG", "PerCapitaIncomeBG"]]
\ No newline at end of file
diff --git a/hydromt_fiat/workflows/exposure_raster.py b/hydromt_fiat/workflows/exposure_raster.py
index a94cf611..9c4484dc 100644
--- a/hydromt_fiat/workflows/exposure_raster.py
+++ b/hydromt_fiat/workflows/exposure_raster.py
@@ -8,6 +8,7 @@
logger = logging.getLogger(__name__)
+
### TO BE UPDATED ###
class ExposureRaster(Exposure):
def setup_buildings_value(
diff --git a/hydromt_fiat/workflows/exposure_vector.py b/hydromt_fiat/workflows/exposure_vector.py
index 0a23a6f4..14487a0d 100644
--- a/hydromt_fiat/workflows/exposure_vector.py
+++ b/hydromt_fiat/workflows/exposure_vector.py
@@ -7,19 +7,36 @@
import numpy as np
import pandas as pd
from hydromt.data_catalog import DataCatalog
-from hydromt.gis_utils import utm_crs
from pyproj import CRS
from hydromt_fiat.data_apis.national_structure_inventory import get_assets_from_nsi
from hydromt_fiat.data_apis.open_street_maps import (
get_assets_from_osm,
get_landuse_from_osm,
+ get_roads_from_osm,
)
-from .damage_values import preprocess_jrc_damage_values, preprocess_hazus_damage_values
-from .exposure import Exposure
-from .utils import detect_delimiter
-from .vulnerability import Vulnerability
+from hydromt_fiat.workflows.damage_values import (
+ preprocess_jrc_damage_values,
+ preprocess_hazus_damage_values,
+)
+from hydromt_fiat.workflows.exposure import Exposure
+from hydromt_fiat.workflows.utils import detect_delimiter
+from hydromt_fiat.workflows.vulnerability import Vulnerability
+from hydromt_fiat.workflows.gis import (
+ get_area,
+ sjoin_largest_area,
+ get_crs_str_from_gdf,
+ join_spatial_data,
+ ground_elevation_from_dem,
+)
+
+from hydromt_fiat.workflows.roads import (
+ get_max_potential_damage_roads,
+ get_road_lengths,
+)
+
+from hydromt_fiat.workflows.aggregation_areas import join_exposure_aggregation_areas
class ExposureVector(Exposure):
@@ -74,8 +91,13 @@ def __init__(
)
self.exposure_db = pd.DataFrame()
self.exposure_geoms = list() # A list of GeoDataFrames
- self.source = gpd.GeoDataFrame()
self.unit = unit
+ self._geom_names = list() # A list of (original) names of the geometry (files)
+
+ def bounding_box(self):
+ if len(self.exposure_geoms) > 0:
+ gdf = gpd.GeoDataFrame(pd.concat(self.exposure_geoms, ignore_index=True))
+ return gdf.total_bounds
def read_table(self, fn: Union[str, Path]):
"""Read the Delft-FIAT exposure data.
@@ -102,13 +124,15 @@ def read_geoms(self, fn: Union[List[str], List[Path], str, Path]):
fn = [fn]
for f in fn:
+ self.set_geom_names(Path(f).stem)
self.set_exposure_geoms(gpd.read_file(f, engine="pyogrio"))
- def setup_from_single_source(
+ def setup_buildings_from_single_source(
self,
source: Union[str, Path],
ground_floor_height: Union[int, float, str, Path, None],
extraction_method: str,
+ ground_elevation_file: Union[int, float, str, Path, None] = None,
) -> None:
"""Set up asset locations and other available data from a single source.
@@ -170,15 +194,89 @@ def setup_from_single_source(
if len(self.exposure_db.index) != len(set(self.exposure_db["Object ID"])):
self.exposure_db["Object ID"] = range(1, len(self.exposure_db.index) + 1)
- self.setup_ground_floor_height(ground_floor_height)
+ # Set the ground floor height if not yet set
+ if ground_floor_height != source:
+ self.setup_ground_floor_height(ground_floor_height)
# Set the extraction method
self.setup_extraction_method(extraction_method)
- # Set the geoms from the X and Y coordinates
- self.set_exposure_geoms_from_xy()
+ # Set the exposure_geoms
+ self.set_exposure_geoms(
+ gpd.GeoDataFrame(self.exposure_db[["Object ID", "geometry"]], crs=self.crs)
+ )
+
+ # Set the name to the geom_names
+ self.set_geom_names("buildings")
+
+ # Set the ground floor height if not yet set
+ # TODO: Check a better way to access to to the geometries, self.empousure_geoms is a list an not a geodataframe
+ if ground_elevation_file is not None:
+ self.setup_ground_elevation(
+ ground_elevation_file,
+ )
+
+ # Remove the geometry column from the exposure_db
+ if "geometry" in self.exposure_db:
+ del self.exposure_db["geometry"]
+
+ def setup_roads(
+ self,
+ source: Union[str, Path],
+ road_damage: Union[str, Path, int],
+ road_types: Union[str, List[str], bool] = True,
+ ):
+ self.logger.info("Setting up roads...")
+ if str(source).upper() == "OSM":
+ polygon = self.region["geometry"].values[
+ 0
+ ] # TODO check if this works each time
+ roads = get_roads_from_osm(polygon, road_types)
+
+ if roads.empty:
+ self.logger.warning(
+ "No roads found in the selected region from source " f"{source}."
+ )
+
+ # Rename the columns to FIAT names
+ roads.rename(
+ columns={"highway": "Secondary Object Type", "name": "Object Name"},
+ inplace=True,
+ )
+
+ # Add an Object ID
+ roads["Object ID"] = range(1, len(roads.index) + 1)
+ else:
+ roads = self.data_catalog.get_geodataframe(source, geom=self.region)
+ # add the function to segmentize the roads into certain segments
+
+ # Add the Primary Object Type and damage function, which is currently not set up to be flexible
+ roads["Primary Object Type"] = "roads"
+ roads["Damage Function: Structure"] = "roads"
+
+ self.logger.info(
+ "The damage function 'roads' is selected for all of the structure damage to the roads."
+ )
+
+ if isinstance(road_damage, str):
+ # Add the max potential damage and the length of the segments to the roads
+ road_damage = self.data_catalog.get_dataframe(road_damage)
+ roads[
+ ["Max Potential Damage: Structure", "Segment Length [m]"]
+ ] = get_max_potential_damage_roads(roads, road_damage)
+ elif isinstance(road_damage, int):
+ roads["Segment Length [m]"] = get_road_lengths(roads)
+ roads["Max Potential Damage: Structure"] = road_damage
- def setup_from_multiple_sources(
+ self.set_exposure_geoms(roads[["Object ID", "geometry"]])
+ self.set_geom_names("roads")
+
+ del roads["geometry"]
+
+ # Update the exposure_db
+ self.exposure_db = pd.concat([self.exposure_db, roads]).reset_index(drop=True)
+
+ def setup_buildings_from_multiple_sources(
self,
asset_locations: Union[str, Path],
occupancy_source: Union[str, Path],
@@ -188,13 +286,20 @@ def setup_from_multiple_sources(
occupancy_type_field: Union[str, None] = None,
damage_types: Union[List[str], None] = None,
country: Union[str, None] = None,
+ attr_name_gfh: Union[str, List[str], None] = None,
+ method_gfh: Union[str, List[str], None] = "nearest",
+ max_dist: Union[int, float, None] = 10,
+ ground_elevation_file: Union[int, float, str, Path, None] = None,
):
self.logger.info("Setting up exposure data from multiple sources...")
self.setup_asset_locations(asset_locations)
self.setup_occupancy_type(occupancy_source, occupancy_type_field)
self.setup_max_potential_damage(max_potential_damage, damage_types, country)
- self.setup_ground_floor_height(ground_floor_height)
+ self.setup_ground_floor_height(
+ ground_floor_height, attr_name_gfh, method_gfh, max_dist
+ )
self.setup_extraction_method(extraction_method)
+ self.setup_ground_elevation(ground_elevation_file)
def setup_asset_locations(self, asset_locations: str) -> None:
"""Set up the asset locations (points or polygons).
@@ -208,12 +313,13 @@ def setup_asset_locations(self, asset_locations: str) -> None:
"""
self.logger.info("Setting up asset locations...")
if str(asset_locations).upper() == "OSM":
- polygon = self.region.iloc[0][0]
+ polygon = self.region.iloc[0].values[0]
assets = get_assets_from_osm(polygon)
if assets.empty:
self.logger.warning(
- f"No assets found in the selected region from source {asset_locations}."
+ "No assets found in the selected region from source "
+ f"{asset_locations}."
)
# Rename the osmid column to Object ID
@@ -224,8 +330,7 @@ def setup_asset_locations(self, asset_locations: str) -> None:
)
# Set the CRS of the exposure data
- source_data_authority = assets.crs.to_authority()
- self.crs = source_data_authority[0] + ":" + source_data_authority[1]
+ self.crs = get_crs_str_from_gdf(assets.crs)
# Check if the 'Object ID' column exists and if so, is unique
if "Object ID" not in assets.columns:
@@ -235,7 +340,28 @@ def setup_asset_locations(self, asset_locations: str) -> None:
assets["Object ID"] = range(1, len(assets.index) + 1)
# Set the asset locations to the geometry variable (self.exposure_geoms)
+ # and set the geom name
self.set_exposure_geoms(assets)
+ self.set_geom_names("buildings")
+
+ def set_geom_names(self, name: str) -> None:
+ """Append a name to the list of geometry names `geom_names`."""
+ self.logger.info(f"Setting geometry name to {name}...")
+ self._geom_names.append(name)
+
+ @property
+ def geom_names(self) -> List[str]:
+ """Returns a list with the geom names."""
+ if len(self._geom_names) > 0 and len(self.exposure_geoms) > 0:
+ return self._geom_names
+ elif len(self._geom_names) == 0 and len(self.exposure_geoms) == 1:
+ return ["exposure"]
+ else:
+ self.logger.warning(
+ "No geometry names found, returning a list with the default names "
+ "'exposure_X'."
+ )
+ return [f"exposure_{i}" for i in range(len(self.exposure_geoms))]
def set_exposure_geoms(self, gdf: gpd.GeoDataFrame) -> None:
"""Append a GeoDataFrame to the exposure geometries `exposure_geoms`."""
@@ -270,12 +396,39 @@ def setup_occupancy_type(self, occupancy_source: str, occupancy_attr: str) -> No
# Spatially join the exposure data with the occupancy map
if len(self.exposure_geoms) == 1:
- self.exposure_db = gpd.sjoin(
- self.exposure_geoms[0],
- occupancy_map[to_keep],
- how="left",
- op="intersects",
+ # If there is only one exposure geom, do the spatial join with the
+ # occupancy_map. Only take the largest overlapping object from the
+ # occupancy_map.
+ gdf = sjoin_largest_area(self.exposure_geoms[0], occupancy_map[to_keep])
+
+ # Remove the objects that do not have a Primary Object Type, that were not
+ # overlapping with the land use map, or that had a land use type of 'nan'.
+ nr_without_primary_object_type = len(
+ gdf.loc[gdf["Primary Object Type"] == ""].index
)
+ if nr_without_primary_object_type > 0:
+ self.logger.warning(
+ f"{nr_without_primary_object_type} objects do not have a Primary Object "
+ "Type and will be removed from the exposure data."
+ )
+ gdf = gdf.loc[gdf["Primary Object Type"] != ""]
+
+ nr_without_landuse = len(gdf.loc[gdf["Primary Object Type"].isna()].index)
+ if nr_without_landuse > 0:
+ self.logger.warning(
+ f"{nr_without_landuse} objects were not overlapping with the "
+ "land use data and will be removed from the exposure data."
+ )
+ gdf = gdf.loc[gdf["Primary Object Type"].notna()]
+
+ # Update the exposure geoms
+ self.exposure_geoms[0] = gdf[["Object ID", "geometry"]]
+
+ # Remove the geometry column from the exposure database
+ del gdf["geometry"]
+
+ # Update the exposure database
+ self.exposure_db = gdf.copy()
else:
print(
"NotImplemented the spatial join of the exposure data with the "
@@ -283,36 +436,6 @@ def setup_occupancy_type(self, occupancy_source: str, occupancy_attr: str) -> No
)
NotImplemented
- # Remove the objects that do not have a Primary Object Type, that were not
- # overlapping with the land use map, or that had a land use type of 'nan'.
- nr_without_primary_object_type = len(
- self.exposure_db.loc[self.exposure_db["Primary Object Type"] == ""].index
- )
- self.logger.warning(
- f"{nr_without_primary_object_type} objects do not have a Primary Object "
- "Type and will be removed from the exposure data."
- )
- self.exposure_db = self.exposure_db.loc[
- self.exposure_db["Primary Object Type"] != ""
- ]
-
- nr_without_landuse = len(
- self.exposure_db.loc[self.exposure_db["Primary Object Type"].isna()].index
- )
- self.logger.warning(
- f"{nr_without_landuse} objects were not overlapping with the "
- "land use data and will be removed from the exposure data."
- )
- self.exposure_db = self.exposure_db.loc[
- self.exposure_db["Primary Object Type"].notna()
- ]
-
- # Update the exposure geoms
- self.exposure_geoms[0] = self.exposure_db[["Object ID", "geometry"]]
-
- # Remove the geometry column from the exposure database
- del self.exposure_db["geometry"]
-
def setup_occupancy_type_from_osm(self) -> None:
# We assume that the OSM land use data contains an attribute 'landuse' that
# contains the land use type.
@@ -378,7 +501,9 @@ def setup_occupancy_type_from_osm(self) -> None:
occupancy_map["Primary Object Type"] = occupancy_map[occupancy_attribute].map(
landuse_to_jrc_mapping
)
- occupancy_map["Secondary Object Type"] = occupancy_map[occupancy_attribute]
+ occupancy_map.rename(
+ columns={occupancy_attribute: "Secondary Object Type"}, inplace=True
+ )
return occupancy_map
@@ -390,16 +515,58 @@ def setup_aggregation_labels(self):
def setup_ground_floor_height(
self,
- ground_floor_height: Union[int, float],
+ ground_floor_height: Union[int, float, None, str, Path, List[str], List[Path]],
+ attr_name: Union[str, List[str], None] = None,
+ method: Union[str, List[str], None] = "nearest",
+ max_dist: float = 10,
) -> None:
- # Set the ground floor height column.
- # If the Ground Floor Height is input as a number, assign all objects with
- # the same Ground Floor Height.
+ """Set the ground floor height of the exposure data. This function overwrites
+ the existing Ground Floor Height column if it already exists.
+
+ Parameters
+ ----------
+ ground_floor_height : Union[int, float, None, str, Path, List[str], List[Path]]
+ A number to set the Ground Floor Height of all assets to the same value, a
+ path to a file that contains the Ground Floor Height of each asset, or a
+ list of paths to files that contain the Ground Floor Height of each asset,
+ in the order of preference (the first item in the list gets the highest
+ priority in assigning the values).
+ attr_name : Union[str, List[str]], optional
+ The name of the attribute that contains the Ground Floor Height in the
+ file(s) that are submitted. If multiple `ground_floor_height` files are
+ submitted, the attribute names are linked to the files in the same order as
+ the files are submitted. By default None.
+ method : Union[str, List[str]], optional
+ The method to use to assign the Ground Floor Height to the assets. If
+ multiple `ground_floor_height` files are submitted, the methods are linked
+ to the files in the same order as the files are submitted. The method can
+ be either 'nearest' (nearest neighbor) or 'intersection'. By default
+ 'nearest'.
+ max_dist : float
+ The maximum distance for the nearest join measured in meters, by default
+ set to 10 meters.
+ """
if ground_floor_height:
- if type(ground_floor_height) == int or type(ground_floor_height) == float:
+ if isinstance(ground_floor_height, int) or isinstance(
+ ground_floor_height, float
+ ):
+ # If the Ground Floor Height is input as a number, assign all objects with
+ # the same Ground Floor Height.
self.exposure_db["Ground Floor Height"] = ground_floor_height
- elif type(ground_floor_height) == str:
- # TODO: implement the option to add the ground floor height from a file.
+ elif isinstance(ground_floor_height, str) or isinstance(
+ ground_floor_height, Path
+ ):
+ # A single file is used to assign the ground floor height to the assets
+ gfh = self.data_catalog.get_geodataframe(ground_floor_height)
+ gdf = self.get_full_gdf(self.exposure_db)
+ gdf = join_spatial_data(
+ gdf, gfh, attr_name, method, max_dist, self.logger
+ )
+ self.exposure_db = self._set_values_from_other_column(
+ gdf, "Ground Floor Height", attr_name
+ )
+ elif isinstance(ground_floor_height, list):
+ # Multiple files are used to assign the ground floor height to the assets
NotImplemented
else:
# Set the Ground Floor Height to 0 if the user did not specify any
@@ -408,59 +575,125 @@ def setup_ground_floor_height(
def setup_max_potential_damage(
self,
- max_potential_damage: Union[List[float], float],
+ max_potential_damage: Union[
+ int, float, str, Path, List[str], List[Path], pd.DataFrame
+ ] = None,
damage_types: Union[List[str], str, None] = None,
+ attr_name: Union[str, List[str], None] = None,
+ method: Union[str, List[str], None] = "nearest",
+ max_dist: float = 10,
country: Union[str, None] = None,
- ):
- if max_potential_damage == "jrc_damage_values":
- damage_source = self.data_catalog.get_dataframe(max_potential_damage)
- if country is None:
- country = "World"
- self.logger.warning(
- f"No country specified, using the '{country}' JRC damage values."
- )
-
- damage_values = preprocess_jrc_damage_values(damage_source, country)
+ ) -> None:
+ """Setup the max potential damage column of the exposure data in various ways.
- elif max_potential_damage == "hazus_max_potential_damages":
- damage_source = self.data_catalog.get_dataframe(max_potential_damage)
- damage_values = preprocess_hazus_damage_values(damage_source)
- else:
- NotImplemented
- # TODO: Implement other ways of setting the max potential damage, e.g. from a
- # vector file or from a table.
-
- # Calculate the area of each object
- gdf = self.get_full_gdf(self.exposure_db)[["Primary Object Type", "geometry"]]
- if gdf.crs.is_geographic:
- # If the CRS is geographic, reproject to the nearest UTM zone
- nearest_utm = utm_crs(gdf.total_bounds)
- gdf_utm = gdf.to_crs(nearest_utm)
- gdf["area"] = gdf_utm["geometry"].area
- elif gdf.crs.is_projected:
- # If the CRS is projected, calculate the area in the same CRS
- gdf["area"] = gdf["geometry"].area
-
- # Set the damage values to the exposure data
+ Parameters
+ ----------
+ max_potential_damage : Union[int, float, str, Path, List[str], List[Path], pd.DataFrame], optional
+ _description_, by default None
+ damage_types : Union[List[str], str, None], optional
+ _description_, by default None
+ country : Union[str, None], optional
+ _description_, by default None
+ attr_name : Union[str, List[str], None], optional
+ _description_, by default None
+ method : Union[str, List[str], None], optional
+ _description_, by default "nearest"
+ max_dist : float, optional
+ _description_, by default 10
+ """
if damage_types is None:
damage_types = ["total"]
- for damage_type in damage_types:
- # Calculate the maximum potential damage for each object and per damage type
- try:
+ if isinstance(damage_types, str):
+ damage_types = [damage_types]
+
+ if isinstance(max_potential_damage, pd.DataFrame):
+ self.update_max_potential_damage(
+ updated_max_potential_damages=max_potential_damage
+ )
+ elif isinstance(max_potential_damage, int) or isinstance(
+ max_potential_damage, float
+ ):
+ # Set the column(s) to a single value
+ for damage_type in damage_types:
self.exposure_db[
- f"Max Potential Damage: {damage_type.capitalize()}"
- ] = [
- damage_values[building_type][damage_type.lower()] * square_meters
- for building_type, square_meters in zip(
- gdf["Primary Object Type"], gdf["area"]
+ f"Max Potential Damage: {damage_type}"
+ ] = max_potential_damage
+
+ elif isinstance(max_potential_damage, list):
+ # Multiple files are used to assign the ground floor height to the assets
+ NotImplemented
+ elif max_potential_damage in [
+ "jrc_damage_values",
+ "hazus_max_potential_damages",
+ ]:
+ if max_potential_damage == "jrc_damage_values":
+ damage_source = self.data_catalog.get_dataframe(max_potential_damage)
+ if country is None:
+ country = "World"
+ self.logger.warning(
+ f"No country specified, using the '{country}' JRC damage values."
)
- ]
- except KeyError as e:
- self.logger.warning(
- f"Not found in the {max_potential_damage} damage "
- f"value data: {e}"
- )
+
+ damage_values = preprocess_jrc_damage_values(damage_source, country)
+
+ elif max_potential_damage == "hazus_max_potential_damages":
+ damage_source = self.data_catalog.get_dataframe(max_potential_damage)
+ damage_values = preprocess_hazus_damage_values(damage_source)
+
+ # Calculate the area of each object
+ gdf = self.get_full_gdf(self.exposure_db)[
+ ["Primary Object Type", "geometry"]
+ ]
+ gdf = get_area(gdf)
+
+ # Set the damage values to the exposure data
+ for damage_type in damage_types:
+ # Calculate the maximum potential damage for each object and per damage type
+ try:
+ self.exposure_db[
+ f"Max Potential Damage: {damage_type.capitalize()}"
+ ] = [
+ damage_values[building_type][damage_type.lower()]
+ * square_meters
+ for building_type, square_meters in zip(
+ gdf["Primary Object Type"], gdf["area"]
+ )
+ ]
+ except KeyError as e:
+ self.logger.warning(
+ f"Not found in the {max_potential_damage} damage "
+ f"value data: {e}"
+ )
+ elif isinstance(max_potential_damage, str) or isinstance(
+ max_potential_damage, Path
+ ):
+ # When the max_potential_damage is a string but not jrc_damage_values
+ # or hazus_max_potential_damages. Here, a single file is used to
+ # assign the ground floor height to the assets
+ gfh = self.data_catalog.get_geodataframe(max_potential_damage)
+ gdf = self.get_full_gdf(self.exposure_db)
+ gdf = join_spatial_data(gdf, gfh, attr_name, method, max_dist, self.logger)
+ self.exposure_db = self._set_values_from_other_column(
+ gdf, f"Max Potential Damage: {damage_types[0].capitalize()}", attr_name
+ )
+
+ def setup_ground_elevation(
+ self,
+ ground_elevation: Union[int, float, None, str, Path],
+ ) -> None:
+ if ground_elevation:
+ self.exposure_db["Ground Elevation"] = ground_elevation_from_dem(
+ ground_elevation=ground_elevation,
+ exposure_db=self.exposure_db,
+ exposure_geoms=self.get_full_gdf(self.exposure_db),
+ )
+
+ else:
+ print(
+ "Ground elevation is not recognized by the setup_ground_elevation function\n Ground elevation will be set to 0"
+ )
+ self.exposure_db["Ground Elevation"] = 0
def update_max_potential_damage(
self, updated_max_potential_damages: pd.DataFrame
@@ -548,8 +781,9 @@ def raise_ground_floor_height(
"Raising the ground floor height of the properties relative to Datum."
)
self.exposure_db.loc[
- (self.exposure_db["Ground Floor Height"] < raise_by) & self.exposure_db.index.isin(idx),
- "Ground Floor Height",
+ (self.exposure_db["Ground Floor Height"] < raise_by)
+ & self.exposure_db.index.isin(idx),
+ "Ground Floor Height",
] = raise_by
elif height_reference.lower() in ["geom", "table"]:
@@ -681,6 +915,10 @@ def setup_new_composite_areas(
elevation_reference: str,
path_ref: str = None,
attr_ref: str = None,
+ ground_elevation: Union[None, str, Path] = None,
+ aggregation_area_fn: Union[List[str], List[Path], str, Path] = None,
+ attribute_names: Union[List[str], str] = None,
+ label_names: Union[List[str], str] = None,
) -> None:
"""Adds one or multiple (polygon) areas to the exposure database with
a composite damage function and a percentage of the total damage.
@@ -720,7 +958,9 @@ def setup_new_composite_areas(
percent_growth = float(percent_growth) / 100
geom_file = Path(geom_file)
- assert geom_file.is_file()
+ assert (
+ geom_file.is_file()
+ ), f"File {str(geom_file)} is missing, cannot set up a new composite area."
# Calculate the total damages for the new object, for the indicated damage types
new_object_damages = self.calculate_damages_new_exposure_object(
@@ -778,7 +1018,9 @@ def setup_new_composite_areas(
# TODO: Take ground elevation from DEM?
# For water level calculation this will not take into account the
# non-flooded cells separately, just averaged over the whole area.
- self.logger.warning("The ground elevation is set to 0.")
+ self.logger.warning(
+ "The ground elevation is set to 0 if no DEM is supplied."
+ )
# Idea: Reduction factor for the part of the area is not build-up?
@@ -842,40 +1084,130 @@ def setup_new_composite_areas(
self.crs,
)
+ # Update the exposure_geoms
+ self.set_geom_names("new_development_area")
+ self.set_exposure_geoms(_new_exposure_geoms)
+
+ # If the user supplied ground elevation data, assign that to the new
+ # composite areas
+ if ground_elevation is not None:
+ new_objects["Ground Elevation"] = ground_elevation_from_dem(
+ ground_elevation=ground_elevation,
+ exposure_db=new_objects,
+ exposure_geoms=_new_exposure_geoms,
+ )
+
+ # If the user supplied aggregation area data, assign that to the
+ # new composite areas
+ if aggregation_area_fn is not None:
+ new_objects = join_exposure_aggregation_areas(
+ _new_exposure_geoms.merge(new_objects, on="Object ID"),
+ aggregation_area_fn=aggregation_area_fn,
+ attribute_names=attribute_names,
+ label_names=label_names,
+ )
+
# Update the exposure_db
self.exposure_db = pd.concat([self.exposure_db, new_objects]).reset_index(
drop=True
)
- # Update the exposure_geoms
- self.set_exposure_geoms(_new_exposure_geoms)
-
def link_exposure_vulnerability(
self,
exposure_linking_table: pd.DataFrame,
damage_types: Optional[List[str]] = ["Structure", "Content"],
):
- linking_dict = dict(
- zip(exposure_linking_table["Link"], exposure_linking_table["Name"])
- )
-
- # Find the column to link the exposure data to the vulnerability data
- unique_types_primary = set(self.exposure_db["Primary Object Type"].unique())
- unique_types_secondary = set(self.exposure_db["Secondary Object Type"].unique())
- unique_linking_types = set(linking_dict.keys())
-
- # Check if the linking column is the Primary Object Type or the Secondary
- # Object Type
- if unique_types_primary.issubset(unique_linking_types):
- linking_column = "Primary Object Type"
- elif unique_types_secondary.issubset(unique_linking_types):
- linking_column = "Secondary Object Type"
-
+ exposure_linking_table["Damage function name"] = [
+ name + "_" + type
+ for name, type in zip(
+ exposure_linking_table["FIAT Damage Function Name"].values,
+ exposure_linking_table["Damage Type"].values,
+ )
+ ]
for damage_type in damage_types:
+ linking_per_damage_type = exposure_linking_table.loc[
+ exposure_linking_table["Damage Type"] == damage_type, :
+ ]
+ assert (
+ not linking_per_damage_type.empty
+ ), f"Damage type {damage_type} not found in the exposure-vulnerability linking table"
+
+ # Create a dictionary that links the exposure data to the vulnerability data
+ linking_dict = dict(
+ zip(
+ linking_per_damage_type["Exposure Link"],
+ linking_per_damage_type["Damage function name"],
+ )
+ )
+ unique_linking_types = set(linking_dict.keys())
+
+ # Find the column to link the exposure data to the vulnerability data
+ unique_types_primary = set()
+
+ # Set the variables below to large numbers to ensure when there is no
+ # Primary Object Type or Secondary Object Type column in the exposure data
+ # that the available column is used to link the exposure data to the
+ # vulnerability data.
+ len_diff_primary_linking_types = 100000
+ len_diff_secondary_linking_types = 100000
+ if "Primary Object Type" in self.exposure_db.columns:
+ unique_types_primary = set(self.get_primary_object_type())
+ diff_primary_linking_types = unique_types_primary - unique_linking_types
+ len_diff_primary_linking_types = len(diff_primary_linking_types)
+
+ unique_types_secondary = set()
+ if "Secondary Object Type" in self.exposure_db.columns:
+ unique_types_secondary = set(self.get_secondary_object_type())
+ diff_secondary_linking_types = (
+ unique_types_secondary - unique_linking_types
+ )
+ len_diff_secondary_linking_types = len(diff_secondary_linking_types)
+
+ # Check if the linking column is the Primary Object Type or the Secondary
+ # Object Type
+ if (len(unique_types_primary) > 0) and (
+ unique_types_primary.issubset(unique_linking_types)
+ ):
+ linking_column = "Primary Object Type"
+ elif (len(unique_types_secondary) > 0) and (
+ unique_types_secondary.issubset(unique_linking_types)
+ ):
+ linking_column = "Secondary Object Type"
+ else:
+ if (
+ len_diff_primary_linking_types < len_diff_secondary_linking_types
+ ) and (len(unique_types_primary) > 0):
+ linking_column = "Primary Object Type"
+ self.logger.warning(
+ "There are "
+ f"{str(len_diff_primary_linking_types)} primary"
+ " object types that are not in the linking "
+ "table and will not have a damage function "
+ f"assigned for {damage_type} damages: "
+ f"{str(list(diff_primary_linking_types))}"
+ )
+ elif (
+ len_diff_secondary_linking_types < len_diff_primary_linking_types
+ ) and (len(unique_types_secondary) > 0):
+ linking_column = "Secondary Object Type"
+ self.logger.warning(
+ "There are "
+ f"{str(len(diff_secondary_linking_types))} "
+ "secondary object types that are not in the "
+ "linking table and will not have a damage "
+ f"function assigned for {damage_type} damages: "
+ f"{str(list(diff_secondary_linking_types))}"
+ )
+
self.exposure_db[
f"Damage Function: {damage_type.capitalize()}"
] = self.exposure_db[linking_column].map(linking_dict)
+ self.logger.info(
+ f"The {linking_column} was used to link the exposure data to the "
+ f"vulnerability curves for {damage_type} damages."
+ )
+
def get_primary_object_type(self):
if "Primary Object Type" in self.exposure_db.columns:
return list(self.exposure_db["Primary Object Type"].unique())
@@ -1032,6 +1364,10 @@ def set_exposure_geoms_from_xy(self):
def get_full_gdf(
self, df: pd.DataFrame
) -> Union[gpd.GeoDataFrame, List[gpd.GeoDataFrame]]:
+ # Create a copy from the dataframe to ensure the values are not changed in the
+ # original dataframe
+ df = df.copy()
+
# Check how many exposure geoms there are
if len(self.exposure_geoms) == 1:
assert set(self.exposure_geoms[0]["Object ID"]) == set(df["Object ID"])
@@ -1182,3 +1518,16 @@ def set_height_relative_to_reference(
)
return exposure_to_modify.reset_index(drop=True)
+
+ @staticmethod
+ def _set_values_from_other_column(
+ df: Union[pd.DataFrame, gpd.GeoDataFrame], col_to_set: str, col_to_copy: str
+ ) -> Union[pd.DataFrame, gpd.GeoDataFrame]:
+ """Sets the values of to where the values of are
+ nan and deletes .
+ """
+ df.loc[df[col_to_copy].notna(), col_to_set] = df.loc[
+ df[col_to_copy].notna(), col_to_copy
+ ]
+ del df[col_to_copy]
+ return df
diff --git a/hydromt_fiat/workflows/gis.py b/hydromt_fiat/workflows/gis.py
new file mode 100644
index 00000000..e2fb4ac2
--- /dev/null
+++ b/hydromt_fiat/workflows/gis.py
@@ -0,0 +1,309 @@
+import geopandas as gpd
+from hydromt.gis_utils import utm_crs, nearest_merge
+from typing import List, Union
+import logging
+import rasterio
+from rasterio.features import rasterize
+from xrspatial import zonal_stats
+import pandas as pd
+import numpy as np
+import xarray as xr
+from pathlib import Path
+from geopy.geocoders import Nominatim
+
+
+def get_area(gdf: gpd.GeoDataFrame) -> gpd.GeoDataFrame:
+ """Adds an area column to a GeoDataFrame.
+
+ Parameters
+ ----------
+ gdf : gpd.GeoDataFrame
+ The GeoDataFrame to which the area column will be added.
+
+ Returns
+ -------
+ gpd.GeoDataFrame
+ The GeoDataFrame with an additional column "area".
+ """
+ # Calculate the area of each object
+ if gdf.crs.is_geographic:
+ # If the CRS is geographic, reproject to the nearest UTM zone
+ nearest_utm = utm_crs(gdf.total_bounds)
+ gdf_utm = gdf.to_crs(nearest_utm)
+ gdf["area"] = gdf_utm["geometry"].area
+ elif gdf.crs.is_projected:
+ # If the CRS is projected, calculate the area in the same CRS
+ gdf["area"] = gdf["geometry"].area
+ return gdf
+
+
+def sjoin_largest_area(
+ left_gdf: gpd.GeoDataFrame, right_gdf: gpd.GeoDataFrame, id_col: str = "Object ID"
+) -> gpd.GeoDataFrame:
+ """Spatial join of two GeoDataFrames, keeping only the joined data from the largest
+ intersection per object.
+
+ Parameters
+ ----------
+ left_gdf : gpd.GeoDataFrame
+ The GeoDataFrame to which the data from the right GeoDataFrame will be joined.
+ right_gdf : gpd.GeoDataFrame
+ The GeoDataFrame from which the data will be joined to the left GeoDataFrame.
+ id_col : str, optional
+ The ID column that will be used to drop the duplicates from overlapping
+ geometries, by default "Object ID"
+
+ Returns
+ -------
+ gpd.GeoDataFrame
+ Resulting GeoDataFrame with the joined data from the largest intersection per
+ object.
+ """
+ gdf = gpd.overlay(left_gdf, right_gdf, how="intersection")
+ gdf["area"] = gdf.geometry.area
+ gdf.sort_values(by="area", inplace=True)
+ gdf.drop_duplicates(subset=id_col, keep="last", inplace=True)
+ gdf.drop(columns=["area"], inplace=True)
+ return gdf
+
+
+def check_geometry_type(gdf: gpd.GeoDataFrame) -> str:
+ """Check if the geometry type of a GeoDataFrame is homogeneous.
+
+ Parameters
+ ----------
+ gdf : gpd.GeoDataFrame
+ The GeoDataFrame to check.
+
+ Raises
+ ------
+ ValueError
+ If the geometry type of the GeoDataFrame is not homogeneous.
+ """
+ if len(gdf.geom_type.unique()) > 1:
+ raise ValueError(
+ "The geometry type of the GeoDataFrame is not homogeneous. "
+ f"Geometry types found: {gdf.geom_type.unique()}"
+ )
+ else:
+ return gdf.geom_type.unique()[0]
+
+
+def get_crs_str_from_gdf(gdf_crs: gpd.GeoDataFrame.crs) -> str:
+ source_data_authority = gdf_crs.to_authority()
+ return source_data_authority[0] + ":" + source_data_authority[1]
+
+
+def clean_up_gdf(gdf: gpd.GeoDataFrame, columns: List[str]) -> gpd.GeoDataFrame:
+ """Clean up a GeoDataFrame by removing unnecessary columns.
+
+ Parameters
+ ----------
+ gdf : gpd.GeoDataFrame
+ The GeoDataFrame to clean up.
+
+ Returns
+ -------
+ gpd.GeoDataFrame
+ The cleaned up GeoDataFrame.
+ """
+ # Remove unnecessary columns
+ for col in columns:
+ del gdf[col]
+
+ return gdf
+
+
+def join_nearest_points(
+ left_gdf: gpd.GeoDataFrame,
+ right_gdf: gpd.GeoDataFrame,
+ attribute_name: str,
+ max_dist: float,
+) -> gpd.GeoDataFrame:
+ """Join two GeoDataFrames based on the nearest distance between their points.
+
+ Parameters
+ ----------
+ left_gdf : gpd.GeoDataFrame
+ The GeoDataFrame to which the data from the right GeoDataFrame will be joined.
+ right_gdf : gpd.GeoDataFrame
+ The GeoDataFrame from which the data will be joined to the left GeoDataFrame.
+ attribute_name : str
+ The name of the attribute that will be joined.
+ max_dist : float
+ The maximum distance for the nearest join measured in meters.
+
+ Returns
+ -------
+ gpd.GeoDataFrame
+ The joined GeoDataFrame.
+ """
+ # Use the HydroMT function nearest_merge to join the geodataframes
+ gdf_merged = nearest_merge(
+ gdf1=left_gdf, gdf2=right_gdf, columns=[attribute_name], max_dist=max_dist
+ )
+
+ # Clean up the geodataframe (remove unnecessary columns)
+ gdf_merged = clean_up_gdf(gdf_merged, ["distance_right", "index_right"])
+
+ return gdf_merged
+
+
+def intersect_points_polygons(left_gdf, right_gdf, attribute_name) -> gpd.GeoDataFrame:
+ gdf_merged = gpd.sjoin(left_gdf, right_gdf, how="left", predicate="intersects")
+
+ # Clean up the geodataframe (remove unnecessary columns)
+ gdf_merged = clean_up_gdf(gdf_merged, ["index_right"])
+
+ return gdf_merged
+
+
+def join_spatial_data(
+ left_gdf: gpd.GeoDataFrame,
+ right_gdf: gpd.GeoDataFrame,
+ attribute_name: str,
+ method: str,
+ max_dist: float = 10,
+ logger: logging.Logger = None,
+) -> gpd.GeoDataFrame:
+ """Join two GeoDataFrames based on their spatial relationship.
+
+ Parameters
+ ----------
+ left_gdf : gpd.GeoDataFrame
+ The GeoDataFrame to which the data from the right GeoDataFrame will be joined.
+ right_gdf : gpd.GeoDataFrame
+ The GeoDataFrame from which the data will be joined to the left GeoDataFrame.
+ attribute_name : str
+ The name of the attribute that will be joined.
+ method : str
+ The method that will be used to join the data. Either "nearest" or
+ "intersection".
+ max_dist : float, optional
+ The maximum distance for the nearest join measured in meters, by default
+ 10 (meters).
+ logger : logging.Logger
+ A logger object.
+
+ Returns
+ -------
+ gpd.GeoDataFrame
+ The joined GeoDataFrame.
+ """
+ try:
+ assert left_gdf.crs == right_gdf.crs, (
+ "The CRS of the GeoDataFrames to join do not match. "
+ f"Left CRS: {get_crs_str_from_gdf(left_gdf.crs)}, "
+ f"Right CRS: {get_crs_str_from_gdf(right_gdf.crs)}"
+ )
+ except AssertionError as e:
+ logger.warning(e)
+ logger.warning(
+ "Reprojecting the GeoDataFrame from "
+ f"{get_crs_str_from_gdf(right_gdf.crs)} to "
+ f"{get_crs_str_from_gdf(left_gdf.crs)}."
+ )
+ right_gdf = right_gdf.to_crs(left_gdf.crs)
+
+ left_gdf_type = check_geometry_type(left_gdf)
+ right_gdf_type = check_geometry_type(right_gdf)
+
+ assert (left_gdf_type == "Polygon") or (
+ left_gdf_type == "Point"
+ ), "The left GeoDataFrame should contain either polygons or points."
+
+ assert (right_gdf_type == "Polygon") or (
+ right_gdf_type == "Point"
+ ), "The right GeoDataFrame should contain either polygons or points."
+
+ if method == "nearest":
+ if left_gdf_type == "Polygon":
+ left_gdf.geometry = left_gdf.geometry.centroid
+
+ if right_gdf_type == "Polygon":
+ right_gdf.geometry = right_gdf.geometry.centroid
+
+ gdf = join_nearest_points(left_gdf, right_gdf, attribute_name, max_dist)
+
+ elif method == "intersection":
+ if (left_gdf_type == "Polygon") and (right_gdf_type == "Polygon"):
+ gdf = sjoin_largest_area(left_gdf, right_gdf)
+ elif (left_gdf_type == "Point") and (right_gdf_type == "Polygon"):
+ gdf = intersect_points_polygons(left_gdf, right_gdf, attribute_name)
+ else:
+ raise NotImplementedError(
+ f"Join method {method} is not implemented for joining data of geometry "
+ f"type {left_gdf_type} and {right_gdf_type}"
+ )
+ else:
+ raise NotImplementedError(f"Join method {method} is not implemented")
+
+ return gdf
+
+
+def ground_elevation_from_dem(
+ ground_elevation: Union[int, float, None, str, Path],
+ exposure_db: pd.DataFrame,
+ exposure_geoms: gpd.GeoDataFrame,
+) -> None:
+ # This function was developed by Willem https://github.com/interTwin-eu/DT-flood/blob/DemonstrationNotebooks/Notebooks/SetupFIAT.ipynb
+ # TODO: Find equivalent functions in hydromt
+ # Read in the DEM
+ dem = rasterio.open(ground_elevation)
+ # gdf = self.get_full_gdf(exposure_db)
+ gdf = exposure_geoms.to_crs(dem.crs.data)
+ # Create a list of geometries plus a label for rasterize
+ # The labels start at 1 since the label 0 is reserved for everything not in a geometry
+ # The order of each tuple is (geometry,label)
+ shapes = list(enumerate(gdf["geometry"].values))
+ shapes = [(t[1], t[0] + 1) for t in shapes]
+
+ rasterized = rasterize(
+ shapes=shapes, out_shape=dem.shape, transform=dem.transform, all_touched=True
+ )
+ # zonal_stats needs xarrays as input
+ rasterized = xr.DataArray(rasterized)
+
+ # Calculate the zonal statistics
+ zonal_out = zonal_stats(
+ rasterized,
+ xr.DataArray(dem.read(1)),
+ stats_funcs=["mean"],
+ nodata_values=np.nan,
+ )
+
+ # The zero label is for pixels not in a geometry so we discard them
+ zonal_out = zonal_out.drop(0)
+
+ # Array storing the zonal means
+ # Store the calculated means at index corresponding to their label
+ zonal_means = np.full(len(shapes), np.nan)
+ zonal_means[[zonal_out["zone"].values - 1]] = zonal_out["mean"].values
+
+ # # Add Ground Elevation column and get rid of nans in the appropriate way
+ exposure_db["Ground Elevation"] = zonal_means
+ exposure_db["Ground Elevation"].bfill(inplace=True)
+
+ return exposure_db["Ground Elevation"]
+
+
+def locate_from_bounding_box(bounding_box):
+ geolocator = Nominatim(user_agent="hydromt-fiat")
+
+ search = [
+ (bounding_box[1], bounding_box[0]),
+ (bounding_box[1], bounding_box[2]),
+ (bounding_box[3], bounding_box[0]),
+ (bounding_box[3], bounding_box[2]),
+ ]
+ locations = [geolocator.reverse(s) for s in search]
+ locations_list = [location[0].split(", ") for location in locations]
+ locations_list_no_numbers = [[y for y in x if not y.isnumeric()] for x in locations_list]
+ counties = [y for x in locations_list for y in x if ("county" in y.lower()) or ("parish" in y.lower())]
+ states = [x[-2] for x in locations_list_no_numbers]
+
+ counties_states_combo = set(list(zip(counties, states)))
+ counties = [c[0] for c in counties_states_combo]
+ states = [c[1] for c in counties_states_combo]
+
+ return counties, states
diff --git a/hydromt_fiat/workflows/hazard.py b/hydromt_fiat/workflows/hazard.py
index 0f014aa3..8ab4b69c 100644
--- a/hydromt_fiat/workflows/hazard.py
+++ b/hydromt_fiat/workflows/hazard.py
@@ -1,6 +1,5 @@
from hydromt_fiat.validation import *
from pathlib import Path
-import geopandas as gpd
from ast import literal_eval
import os
import xarray as xr
@@ -10,11 +9,9 @@
# from hydromt_sfincs import SfincsModel
from typing import Union
from typing import Tuple
-from hydromt.data_catalog import DataCatalog
-import geopandas as gpd
-def check_parameters_type(
+def create_lists(
map_fn: Union[str, Path, list[str], list[Path]],
map_type: Union[str, list[str]],
rp: Union[int, list[int], None] = None,
@@ -23,7 +20,7 @@ def check_parameters_type(
var: Union[str, list[str], None] = None,
chunks: Union[int, str, list[int]] = "auto",
) -> dict:
- """Check data type of parameters and save them as list items.
+ """Make list out of the parameters provided in the setup hazard maps.
Parameters
----------
@@ -71,28 +68,28 @@ def check_parameters_type(
params["nodata"] = nodata
params["var"] = var
- def validate_type(dictionary, param, name, types):
- params_lst = [param] if isinstance(param, types) else param
- check_param_type(params_lst, name=name, types=types)
- dictionary[name + "_lst"] = params_lst
+ def check_list(param, name):
+ params_lst = [param] if not isinstance(param, list) else param
+ params[name + "_lst"] = params_lst
return
+
+ check_list(map_fn, name="map_fn")
+ check_list(map_type, name="map_type")
- validate_type(params, map_fn, name="map_fn", types=(str, Path))
- validate_type(params, map_type, name="map_type", types=str)
if chunks != "auto":
- validate_type(params, chunks, name="chunks", types=(int, dict))
+ check_list(chunks, name="chunks")
if rp is not None:
- validate_type(params, rp, name="rp", types=(float, int))
+ check_list(rp, name="rp")
if crs is not None:
- validate_type(params, crs, name="crs", types=(int, str))
+ check_list(crs, name="crs")
if nodata is not None:
- validate_type(params, nodata, name="nodata", types=(float, int))
+ check_list(nodata, name="nodata")
if var is not None:
- validate_type(params, var, name="var", types=str)
+ check_list(var, name="var")
return params
-def check_parameters_size(
+def check_lists_size(
params: dict,
):
"""Check that list of parameters are of the same size in case multiple maps are
@@ -170,29 +167,7 @@ def error_message(variable_list):
):
error_message("var")
-
-def check_files(
- params: dict,
- root: str,
-):
- """Check if the provided files paths exists. I will raise an error in case the
- flood maps does not exist
-
- Parameters
- ----------
- params : dict
- Dictionary with the parameters and list of parameters used in setup_hazard.
- root : str
- The root directory of the model.
- """
- # load dictionary variables
- map_fn_lst = params["map_fn_lst"]
-
- # Check if the hazard input files exist.
- check_file_exist(root, param_lst=map_fn_lst, name="map_fn")
-
-
-def read_floodmaps(
+def read_maps(
params: dict,
da_map_fn: str,
idx: int,
@@ -230,24 +205,36 @@ def read_floodmaps(
map_fn_lst = params["map_fn_lst"]
map_type_lst = params["map_type_lst"]
- # Check if it is a path or a name from the catalog
+ # check existance of path
if os.path.exists(da_map_fn):
da_map_fn = Path(da_map_fn)
- da_name = da_map_fn.stem
+ da_name = da_map_fn.stem
da_suffix = da_map_fn.suffix
else:
- da_name = da_map_fn
+ raise ValueError(
+ f"The map {da_map_fn} could not be found."
+ )
- da_type = get_param(map_type_lst, map_fn_lst, "hazard", da_name, idx, "map type")
+ # retrieve data type
+ da_type = get_param(
+ map_type_lst,
+ map_fn_lst,
+ "hazard",
+ da_name,
+ idx,
+ "map type"
+ )
- # Get the local hazard map.
+ # get chuck area for the map
kwargs.update(chunks=chunks if chunks == "auto" else params["chunks_lst"][idx])
- if "da_suffix" in locals() and da_suffix == ".nc":
+ # check if we are providing a NetCDF file
+ if da_suffix == ".nc":
if var is None:
raise ValueError(
"The 'var' parameter is required when reading NetCDF data."
)
+ # retrieve variable name from parameter lists
da_var = get_param(
params["var_lst"],
map_fn_lst,
@@ -260,118 +247,6 @@ def read_floodmaps(
return da_map_fn, da_name, da_type
-def load_floodmaps(
- data_catalog: DataCatalog,
- region: gpd.GeoDataFrame,
- da_map_fn: Union[str, Path],
- da_name: str,
- name_catalog: str = "flood_maps",
- **kwargs,
-) -> xr.DataArray:
- """Load flood maps in memory from datacatalog or a local path
-
- Parameters
- ----------
- data_catalog : DataCatalog
- Data catalog object from model.
- region : gpd.GeoDataFrame
- Region of the model.
- da_map_fn : Union[str, Path]
- Path as string or key name in datacatalog of the hazard a specific hazard
- map idx.
- da_name : str
- File name of a specific hazard map.
- name_catalog : str, optional
- Name of data catalog item to take the flood maps from, by default "flood_maps".
-
- Returns
- -------
- xr.DataArray
- Hazard map to be loaded to the model's maps
- """
-
- # reading from path
- if da_map_fn.stem:
- if da_map_fn.stem == "sfincs_map":
- sfincs_root = os.path.dirname(da_map_fn)
- sfincs_model = SfincsModel(sfincs_root, mode="r")
- sfincs_model.read_results()
- # result_list = list(sfincs_model.results.keys())
- # sfincs_model.write_raster("results.zsmax", compress="LZW")
- da = sfincs_model.results["zsmax"]
- da.encoding["_FillValue"] = None
- else:
- if not region.empty:
- da = data_catalog.get_rasterdataset(da_map_fn, geom=region, **kwargs)
- else:
- da = data_catalog.get_rasterdataset(da_map_fn, **kwargs)
- # reading from the datacatalog
- else:
- if not region.empty:
- da = data_catalog.get_rasterdataset(
- name_catalog, variables=da_name, geom=region
- )
- else:
- da = data_catalog.get_rasterdataset(name_catalog, variables=da_name)
-
- return da
-
-# def load_floodmaps(
-# data_catalog: DataCatalog,
-# region: gpd.GeoDataFrame,
-# da_map_fn: Union[str, Path],
-# da_name: str,
-# name_catalog: str = "flood_maps",
-# **kwargs,
-# ) -> xr.DataArray:
-# """Load flood maps in memory from datacatalog or a local path
-
-# Parameters
-# ----------
-# data_catalog : DataCatalog
-# Data catalog object from model.
-# region : gpd.GeoDataFrame
-# Region of the model.
-# da_map_fn : Union[str, Path]
-# Path as string or key name in datacatalog of the hazard a specific hazard
-# map idx.
-# da_name : str
-# File name of a specific hazard map.
-# name_catalog : str, optional
-# Name of data catalog item to take the flood maps from, by default "flood_maps".
-
-# Returns
-# -------
-# xr.DataArray
-# Hazard map to be loaded to the model's maps
-# """
-
-# # reading from path
-# if da_map_fn.stem:
-# if da_map_fn.stem == "sfincs_map":
-# sfincs_root = os.path.dirname(da_map_fn)
-# sfincs_model = SfincsModel(sfincs_root, mode="r")
-# sfincs_model.read_results()
-# # result_list = list(sfincs_model.results.keys())
-# # sfincs_model.write_raster("results.zsmax", compress="LZW")
-# da = sfincs_model.results["zsmax"]
-# da.encoding["_FillValue"] = None
-# else:
-# if not region.empty:
-# da = data_catalog.get_rasterdataset(da_map_fn, geom=region, **kwargs)
-# else:
-# da = data_catalog.get_rasterdataset(da_map_fn, **kwargs)
-# # reading from the datacatalog
-# else:
-# if not region.empty:
-# da = data_catalog.get_rasterdataset(
-# name_catalog, variables=da_name, geom=region
-# )
-# else:
-# da = data_catalog.get_rasterdataset(name_catalog, variables=da_name)
-
-# return da
-
def check_maps_metadata(
maps: xr.Dataset,
@@ -427,7 +302,12 @@ def check_maps_metadata(
# Set nodata and mask the nodata value.
if nodata is not None:
da_nodata = get_param(
- params["nodata_lst"], map_fn_lst, "hazard", da_name, idx, "nodata"
+ params["nodata_lst"],
+ map_fn_lst,
+ "hazard",
+ da_name,
+ idx,
+ "nodata"
)
da.raster.set_nodata(nodata=da_nodata)
elif nodata is None and da.raster.nodata is None:
@@ -497,11 +377,8 @@ def check_maps_rp(
else:
da_rp = None
- if risk_output:
- da = da.expand_dims({"rp": [da_rp]}, axis=0)
-
if risk_output and da_rp is None:
- # Get (if possible) the return period from dataset names if the input parameter is None.
+ # get (if possible) the return period from dataset names if the input parameter is None.
if "rp" in da_name.lower():
def fstrip(x):
@@ -525,7 +402,7 @@ def fstrip(x):
raise ValueError(
"The hazard map must contain a return period in order to conduct a risk calculation."
)
-
+
return da_rp
@@ -542,156 +419,27 @@ def check_map_uniqueness(
check_uniqueness(map_name_lst)
-# old version of check_maps_uniquenes
-# def check_maps_uniquenes(
-# get_config,
-# maps: xr.Dataset,
-# params: dict,
-# da: xr.DataArray,
-# da_map_fn: Union[str, Path],
-# da_name: str,
-# da_type: str,
-# da_rp: Union[int, float],
-# idx: int,
-# ):
-
-# chunks = params['chunks']
-
-# # Add the hazard map to config and staticmaps.
-# check_uniqueness(
-# get_config,
-# maps,
-# "hazard",
-# da_type,
-# da_name,
-# {
-# "usage": True,
-# "map_fn": da_map_fn,
-# "map_type": da_type,
-# "rp": da_rp,
-# "crs": da.raster.crs,
-# "nodata": da.raster.nodata,
-# # "var": None if "var_lst" not in locals() else self.var_lst[idx],
-# "var": None if not 'var_lst' in params else params['var_lst'][idx],
-# "chunks": "auto" if chunks == "auto" else params['chunks_lst'][idx],
-# },
-# file_type="hazard",
-# filename=da_name,
-# )
-
-
-def check_floodmaps(
- get_config,
- maps,
- params,
- da,
- da_map_fn,
- da_name,
- da_type,
- idx,
- risk_output,
- **kwargs,
-):
- map_fn_lst = params["map_fn_lst"]
- chunks = params["chunks"]
- crs = params["crs"]
- nodata = params["nodata"]
- # Set the coordinate reference system.
- if crs is not None:
- da_crs = get_param(
- params["crs_lst"],
- map_fn_lst,
- "hazard",
- da_name,
- idx,
- "coordinate reference system",
- )
- da_crs_str = da_crs if "EPSG" in da_crs else f"EPSG:{da_crs}"
- da.raster.set_crs(da_crs_str)
- elif crs is None and not da.raster.crs:
- raise ValueError("The hazard map has no coordinate reference system assigned.")
-
- # Set nodata and mask the nodata value.
- if nodata is not None:
- da_nodata = get_param(
- params["nodata_lst"], map_fn_lst, "hazard", da_name, idx, "nodata"
- )
- da.raster.set_nodata(nodata=da_nodata)
- elif nodata is None and da.raster.nodata is None:
- raise ValueError("The hazard map has no nodata value assigned.")
-
- # Correct (if necessary) the grid orientation from the lower to the upper left corner.
- # This check could not be implemented into the sfincs_map outputs. They require to be transformed to geotiff first
- # if da_name != "sfincs_map":
- if da.raster.res[1] > 0:
- da = da.reindex({da.raster.y_dim: list(reversed(da.raster.ycoords))})
-
- # Check if the obtained hazard map is identical.
- if maps and not maps.raster.identical_grid(da):
- raise ValueError("The hazard maps should have identical grids.")
-
- # Get the return period input parameter.
- if "rp_lst" in params:
- da_rp = get_param(
- params["rp_lst"],
- map_fn_lst,
- "hazard",
- da_name,
- idx,
- "return period",
- )
- else:
- da_rp = None
-
- if risk_output:
- da = da.expand_dims({"rp": [da_rp]}, axis=0)
-
- if risk_output and da_rp is None:
- # Get (if possible) the return period from dataset names if the input parameter is None.
- if "rp" in da_name.lower():
-
- def fstrip(x):
- return x in "0123456789."
-
- rp_str = "".join(filter(fstrip, da_name.lower().split("rp")[-1])).lstrip(
- "0"
- )
-
- try:
- assert isinstance(
- literal_eval(rp_str) if rp_str else None, (int, float)
- )
- da_rp = literal_eval(rp_str)
-
- except AssertionError:
- raise ValueError(
- f"Could not derive the return period for hazard map: {da_name}."
- )
- else:
- raise ValueError(
- "The hazard map must contain a return period in order to conduct a risk calculation."
- )
-
- # Add the hazard map to config and staticmaps.
- check_uniqueness(
- get_config,
+def create_risk_dataset(
+ params: dict,
+ rp_list: list,
+ map_name_lst: list,
maps,
- "hazard",
- da_type,
- da_name,
- {
- "usage": True,
- "map_fn": da_map_fn,
- "map_type": da_type,
- "rp": da_rp,
- "crs": da.raster.crs,
- "nodata": da.raster.nodata,
- # "var": None if "var_lst" not in locals() else self.var_lst[idx],
- "var": None if "var_lst" not in params else params["var_lst"][idx],
- "chunks": "auto" if chunks == "auto" else params["chunks_lst"][idx],
- },
- file_type="hazard",
- filename=da_name,
- )
- return da_rp
+):
+ # order return periods and maps
+ dict_rp_name = {}
+ for rp, name in zip(rp_list, map_name_lst):
+ dict_rp_name[rp] = name
+ sorted_rp = sorted(rp_list, reverse=False)
+ dict_rp_name = {key: dict_rp_name[key] for key in sorted_rp}
+
+ sorted_maps = []
+ sorted_names = []
+
+ for key, value in dict_rp_name.items():
+ sorted_maps.append(maps[value])
+ sorted_names.append(value)
+
+ da = xr.merge(sorted_maps)
+
+ return da, sorted_rp, sorted_names
\ No newline at end of file
diff --git a/hydromt_fiat/workflows/roads.py b/hydromt_fiat/workflows/roads.py
new file mode 100644
index 00000000..a6357505
--- /dev/null
+++ b/hydromt_fiat/workflows/roads.py
@@ -0,0 +1,50 @@
+import pandas as pd
+import geopandas as gpd
+from hydromt.gis_utils import utm_crs
+import numpy as np
+
+
+def get_max_potential_damage_roads(
+ roads: gpd.GeoDataFrame, road_damage: pd.DataFrame
+) -> gpd.GeoDataFrame:
+ if roads.crs.is_geographic:
+ # If the CRS is geographic, reproject to the nearest UTM zone
+ nearest_utm = utm_crs(roads.total_bounds)
+ roads = roads.to_crs(nearest_utm)
+
+ unit = roads.crs.axis_info[0].unit_name
+
+ roads = gpd.GeoDataFrame(
+ {
+ "lanes": pd.to_numeric(roads["lanes"], errors="coerce"),
+ "segment_length": roads.length,
+ "geometry": roads["geometry"],
+ }
+ )
+ # Create dictionary of damages per number of lanes
+ damage_dic = dict(zip(road_damage["lanes"], road_damage["cost [USD/ft]"]))
+
+ # Step 3: Iterate through the DataFrame column and retrieve corresponding values
+ roads["lanes"] = [
+ 1 if (np.isnan(lane)) or (lane == 0) else lane for lane in list(roads["lanes"])
+ ]
+ roads["damage_value"] = roads["lanes"].map(damage_dic)
+
+ # Potentially convert the length to meters
+ roads["maximum_potential_damage"] = roads["damage_value"] * roads["segment_length"]
+ if unit == "meter" or unit == "metre" or unit == "m":
+ roads["maximum_potential_damage"] = roads["maximum_potential_damage"] * 0.3048
+ else:
+ print(
+ "You are using the wrong unit for the segment length. Please use <'foot/feet/ft'> or <'meter/metre/m'>"
+ )
+
+ return roads[["maximum_potential_damage", "segment_length"]]
+
+
+def get_road_lengths(roads):
+ if roads.crs.is_geographic:
+ # If the CRS is geographic, reproject to the nearest UTM zone
+ nearest_utm = utm_crs(roads.total_bounds)
+ roads = roads.to_crs(nearest_utm)
+ return roads.length
\ No newline at end of file
diff --git a/hydromt_fiat/workflows/social_vulnerability_index.py b/hydromt_fiat/workflows/social_vulnerability_index.py
index 187df87e..53a08b6b 100644
--- a/hydromt_fiat/workflows/social_vulnerability_index.py
+++ b/hydromt_fiat/workflows/social_vulnerability_index.py
@@ -1,21 +1,84 @@
-from census import Census #install in your environment using pip install Census
-from us import states #install in your environment using pip install us
+from census import Census # install in your environment using pip install Census
+from us import states # install in your environment using pip install us
from hydromt.data_catalog import DataCatalog
-from hydromt.log import setuplog
-import logging
+from logging import Logger
import pandas as pd
import numpy as np
import geopandas as gpd
-
-
+from urllib.request import urlopen
+from io import BytesIO
+from zipfile import ZipFile
+from pathlib import Path
+from typing import List
+
+def list_of_states(inverted: bool=True):
+ states = {
+ 'AK': 'Alaska',
+ 'AL': 'Alabama',
+ 'AR': 'Arkansas',
+ 'AZ': 'Arizona',
+ 'CA': 'California',
+ 'CO': 'Colorado',
+ 'CT': 'Connecticut',
+ 'DC': 'District of Columbia',
+ 'DE': 'Delaware',
+ 'FL': 'Florida',
+ 'GA': 'Georgia',
+ 'HI': 'Hawaii',
+ 'IA': 'Iowa',
+ 'ID': 'Idaho',
+ 'IL': 'Illinois',
+ 'IN': 'Indiana',
+ 'KS': 'Kansas',
+ 'KY': 'Kentucky',
+ 'LA': 'Louisiana',
+ 'MA': 'Massachusetts',
+ 'MD': 'Maryland',
+ 'ME': 'Maine',
+ 'MI': 'Michigan',
+ 'MN': 'Minnesota',
+ 'MO': 'Missouri',
+ 'MS': 'Mississippi',
+ 'MT': 'Montana',
+ 'NC': 'North Carolina',
+ 'ND': 'North Dakota',
+ 'NE': 'Nebraska',
+ 'NH': 'New Hampshire',
+ 'NJ': 'New Jersey',
+ 'NM': 'New Mexico',
+ 'NV': 'Nevada',
+ 'NY': 'New York',
+ 'OH': 'Ohio',
+ 'OK': 'Oklahoma',
+ 'OR': 'Oregon',
+ 'PA': 'Pennsylvania',
+ 'RI': 'Rhode Island',
+ 'SC': 'South Carolina',
+ 'SD': 'South Dakota',
+ 'TN': 'Tennessee',
+ 'TX': 'Texas',
+ 'UT': 'Utah',
+ 'VA': 'Virginia',
+ 'VT': 'Vermont',
+ 'WA': 'Washington',
+ 'WI': 'Wisconsin',
+ 'WV': 'West Virginia',
+ 'WY': 'Wyoming'
+ }
+
+ if inverted:
+ states_inverted = {value: key for key, value in states.items()}
+ return states_inverted
+ else:
+ return states
class SocialVulnerabilityIndex:
-
- def __init__(self, data_catalog: DataCatalog = None, logger: logging.Logger = None):
+ def __init__(self, data_catalog: DataCatalog, logger: Logger, save_folder: str):
self.data_catalog = data_catalog
+ self.save_folder = save_folder
self.census_key = Census
self.download_codes = {}
- self.state_fips = 0
+ self.state_fips = []
self.pd_census_data = pd.DataFrame()
self.codebook = pd.DataFrame()
self.indicator_groups = {}
@@ -27,17 +90,18 @@ def __init__(self, data_catalog: DataCatalog = None, logger: logging.Logger = No
self.pd_domain_scores = pd.DataFrame()
self.pd_domain_scores_z = pd.DataFrame()
self.pd_domain_scores_geo = pd.DataFrame()
- self.logger = setuplog("SVI", log_level=10)
+ self.logger = logger
self.svi_data_shp = gpd.GeoDataFrame()
-
+ self.block_groups = gpd.GeoDataFrame()
+
def read_dataset(self, path: str):
"""If you have your own dataset (e.g. already downloaded census data), you can use this to load it from a csv
Make sure to only use the relevant functions in fiat then"""
- self.pd_census_data = self.data_catalog.get_dataframe(path, sep = ";", header = 0)
+ self.pd_census_data = self.data_catalog.get_dataframe(path, sep=";", header=0)
self.logger.info(f"You loaded your dataset from {path}")
- def set_up_census_key(self, census_key:str):
- """The Census key can be inputted in the ini file.
+ def set_up_census_key(self, census_key: str):
+ """The Census key can be inputted in the ini file.
This is a unique key that every user needs to specify to download census data
Parameters
@@ -47,9 +111,11 @@ def set_up_census_key(self, census_key:str):
"""
self.census_key = Census(census_key)
- self.logger.info(f"your census key {census_key} is used to download data from the Census website ")
-
- def variable_code_csv_to_pd_df(self, path:str):
+ self.logger.info(
+ f"your census key {census_key} is used to download data from the Census website "
+ )
+
+ def variable_code_csv_to_pd_df(self, path: str):
"""Loading the csv in and converting to pd_dataframe to be used in the other functions.
Parameters
@@ -57,7 +123,7 @@ def variable_code_csv_to_pd_df(self, path:str):
path : specify the path where the census_vulnerability_data_codebook.csv is stored.
The Excel/CSV specifies the names of the variables that need to be downloaded from the Census API
- It contains additional information that can be called and used in the other functions.
+ It contains additional information that can be called and used in the other functions.
"""
self.codebook = self.data_catalog.get_dataframe(path)
@@ -67,9 +133,9 @@ def set_up_download_codes(self):
In this way the download_census_data function knows which variables to retrieve from the API.
"""
self.download_codes = list(self.codebook["Census_code_withE"])
- self.download_codes.append('NAME')
-
- def set_up_state_code(self, state_abbreviation:str):
+ self.download_codes.append("NAME")
+
+ def set_up_state_code(self, state_abbreviation: List[str]):
"""download census data for a state
Parameters
@@ -77,29 +143,38 @@ def set_up_state_code(self, state_abbreviation:str):
state_abbreviation : str
Abbreviation of the state for which you want to set up the census data download
"""
- state = [state_abbreviation] #read in the state abbreviation as specified in the ini file
- state_obj = getattr(states, state[0])
- self.state_fips = state_obj.fips
- self.logger.info(f"The state abbreviation specified is: {state_abbreviation}")
-
- def download_census_data(self):
+ states_done = []
+ for state in state_abbreviation:
+ if state not in states_done:
+ self.logger.info(f"The states for which census data will be downloaded is: {list_of_states(inverted=False)[state]}")
+ state_obj = getattr(states, state)
+ self.state_fips.append(state_obj.fips)
+ states_done.append(state)
+
+
+ def download_census_data(self, year_data):
"""download the census data
it is possible to also make the county, tract and blockgroup flexible so that a user could specify exactly what to download
But: bear in mind, with social vulneraiblity we compare areas against each other, so Ideally you would have a large enough dataset (for statistical and validity purposes)
"""
- download_census_codes = self.census_key.acs.state_county_blockgroup(
- fields=self.download_codes,
- state_fips=self.state_fips,
- county_fips="*",
- tract="*",
- blockgroup="*"
+ dfs = []
+ for sf in self.state_fips:
+ download_census_codes = self.census_key.acs5.state_county_blockgroup(
+ fields=self.download_codes,
+ state_fips=sf,
+ county_fips="*",
+ tract="*",
+ blockgroup="*",
+ year=year_data
)
- self.pd_census_data = pd.DataFrame(download_census_codes)
- self.logger.info("The census data was successfully downloaded using the specified variables in the codebook Excel")
- return self.pd_census_data
+ dfs.append(pd.DataFrame(download_census_codes))
+ self.pd_census_data = pd.concat(dfs, ignore_index=True)
+ self.logger.info(
+ "The census data was successfully downloaded using the specified variables in the codebook Excel"
+ )
- def rename_census_data(self, from_name:str, to_name:str):
+ def rename_census_data(self, from_name: str, to_name: str):
"""renaming the columns so that they have variable names instead of variable codes as their headers
Parameters
@@ -110,33 +185,39 @@ def rename_census_data(self, from_name:str, to_name:str):
The name to replace with
"""
name_dict = dict(zip(self.codebook[from_name], self.codebook[to_name]))
- self.pd_census_data = self.pd_census_data.rename(columns = name_dict)
-
+ self.pd_census_data = self.pd_census_data.rename(columns=name_dict)
def identify_no_data(self):
- """This function identifies and replaces negative values in the pd_census_data DataFrame with NaN.
- It then filters out rows that have no values greater than 0 in the columns (excluding specific columns) and updates the DataFrame accordingly.
+ """This function identifies and replaces negative values in the pd_census_data DataFrame with NaN.
+ It then filters out rows that have no values greater than 0 in the columns (excluding specific columns) and updates the DataFrame accordingly.
The resulting DataFrame will contain only the rows where at least one value is greater than 0 in the selected columns, with negative values replaced by NaN.
"""
- self.columns_to_exclude = ["GEO_ID", "NAME", "state", "county", "tract", "block group", "Index"]
+ self.columns_to_exclude = [
+ "GEO_ID",
+ "NAME",
+ "state",
+ "county",
+ "tract",
+ "block group",
+ "Index",
+ ]
rows_to_keep = []
-
+
for column, series in self.pd_census_data.items():
if column in self.columns_to_exclude:
continue
for idx, value in series.items():
if value < 0:
self.pd_census_data.loc[idx, column] = np.nan
-
+
for index, row in self.pd_census_data.iterrows():
values_to_check = row.loc[~row.index.isin(self.columns_to_exclude)]
if any(values_to_check > 0):
rows_to_keep.append(index)
-
+
self.pd_census_data = self.pd_census_data.loc[rows_to_keep]
-
-
- def check_nan_variable_columns(self, column_to_check:str, indicator_column:str):
+
+ def check_nan_variable_columns(self, column_to_check: str, indicator_column: str):
"""
Parameters
----------
@@ -151,7 +232,9 @@ def check_nan_variable_columns(self, column_to_check:str, indicator_column:str):
When a census variable is dropped from the dataset because of too many nan-values (>5%) we print a logging message
"""
- nan_percentages = (self.pd_census_data.isna().sum() / len(self.pd_census_data)) * 100
+ nan_percentages = (
+ self.pd_census_data.isna().sum() / len(self.pd_census_data)
+ ) * 100
self.percentage = []
self.missing_values = {}
@@ -171,14 +254,18 @@ def check_nan_variable_columns(self, column_to_check:str, indicator_column:str):
self.fill_nan_columns(column)
for value in self.codebook[column_to_check]:
- if value not in self.pd_census_data.columns:
- indicator_name = self.codebook.loc[self.codebook[column_to_check] == value, indicator_column].iloc[0]
- self.missing_values[value] = indicator_name
- self.logger.info(f"From indicator '{indicator_name}' the variable '{value}' is dropped because of too many missing values (exceeding the threshold of 5%)")
-
- def fill_nan_columns(self, nancolumn:str):
+ if value not in self.pd_census_data.columns:
+ indicator_name = self.codebook.loc[
+ self.codebook[column_to_check] == value, indicator_column
+ ].iloc[0]
+ self.missing_values[value] = indicator_name
+ self.logger.info(
+ f"From indicator '{indicator_name}' the variable '{value}' is dropped because of too many missing values (exceeding the threshold of 5%)"
+ )
+
+ def fill_nan_columns(self, nancolumn: str):
"""This function is created to fill no data with the column mean
-
+
Parameters
----------
nancolumn : str
@@ -186,40 +273,41 @@ def fill_nan_columns(self, nancolumn:str):
In the case of the census data: The column mean is the average value of all regions, for that specific variable
"""
-
+
column_average = self.pd_census_data[nancolumn].mean()
self.pd_census_data[nancolumn].fillna(column_average, inplace=True)
def check_zeroes_variable_rows(self):
- #Here you count the zeroes in a row.
- #This is under the assumption that if more than half of the data is zero, it indicates missing data.
- #Note: in theory a region can be least vulnerable on all commponents and score zero everywhere. We assume however that it is unlikely that this is the case for more than 50%
+ # Here you count the zeroes in a row.
+ # This is under the assumption that if more than half of the data is zero, it indicates missing data.
+ # Note: in theory a region can be least vulnerable on all commponents and score zero everywhere. We assume however that it is unlikely that this is the case for more than 50%
self.excluded_regions = []
num_columns = self.pd_census_data.shape[1]
- if 'NAME' in self.pd_census_data.columns:
+ if "NAME" in self.pd_census_data.columns:
num_columns = self.pd_census_data.shape[1] - 1
for index, row in self.pd_census_data.iterrows():
num_zeros = (row == 0).sum()
percentage_zeros = num_zeros / num_columns
-
+
if percentage_zeros > 0.5:
self.pd_census_data = self.pd_census_data.drop(index)
names_exluced_areas = row["NAME"]
self.excluded_regions.append(names_exluced_areas)
-
- def create_indicator_groups(self, column_to_group:str, aggregation_name:str) -> dict:
- """With this code we can group the different variables into their indicator group
+ def create_indicator_groups(
+ self, column_to_group: str, aggregation_name: str
+ ) -> dict:
+ """With this code we can group the different variables into their indicator group
We can also group the indicators into the domains with this code
-
+
Parameters
----------
column_to_group : str
- This is the column which you want to group together into an aggregate group.
+ This is the column which you want to group together into an aggregate group.
For example grouping all the Census variables into the indicators they belong to
aggregation_name : str
- This is the column by which you want to group. So for example:
+ This is the column by which you want to group. So for example:
this column indicates the group name for the indicators to which variables belong.
Returns
@@ -231,8 +319,8 @@ def create_indicator_groups(self, column_to_group:str, aggregation_name:str) ->
for indicator_code, group_df in self.codebook.groupby(aggregation_name):
translation[indicator_code] = set(group_df[column_to_group].tolist())
return translation
-
- def processing_svi_data(self, translation_var_ind:dict):
+
+ def processing_svi_data(self, translation_var_ind: dict):
"""
This is a function to pre-process the Census data.
The function is quite specific to the census data! Check if it can be applied to other datasets!
@@ -249,9 +337,9 @@ def processing_svi_data(self, translation_var_ind:dict):
The preprocessing needs to be done to convert variables into indicators, for example by dividing a variable by a total * 100 to find a percentage.
The variables with 'total' in their name, are used to do divisions for the creation of the percentages / shares.
Therefore, this function below firstly finds for every indicator group if there is a variable with 'total' in the name.
- If that is not the case, the indicator is not a percentage or share but a Median value.
+ If that is not the case, the indicator is not a percentage or share but a Median value.
We can just keep the Median value and put it in the final dataframe (processed_census_data)
- When there are more than 2 columns, we first need to sum all the data (except for the total).
+ When there are more than 2 columns, we first need to sum all the data (except for the total).
Then we compute the percentage of that sum as a share of the whole (the total). For example to find the population under 5 years and over 65 years we need to add females and males and the different age groups and then take that as a percentage of the total population.
If there are exactly two columns, then the one needs to be divided by the other. The other is always the total.
Together these procedures make up the processed indicators.
@@ -265,11 +353,13 @@ def processing_svi_data(self, translation_var_ind:dict):
# Loop over the indicator_groups
for key, values in translation_var_ind.items():
- updated_values = [value for value in values if value not in self.missing_values.keys()]
+ updated_values = [
+ value for value in values if value not in self.missing_values.keys()
+ ]
if len(updated_values) > 0:
updated_translation_var_ind[key] = updated_values
if key in self.columns_to_exclude:
- continue
+ continue
# Update translation_var_ind with the key-value pairs and remove the 'missing values' / the excluded values
@@ -280,46 +370,55 @@ def processing_svi_data(self, translation_var_ind:dict):
group_vars = self.pd_census_data[values]
group_varslist.append(group_vars)
else:
- continue
+ continue
# identify the columns that contain 'total' in the name. These are the columns that are used for division, to calculate percentages etc.
- total_cols = [col for col in group_vars.columns if 'total' in col.lower()]
-
- #if there is no total column, the indicator is a single column (e.g. Median) and we just want to take that column
- if len(total_cols) == 0:
- self.processed_census_data[key] = group_vars.iloc[:,0]
-
- #if there are more than 2 columns we need to first sum the columns together and then divide over the total to get a percentage
- elif len(group_vars.columns) > 2:
+ total_cols = [col for col in group_vars.columns if "total" in col.lower()]
+
+ # if there is no total column, the indicator is a single column (e.g. Median) and we just want to take that column
+ if len(total_cols) == 0:
+ self.processed_census_data[key] = group_vars.iloc[:, 0]
+
+ # if there are more than 2 columns we need to first sum the columns together and then divide over the total to get a percentage
+ elif len(group_vars.columns) > 2:
# identify the columns that do not contain 'total' in the name
- value_cols = [col for col in group_vars.columns if col not in total_cols]
- #sum the values across the rows (axis = 1) for value columns
+ value_cols = [
+ col for col in group_vars.columns if col not in total_cols
+ ]
+ # sum the values across the rows (axis = 1) for value columns
sum_values = group_vars[value_cols].sum(axis=1)
# divide the sum of values by the total column for this group
total_col = total_cols[0]
result = sum_values / group_vars[total_col] * 100
# add a new column to the result dataframe with the result and the key as the column header
self.processed_census_data[key] = result
-
- #if the length is exactly 2, you do not need to sum but you do need to divide over the corresponding total column
- elif len(group_vars.columns) == 2:
+
+ # if the length is exactly 2, you do not need to sum but you do need to divide over the corresponding total column
+ elif len(group_vars.columns) == 2:
total_col = total_cols[0]
- value_cols = [col for col in group_vars.columns if col not in total_cols]
- if 'capita' in value_cols[0]: #if 'capita' is in the column name, do not multiply by 100
- result = group_vars[value_cols].iloc[:,0] / group_vars[total_col]
+ value_cols = [
+ col for col in group_vars.columns if col not in total_cols
+ ]
+ if (
+ "capita" in value_cols[0]
+ ): # if 'capita' is in the column name, do not multiply by 100
+ result = group_vars[value_cols].iloc[:, 0] / group_vars[total_col]
else:
- result = group_vars[value_cols].iloc[:,0] / group_vars[total_col] * 100
+ result = (
+ group_vars[value_cols].iloc[:, 0] / group_vars[total_col] * 100
+ )
# add a new column to the result dataframe with the result and the key as the column header
self.processed_census_data[key] = result
self.processed_census_data["NAME"] = self.pd_census_data["NAME"]
- self.logger.info("The specified variables are processed into their corresponding indicators, as specified in the Excel codebook")
+ self.logger.info(
+ "The specified variables are processed into their corresponding indicators, as specified in the Excel codebook"
+ )
def zscore(self, dataseries):
return (dataseries - dataseries.mean()) / dataseries.std()
-
- def normalization_svi_data(self):
+ def normalization_svi_data(self):
"""This function normalizes the processed indicator dataset using zscores for the first time
The normalization direction is specified in the input excel codebook
The inverse or normal standardization is based in the indicator's relation with social vulnerability
@@ -331,153 +430,193 @@ def normalization_svi_data(self):
vulnerability_features = selected_data["Indicator_code"].tolist()
direction = selected_data["zscore"].tolist()
- self.svi_fiat['NAME'] = self.processed_census_data['NAME']
+ self.svi_fiat["NAME"] = self.processed_census_data["NAME"]
svi_fiat_names = []
for column, dir in zip(vulnerability_features, direction):
- if column not in self.processed_census_data.columns: #If there are variables in the codebook but not in the data, this line ensures the model does not crash
+ if (
+ column not in self.processed_census_data.columns
+ ): # If there are variables in the codebook but not in the data, this line ensures the model does not crash
continue
- if dir == 'normal':
- self.svi_fiat[column + '_norm'] = self.zscore(self.processed_census_data[column])
- svi_fiat_names.append(column + '_norm')
- elif dir == 'inverse':
- self.svi_fiat[column + '_norm'] = self.zscore(self.processed_census_data[column]) * -1
- svi_fiat_names.append(column + '_norm')
+ if dir == "normal":
+ self.svi_fiat[column + "_norm"] = self.zscore(
+ self.processed_census_data[column]
+ )
+ svi_fiat_names.append(column + "_norm")
+ elif dir == "inverse":
+ self.svi_fiat[column + "_norm"] = (
+ self.zscore(self.processed_census_data[column]) * -1
+ )
+ svi_fiat_names.append(column + "_norm")
else:
- print('Direction is not provided')
- self.logger.info("The data is standardized according to their relation specified in the codebook Excel (inverse or normal z-scores)")
+ self.logger.warning(f"Normalization direction for {column} is not provided")
+ self.logger.info(
+ "The data is standardized according to their relation specified in the codebook Excel (inverse or normal z-scores)"
+ )
def domain_scores(self):
"""This function groups the indicators together in domains (called 'Category' in the input excel codebook) and calculates their domain score
First, the domaingroups need to contain the right indicators, specific to the dataset
It could be that an indiator is missing because of variable exclusion on the basis of nan-criteria >5% and there are no variables left to compute an indicator
- In that case, the indicators are removed from domaingroups and the domain score is composed of the indicators that are there to make up a domain"""
-
+ In that case, the indicators are removed from domaingroups and the domain score is composed of the indicators that are there to make up a domain
+ """
+
self.pd_domain_scores = pd.DataFrame()
-
+
# Get the column names from the processed dataframe
columns_data = self.processed_census_data.columns.tolist()
+ columns_data.remove("NAME")
domaingroups = self.create_indicator_groups("Indicator_code", "Category")
self.missing_indicators = []
# Remove key-value pairs without a match -> you want to remove from the default match set (based in codebook) the variables that are no longer in your dataset
- if any(value not in columns_data for values in domaingroups.values() for value in values):
- self.missing_indicators.append([value for values in domaingroups.values() for value in values if value not in columns_data])
- # Create a new dictionary with the values that are present in columns_data
- domaingroups = {key: [value for value in values if value in columns_data] for key, values in domaingroups.items()}
-
+ if any(
+ value not in columns_data
+ for values in domaingroups.values()
+ for value in values
+ ):
+ self.missing_indicators.append(
+ [
+ value
+ for values in domaingroups.values()
+ for value in values
+ if value not in columns_data
+ ]
+ )
+ # Create a new dictionary with the values that are present in columns_data
+ domaingroups = {
+ key: [value for value in values if value in columns_data]
+ for key, values in domaingroups.items()
+ }
+
for key, values in domaingroups.items():
# Add _norm to all values to match with the normalization column names
values_norm = [v + '_norm' for v in values]
sum_variables = self.svi_fiat.filter(items=values_norm) #use the normalized dataset!
- sum_values = sum_variables.sum(axis=1) / len(values_norm) #use the normalized dataset!
+ z_variables = self.zscore(sum_variables)
+ sum_values = z_variables.sum(axis=1) / len(values_norm) #use the normalized dataset!
self.pd_domain_scores[key] = sum_values
- self.pd_domain_scores["NAME"] = self.processed_census_data["NAME"]
- self.pd_domain_scores_z = self.zscore(self.pd_domain_scores)
- self.pd_domain_scores_z["NAME"] = self.processed_census_data["NAME"]
- self.logger.info("The indicators are grouped together into their respective domains and are standardized again using zscores")
+ self.pd_domain_scores_z[key] = sum_values
+
+ #finding and storing the highest scoring domain for each row and adding it to the dataset
+ self.pd_domain_scores_z['SVI_key_domain'] = self.pd_domain_scores_z.apply(lambda row: row.idxmax(), axis=1)
+
+ #Now we add the names so we can later match these regions with their geometry
+ self.pd_domain_scores_z["NAME"] = self.processed_census_data["NAME"]
+ self.pd_domain_scores["NAME"] = self.processed_census_data["NAME"]
+
+ self.logger.info(
+ "The indicators are grouped together into their respective domains and are standardized again using zscores"
+ )
def composite_scores(self):
- """create composite scores from the domain scores
- """
- columns_to_sum = self.pd_domain_scores_z.columns.drop("NAME")
+ """create composite scores from the domain scores"""
+ columns_to_sum = self.pd_domain_scores_z.columns.drop(["NAME", "SVI_key_domain"])
self.pd_domain_scores_z["composite_SVI"] = self.pd_domain_scores_z[columns_to_sum].sum(axis=1)
self.pd_domain_scores_z["composite_svi_z"] = self.zscore(self.pd_domain_scores_z["composite_SVI"])
- self.logger.info("Composite SVI scores are created based on the domain scores using equal weighting and are standardized again using zscores")
+ self.logger.info(
+ "Composite SVI scores are created based on the domain scores using equal weighting and are standardized again using zscores"
+ )
+
def match_geo_ID(self):
"""Matches GEO_IDs for the regions that are still in the dataset"""
self.pd_domain_scores_geo = self.pd_domain_scores_z.copy()
- self.pd_domain_scores_geo['GEO_ID'] = None # Create a new column 'GEO_ID' with initial values set to None
+ self.pd_domain_scores_geo[
+ "GEO_ID"
+ ] = None # Create a new column 'GEO_ID' with initial values set to None
for index, value in enumerate(self.pd_domain_scores_geo["NAME"]):
if value in self.pd_census_data["NAME"].values:
- matching_row = self.pd_census_data.loc[self.pd_census_data["NAME"] == value]
- geo_id = matching_row["GEO_ID"].values[0] # Assuming there's only one matching row, extract the GEO_ID value
- self.pd_domain_scores_geo.at[index, 'GEO_ID'] = geo_id # Assign the GEO_ID value to the corresponding row in self.pd_domain_scores_geo
- self.pd_domain_scores_geo['GEOID_short'] = self.pd_domain_scores_geo['GEO_ID'].str.split('US').str[1]
-
- def load_shp_geom(self, path):
- """loading geometry data from the user's specified shapefile.
- The user can download the right block groups for their preferred state from: https://www.census.gov/cgi-bin/geo/shapefiles/index.php
+ matching_row = self.pd_census_data.loc[
+ self.pd_census_data["NAME"] == value
+ ]
+ geo_id = matching_row["GEO_ID"].values[
+ 0
+ ] # Assuming there's only one matching row, extract the GEO_ID value
+ self.pd_domain_scores_geo.at[
+ index, "GEO_ID"
+ ] = geo_id # Assign the GEO_ID value to the corresponding row in self.pd_domain_scores_geo
+ self.pd_domain_scores_geo["GEOID_short"] = (
+ self.pd_domain_scores_geo["GEO_ID"].str.split("US").str[1]
+ )
+
+ def download_and_unzip(self, url, extract_to='.'):
+ """function to download the shapefile data from census tiger website
Parameters
----------
- path : path to shapefile
- A shapefile containing block groups geometry: https://www.census.gov/cgi-bin/geo/shapefiles/index.php
- """
- self.shape_data = self.data_catalog.get_geodataframe(path)
- self.logger.info(f"Succesfully read in the shapefile from {path}")
-
- def merge_svi_data_shp(self):
- """Merges the geometry data with the social vulnerability index computed in the module
+ url : webpage
+ URL to census website (TIGER) to download shapefiles for visualisation
+ extract_to : str, optional
+ _description_, by default '.'
"""
- self.svi_data_shp = self.pd_domain_scores_geo.merge(self.shape_data, left_on='GEOID_short', right_on='GEOID')
- self.svi_data_shp = gpd.GeoDataFrame(self.svi_data_shp)
- columns_to_drop = ['STATEFP','COUNTYFP','TRACTCE','BLKGRPCE', 'GEOID','NAMELSAD','MTFCC','FUNCSTAT','ALAND', 'AWATER','INTPTLAT','INTPTLON']
- self.svi_data_shp = self.svi_data_shp.drop(columns=columns_to_drop)
- self.logger.info("The geometry information was successfully added to the social vulnerability information")
-
-
-
-###OTHER
-
-#import requests
-#import json
-
-#This function does not work yet. Somehow the json data does not contain the acutal geometry data.
- #svi.load_geometry_data("https://tigerweb.geo.census.gov/arcgis/rest/services/TIGERweb/tigerWMS_ACS2022/MapServer/8?f=pjson")
- #def load_geometry_data(self, url:str):
-
- # # Specify the parameters for the request
- # params = {
- # "where": "1=1",
- # "geometryType": "esriGeometryPolygon",
- # "spatialRel": "esriSpatialRelIntersects",
- # "outFields": "*",
- # "returnGeometry": True,
- # "f": "geojson" # Retrieve data as GeoJSON
- # }
-
- # # Send the GET request
- # response = requests.get(url, params=params)
- # response2 = response.text
- # # Load the JSON data
- # json_data = json.loads(response2)
-
- # geometry_data = json_data['geometryField']
-
- # # Extract the GEOID data
- # geoid_data = [feature['attributes']['GEOID'] for feature in json_data['features']]
-
- # # Create a GeoPandas DataFrame
- # df = gpd.GeoDataFrame(pd.DataFrame(geoid_data, columns=['GEOID']))
- # df['geometry'] = gpd.GeoSeries.from_wkt(geometry_data)
-
- # # Print the resulting GeoPandas DataFrame
- # print(df)
-
-
-
-
- #Testing the functions
- #Removing some variables from the codebook
- #I think in all cases you would need the total column, otherwise you would mess up the making of your indicators and thus final scores
- #but you would not see that in the results as they will be standardized anyways
- #the code works, so that does not really matter
-
- #lowering the nan-threshold to 3%
- #it runs
-
- #changing the input data now also works. The code can still run if there are varaibles missing or if there is a large amount of Nans
- #we now assume that we will still make the indciator if data is missing, as a default. In future versions we might want to chagne this for accuracy purposes
- #We now printed the information about missing variables / indicators to lists that can be inspected so that informed decision can be made.
+ try:
+ http_response = urlopen(url)
+ zipfile = ZipFile(BytesIO(http_response.read()))
+ zipfile.extractall(path=extract_to)
+ except Exception as e:
+ self.logger.warning(f"Error during download and unzip: {e}")
+ def download_shp_geom(self, year_data: int, counties: List[str]):
+ """Downloading the shapefiles from the government Tiger website
+ Parameters
+ ----------
+ year_data : int
+ The year for which you want to download the census data and the corresponding shapefiles (for geometry)
+ counties : List[str]
+ A list of county codes in which your area of interest lies
+ """
+ block_groups_list = []
+ for sf, county in zip(self.state_fips, counties):
+ # Download shapefile of blocks
+ if year_data == 2022:
+ url = f"https://www2.census.gov/geo/tiger/TIGER_RD18/LAYER/FACES/tl_rd22_{sf}{county}_faces.zip"
+ elif year_data == 2021:
+ url = f"https://www2.census.gov/geo/tiger/TIGER2021/FACES/tl_2021_{sf}{county}_faces.zip"
+ elif year_data == 2020:
+ url = f"https://www2.census.gov/geo/tiger/TIGER2020PL/LAYER/FACES/tl_2020_{sf}{county}_faces.zip"
+ else:
+ self.logger.warning(f"Year {year_data} not available from 'https://www2.census.gov/geo/tiger'")
+ return
+
+ # Save shapefiles
+ folder = Path(self.save_folder) / "shapefiles" / (sf + county) / str(year_data)
+ self.logger.info(f"Downloading the county shapefile for {str(year_data)}")
+ self.download_and_unzip(url, folder)
+ shapefiles = list(Path(folder).glob("*.shp"))
+ if shapefiles:
+ shp = gpd.read_file(shapefiles[0])
+ self.logger.info("The shapefile was downloaded")
+ else:
+ self.logger.warning("No county shapefile found in the directory.")
+
+ # Dissolve shapefile based on block groups
+ code = "20"
+ attrs = ["STATEFP", "COUNTYFP", "TRACTCE", "BLKGRPCE"]
+ attrs = [attr + code for attr in attrs]
+ block_groups_shp = shp.dissolve(by=attrs, as_index=False)
+ block_groups_shp = block_groups_shp[attrs + ["geometry"]]
+ # block_groups["Census_Bg"] = block_groups['TRACTCE' + code].astype(str) + "-block" + block_groups['BLKGRPCE' + code].astype(str)
+ block_groups_shp["GEO_ID"] = "1500000US" + block_groups_shp['STATEFP' + code].astype(str) + block_groups_shp['COUNTYFP' + code].astype(str) + block_groups_shp['TRACTCE' + code].astype(str) + block_groups_shp['BLKGRPCE' + code].astype(str)
+ block_groups_list.append(block_groups_shp)
+ self.block_groups = gpd.GeoDataFrame(pd.concat(block_groups_list))
+ def merge_svi_data_shp(self):
+ """Merges the geometry data with the social vulnerability index computed in the module"""
+ self.svi_data_shp = self.pd_domain_scores_geo.merge(self.block_groups[["GEO_ID", "geometry"]], on="GEO_ID", how="left")
+ self.svi_data_shp = gpd.GeoDataFrame(self.svi_data_shp)
+ # Delete the rows that do not have a geometry column
+ self.svi_data_shp = self.svi_data_shp.loc[self.svi_data_shp["geometry"].notnull()]
+ #self.svi_data_shp.drop(columns=columns_to_drop, inplace=True)
+ self.svi_data_shp = self.svi_data_shp.to_crs(epsg=4326)
+ self.logger.info(
+ "The geometry information was successfully added to the social vulnerability information"
+ )
\ No newline at end of file
diff --git a/hydromt_fiat/workflows/utils.py b/hydromt_fiat/workflows/utils.py
index 69d9d45a..c6c4511e 100644
--- a/hydromt_fiat/workflows/utils.py
+++ b/hydromt_fiat/workflows/utils.py
@@ -1,3 +1,6 @@
+import pandas as pd
+
+
def detect_delimiter(csvFile):
"""From stackoverflow
https://stackoverflow.com/questions/16312104/can-i-import-a-csv-file-and-automatically-infer-the-delimiter
@@ -10,3 +13,9 @@ def detect_delimiter(csvFile):
return ","
# default delimiter
return ","
+
+
+def get_us_county_numbers(county_names: list, states_counties_table: pd.DataFrame) -> list:
+ county_numbers = list(states_counties_table.loc[states_counties_table["COUNTYNAME"].isin(county_names), "COUNTYFP"].values)
+ county_numbers = [str(cn).zfill(3) for cn in county_numbers]
+ return county_numbers
\ No newline at end of file
diff --git a/hydromt_fiat/workflows/vulnerability.py b/hydromt_fiat/workflows/vulnerability.py
index 23fe6d82..a2553acf 100644
--- a/hydromt_fiat/workflows/vulnerability.py
+++ b/hydromt_fiat/workflows/vulnerability.py
@@ -6,6 +6,7 @@
from typing import List
from pathlib import Path
from typing import Union, Tuple
+import math
class Vulnerability:
@@ -15,13 +16,50 @@ def __init__(
logger: logging.Logger = None,
fn: Union[str, Path] = None,
):
- """To create, add, and update damage functions to use in Delft-FIAT.
+ """A class for creating, adding, and updating damage functions to use in
+ Delft-FIAT.
- Parameters
+ Attributes
----------
- unit : str, optional
+ hazard_name : str
+ The name of the hazard variable in the damage functions.
+ hazard_values : list
+ The hazard values of the damage functions. This variable is updated when a
+ new damage function is added which has different hazard values than the
+ current.
+ functions : dict
+ A dictionary of the damage functions fractions.
+ unit : str
The unit of the hazard values in the damage functions (e.g. of the water
depth), by default "m"
+ extraction_method : dict
+ A dictionary of the extraction method for each damage function.
+ logger : logging.Logger
+ A logger object for logging messages.
+
+ Methods
+ -------
+ __init__(self, unit='m', logger=None, fn=None)
+ Initializes a new Vulnerability object.
+ read(self, fn)
+ Reads the vulnerability data from a CSV file.
+ from_csv(self, fn)
+ Reads in one or multiple CSVs containing damage functions and adds them to
+ the vulnerability functions variable.
+ from_table(self, vulnerability_functions)
+ Reads an existing Delft-FIAT damage function (i.e. vulnerability_curves.csv)
+ file.
+ add(self, name, hazard_values, fraction_values)
+ Adds a vulnerability function.
+ add_multiple(self, names, hazard_values, fraction_values_list)
+ Adds multiple vulnerability functions.
+ update(self, name, hazard_values, fraction_values)
+ Updates an existing vulnerability function.
+ set_area_extraction_methods(self, method='mean')
+ Sets the area extraction method for all damage functions.
+ get_new_hazard_values(self, hazard_values)
+ Returns the new hazard values of the existing and to be added damage
+ functions.
"""
self.hazard_name = f"water depth [{unit}]"
self.hazard_values = list()
@@ -183,13 +221,22 @@ def update_single_df(
new_fraction_values: List[float],
combined_hazard_values: List[float],
):
- if len(new_fraction_values) == len(combined_hazard_values):
+ len_new_fraction_values = len(new_fraction_values)
+ len_combined_hazard_values = len(combined_hazard_values)
+
+ if len_new_fraction_values == len_combined_hazard_values:
_new_fraction_values = new_fraction_values
- elif len(new_fraction_values) < len(combined_hazard_values):
+ elif len_new_fraction_values < len_combined_hazard_values:
# Tnterpolate the existing damage functions to the new damage function hazard values
_new_fraction_values = self.interpolate_damage_function(
new_hazard_values, new_fraction_values, combined_hazard_values
)
+ elif len_new_fraction_values > len_combined_hazard_values:
+ difference = len_new_fraction_values - len_combined_hazard_values
+ if (max(new_fraction_values) == new_fraction_values[-2]) and (difference == 1):
+ _new_fraction_values = new_fraction_values[:-1]
+ else:
+ NotImplemented
# Add the vulnerability function to the functions variable
self.functions[name] = _new_fraction_values
@@ -242,6 +289,46 @@ def set_area_extraction_method(
{k: method for k in function_selection if k in self.functions.keys()}
)
+ def create_step_function(
+ self,
+ name: str,
+ threshold_value: float = 0.6,
+ min_hazard_value: float = 0,
+ max_hazard_value: float = 10,
+ step_hazard_value: float = 1.0,
+ ):
+ # list before threshold
+ if threshold_value <= 1:
+ list_bt = np.arange(min_hazard_value, threshold_value, 0.01).tolist()
+ del list_bt[1:-1]
+ list_bt.append(threshold_value)
+ else:
+ list_bt = np.arange(0, threshold_value, step_hazard_value).tolist()
+ list_bt.append(threshold_value - 0.01)
+ list_bt.append(threshold_value)
+ list_bt = [round(elem, 2) for elem in list_bt]
+
+ # list after threshold
+ if threshold_value == math.ceil(threshold_value):
+ list_at = np.arange(
+ math.ceil(threshold_value) + 1, max_hazard_value + 1, step_hazard_value
+ ).tolist()
+ else:
+ list_at = np.arange(
+ math.ceil(threshold_value), max_hazard_value + 1, step_hazard_value
+ ).tolist()
+ # merge list before and after threshold
+ hazard_values = list_bt + list_at
+ # create fraction_values
+ fraction_values = [
+ 0 if value < threshold_value else 1 for value in hazard_values
+ ]
+
+ self.add(name, hazard_values, fraction_values)
+
+ # Set the area extraction method
+ self.set_area_extraction_methods()
+
def get_damage_function_names(self):
return list(self.functions.keys())
@@ -277,13 +364,20 @@ def get_vulnerability_functions_from_one_file(
if vulnerability_values.max() > 1:
vulnerability_values = vulnerability_values / 100
- vf_names = df_identifiers_linking["Name"].values
+ vf_names = [
+ name + "_" + type
+ for name, type in zip(
+ df_identifiers_linking["FIAT Damage Function Name"].values,
+ df_identifiers_linking["Damage Type"].values,
+ )
+ ]
self.add_multiple(vf_names, hazard_values, vulnerability_values)
@staticmethod
def get_identifier_names(
- df: pd.DataFrame, to_remove: list = ["Name", "Link", "Damage Type"]
+ df: pd.DataFrame,
+ to_remove: list = ["FIAT Damage Function Name", "Exposure Link", "Damage Type"],
) -> list:
"""_summary_
@@ -519,7 +613,7 @@ def weighted_average_damage_function(
return list(new_damage_fractions)
- def get_table_and_metadata(self):
+ def get_table(self):
"""Transforms the damage functions into a pandas DataFrame.
Returns
@@ -532,7 +626,6 @@ def get_table_and_metadata(self):
vf_values = np.concatenate([hazard_values, vf_values], axis=1)
vf_names_header = np.append(self.hazard_name, list(self.functions.keys()))
- metadata = [f"#UNIT={self.unit}"]
# Add the extraction method under the unit header
column_names = list()
@@ -545,7 +638,17 @@ def get_table_and_metadata(self):
vf_fiat_format = np.concatenate([vf_names_header, vf_values])
df = pd.DataFrame(vf_fiat_format, columns=column_names)
- return df, metadata
+ return df
+
+ def get_metadata(self) -> List[str]:
+ """Retrieves the vulnerability metadata.
+
+ Returns
+ -------
+ List[str]
+ The list with vulnerability metadata
+ """
+ return [f"#UNIT={self.unit}"]
def get_new_hazard_values(self, additional_hazard_values):
all_hazard_values = additional_hazard_values + self.hazard_values
@@ -586,10 +689,10 @@ def interpolate_damage_function(
df_output.interpolate(method="index", limit_area="inside", inplace=True)
# Second: fill the nan values before the first value per column (with 0)
- df_output.fillna(method="bfill", inplace=True)
+ df_output.bfill(inplace=True)
# Third: fill the nan values after the later value per column (with the same value as the last value)
- df_output.fillna(method="ffill", inplace=True)
+ df_output.ffill(inplace=True)
# Reset the index to a column
df_output.reset_index(inplace=True)
diff --git a/pyproject.toml b/pyproject.toml
index 88ac08f6..85874e03 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -10,15 +10,16 @@ authors = [
{name = "Mario Fuentes Monjaraz", email = "mario.fuentesmonjaraz@deltares.nl"},
{name = "Luis Rodriguez Galvez", email = "luis.rodriguez@deltares.nl"},
{name = "Lieke Meijer", email = "lieke.meijer@deltares.nl"},
+ {name = "Sarah Rautenbach", email = "sarah.rautenbach@deltares.nl"},
]
requires-python = ">=3.8"
dependencies = [
"hydromt==0.8.0",
"hydromt_sfincs",
"geopandas",
+ "geopy",
"numpy",
"pandas",
- "pygeos",
"pyogrio",
"tomli-w",
"tomli",
@@ -26,7 +27,8 @@ dependencies = [
"pydantic==1.10.9",
"osmnx",
"census",
- "us"
+ "us",
+ "xarray-spatial"
]
readme = "README.rst"
classifiers = [
@@ -34,8 +36,8 @@ classifiers = [
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"Intended Audience :: Science/Research",
- "Topic :: Scientific/Engineering :: Hydrology",
- "License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)",
+ "Topic :: Scientific/Engineering :: Climate impact",
+ "License :: OSI Approved :: MIT License",
"Programming Language :: Python :: 3",
]
dynamic = ['version', 'description']
@@ -66,4 +68,4 @@ target-version = ['py38']
[tool.flit.sdist]
include = ["hydromt_fiat"]
-exclude = ["docs", "examples", "envs", "tests", "binder", ".github"]
\ No newline at end of file
+exclude = ["docs", "examples", "envs", "tests", "binder", ".github"]
diff --git a/tests/data/correct/test_hazus_vulnerability_curves.csv b/tests/data/correct/test_hazus_vulnerability_curves.csv
index 81d37acf..b7baee0f 100644
--- a/tests/data/correct/test_hazus_vulnerability_curves.csv
+++ b/tests/data/correct/test_hazus_vulnerability_curves.csv
@@ -1,32 +1,37 @@
#UNIT=feet
-#METHOD,max,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean
-water depth [feet],AGR1,COM1,COM10,COM2,COM3,COM4,COM5,COM6,COM7,COM8,COM9,EDU1,GOV1,GOV2,IND1,IND2,IND3,IND4,IND5,IND6,REL1,RES1-1SNB,RES1-1SWB,RES1-2SNB,RES1-2SWB,RES1-3SNB,RES1-3SWB,RES2,RES3A,RES3B,RES3C,RES3D,RES3E,RES3F,RES4,RES5,RES6
--4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,0.0,0.04,0.0,0.03,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
--3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,0.0,0.04,0.0,0.03,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
--2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.07,0.0,0.08,0.0,0.06,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
--1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.11,0.0,0.14,0.0,0.1,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
-0.0,0.0,0.01,0.0,0.0,0.0,0.02,0.0,0.0,0.02,0.01,0.0,0.0,0.0,0.0,0.01,0.01,0.0,0.0,0.0,0.0,0.0,0.18,0.17,0.11,0.19,0.05,0.12,0.11,0.12,0.12,0.12,0.12,0.12,0.12,0.0,0.0,0.0
-1.0,0.06,0.09,0.03,0.05,0.09,0.11,0.11,0.0,0.11,0.09,0.02,0.05,0.05,0.07,0.1,0.09,0.13,0.1,0.13,0.22,0.1,0.22,0.21,0.12,0.21,0.08,0.14,0.44,0.25,0.25,0.25,0.25,0.25,0.25,0.03,0.07,0.07
-2.0,0.11,0.14,0.05,0.08,0.12,0.16,0.11,0.0,0.12,0.11,0.04,0.07,0.08,0.1,0.12,0.14,0.14,0.14,0.14,0.31,0.11,0.25,0.29,0.14,0.26,0.12,0.2,0.63,0.5,0.5,0.5,0.5,0.5,0.5,0.05,0.1,0.1
-3.0,0.15,0.16,0.06,0.11,0.13,0.22,0.12,0.2,0.13,0.12,0.05,0.09,0.13,0.11,0.15,0.17,0.19,0.18,0.19,0.37,0.11,0.28,0.34,0.18,0.29,0.17,0.25,0.73,0.75,0.75,0.75,0.75,0.75,0.75,0.06,0.14,0.14
-4.0,0.19,0.18,0.07,0.13,0.16,0.28,0.13,0.25,0.14,0.14,0.05,0.09,0.14,0.12,0.19,0.22,0.22,0.22,0.22,0.43,0.12,0.3,0.38,0.2,0.34,0.19,0.31,0.78,1.0,1.0,1.0,1.0,1.0,1.0,0.07,0.15,0.15
-5.0,0.25,0.2,0.08,0.16,0.19,0.35,0.15,0.3,0.16,0.16,0.05,0.1,0.14,0.15,0.22,0.26,0.25,0.26,0.25,0.47,0.12,0.31,0.43,0.22,0.39,0.22,0.36,0.79,1.0,1.0,1.0,1.0,1.0,1.0,0.09,0.15,0.15
-6.0,0.3,0.23,0.1,0.19,0.22,0.38,0.17,0.35,0.17,0.18,0.06,0.11,0.15,0.17,0.26,0.3,0.28,0.34,0.28,0.5,0.13,0.4,0.5,0.24,0.44,0.24,0.38,0.81,1.0,1.0,1.0,1.0,1.0,1.0,0.12,0.16,0.16
-7.0,0.35,0.26,0.13,0.22,0.25,0.41,0.19,0.4,0.18,0.2,0.08,0.13,0.17,0.2,0.3,0.32,0.3,0.41,0.3,0.54,0.14,0.43,0.5,0.26,0.5,0.25,0.41,0.82,1.0,1.0,1.0,1.0,1.0,1.0,0.14,0.18,0.18
-8.0,0.41,0.3,0.17,0.25,0.28,0.44,0.22,0.43,0.2,0.22,0.1,0.15,0.19,0.23,0.35,0.35,0.33,0.42,0.33,0.57,0.14,0.43,0.54,0.3,0.55,0.3,0.44,0.83,1.0,1.0,1.0,1.0,1.0,1.0,0.18,0.2,0.2
-9.0,0.46,0.34,0.21,0.29,0.32,0.47,0.24,0.47,0.22,0.26,0.12,0.17,0.22,0.27,0.39,0.37,0.34,0.42,0.34,0.61,0.15,0.45,0.55,0.34,0.57,0.35,0.48,0.84,1.0,1.0,1.0,1.0,1.0,1.0,0.21,0.23,0.23
-10.0,0.51,0.38,0.25,0.32,0.35,0.5,0.28,0.5,0.24,0.29,0.15,0.2,0.26,0.31,0.42,0.39,0.36,0.45,0.36,0.63,0.17,0.46,0.55,0.38,0.59,0.38,0.5,0.85,1.0,1.0,1.0,1.0,1.0,1.0,0.26,0.26,0.26
-11.0,0.57,0.42,0.3,0.37,0.39,0.54,0.31,0.53,0.27,0.33,0.2,0.24,0.31,0.35,0.48,0.43,0.39,0.47,0.39,0.64,0.19,0.47,0.57,0.39,0.61,0.39,0.52,0.86,1.0,1.0,1.0,1.0,1.0,1.0,0.31,0.3,0.3
-12.0,0.63,0.47,0.35,0.41,0.43,0.57,0.34,0.55,0.3,0.37,0.24,0.28,0.37,0.4,0.5,0.46,0.4,0.49,0.4,0.65,0.24,0.47,0.58,0.4,0.63,0.4,0.54,0.88,1.0,1.0,1.0,1.0,1.0,1.0,0.36,0.34,0.34
-13.0,0.7,0.51,0.41,0.45,0.47,0.59,0.37,0.57,0.34,0.41,0.29,0.33,0.44,0.44,0.51,0.48,0.42,0.5,0.42,0.67,0.3,0.49,0.6,0.42,0.65,0.42,0.56,0.89,1.0,1.0,1.0,1.0,1.0,1.0,0.41,0.38,0.38
-14.0,0.75,0.55,0.47,0.49,0.5,0.62,0.4,0.6,0.37,0.45,0.35,0.39,0.51,0.48,0.53,0.5,0.42,0.5,0.42,0.68,0.38,0.5,0.62,0.43,0.66,0.43,0.57,0.9,1.0,1.0,1.0,1.0,1.0,1.0,0.46,0.42,0.42
-15.0,0.79,0.58,0.52,0.52,0.54,0.66,0.44,0.6,0.41,0.5,0.42,0.45,0.59,0.52,0.54,0.51,0.43,0.5,0.43,0.69,0.45,0.5,0.63,0.44,0.68,0.44,0.59,0.91,1.0,1.0,1.0,1.0,1.0,1.0,0.5,0.47,0.47
-16.0,0.82,0.61,0.58,0.55,0.57,0.68,0.48,0.6,0.44,0.53,0.49,0.52,0.65,0.56,0.55,0.54,0.43,0.5,0.43,0.7,0.52,0.5,0.65,0.45,0.69,0.45,0.61,0.92,1.0,1.0,1.0,1.0,1.0,1.0,0.54,0.52,0.52
-17.0,0.84,0.64,0.65,0.58,0.61,0.7,0.51,0.6,0.48,0.57,0.56,0.59,0.7,0.6,0.55,0.55,0.44,0.5,0.44,0.71,0.58,0.51,0.67,0.47,0.71,0.47,0.63,0.94,1.0,1.0,1.0,1.0,1.0,1.0,0.58,0.57,0.57
-18.0,0.87,0.67,0.71,0.61,0.64,0.72,0.55,0.6,0.51,0.6,0.62,0.64,0.74,0.64,0.56,0.57,0.44,0.5,0.44,0.72,0.64,0.51,0.69,0.48,0.72,0.48,0.65,0.95,1.0,1.0,1.0,1.0,1.0,1.0,0.62,0.62,0.62
-19.0,0.89,0.69,0.76,0.63,0.68,0.74,0.59,0.6,0.54,0.63,0.68,0.69,0.79,0.68,0.56,0.59,0.44,0.5,0.44,0.73,0.69,0.52,0.7,0.49,0.74,0.49,0.67,0.96,1.0,1.0,1.0,1.0,1.0,1.0,0.66,0.67,0.67
-20.0,0.9,0.71,0.81,0.66,0.71,0.76,0.63,0.6,0.56,0.66,0.74,0.74,0.83,0.72,0.57,0.6,0.44,0.5,0.44,0.74,0.74,0.52,0.72,0.5,0.75,0.5,0.69,0.97,1.0,1.0,1.0,1.0,1.0,1.0,0.7,0.72,0.72
-21.0,0.92,0.74,0.86,0.68,0.75,0.77,0.67,0.6,0.59,0.69,0.8,0.79,0.87,0.76,0.57,0.62,0.44,0.5,0.44,0.75,0.78,0.53,0.74,0.52,0.77,0.52,0.71,0.98,1.0,1.0,1.0,1.0,1.0,1.0,0.74,0.77,0.77
-22.0,0.93,0.76,0.91,0.7,0.78,0.78,0.71,0.6,0.61,0.73,0.86,0.84,0.91,0.8,0.57,0.63,0.45,0.5,0.45,0.76,0.82,0.53,0.76,0.53,0.79,0.53,0.73,0.99,1.0,1.0,1.0,1.0,1.0,1.0,0.78,0.82,0.82
-23.0,0.95,0.78,0.96,0.71,0.82,0.79,0.75,0.6,0.64,0.76,0.92,0.89,0.95,0.84,0.58,0.65,0.45,0.5,0.45,0.76,0.85,0.54,0.77,0.54,0.8,0.54,0.75,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.82,0.87,0.87
-24.0,0.96,0.8,1.0,0.73,0.85,0.8,0.79,0.6,0.66,0.78,0.98,0.94,0.98,0.88,0.58,0.66,0.45,0.5,0.45,0.77,0.88,0.54,0.79,0.56,0.82,0.56,0.77,1.0,1.0,1.0,1.0,1.0,1.0,1.0,0.86,0.92,0.92
+#METHOD,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean,mean
+water depth [feet],AGR1_structure,COM1_structure,COM10_structure,COM2_structure,COM3_structure,COM4_structure,COM5_structure,COM6_structure,COM7_structure,COM8_structure,COM9_structure,EDU1_structure,EDU2_structure,GOV1_structure,GOV2_structure,IND1_structure,IND2_structure,IND3_structure,IND4_structure,IND5_structure,IND6_structure,REL1_structure,RES1-1SNB_structure,RES1-1SWB_structure,RES1-2SNB_structure,RES1-2SWB_structure,RES1-3SNB_structure,RES1-3SWB_structure,RES2_structure,RES3A_structure,RES3B_structure,RES3C_structure,RES3D_structure,RES3E_structure,RES3F_structure,RES4_structure,RES5_structure,RES6_structure,AGR1_content,COM1_content,COM10_content,COM2_content,COM3_content,COM4_content,COM5_content,COM6_content,COM7_content,COM8_content,COM9_content,EDU1_content,EDU2_content,GOV1_content,GOV2_content,IND1_content,IND2_content,IND3_content,IND4_content,IND5_content,IND6_content,REL1_content,RES1-1SNB_content,RES1-1SWB_content,RES1-2SNB_content,RES1-2SWB_content,RES1-3SNB_content,RES1-3SWB_content,RES2_content,RES3A_content,RES3B_content,RES3C_content,RES3D_content,RES3E_content,RES3F_content,RES4_content,RES5_content,RES6_content
+-9.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-8.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.017,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1e-06,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-7.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.007,0.0,0.017,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.008,0.0,0.01,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-6.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.008,0.0,0.019,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.021,0.0,0.023,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-5.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.024,0.0,0.029,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.037,0.0,0.037,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-4.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.052,0.0,0.047,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.057,0.0,0.052,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-3.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.09,0.0,0.072,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.08,0.0,0.068,0.0,0.05,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
+-2.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.138,0.0,0.102,0.0,0.03,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.105,0.0,0.084,0.0,0.07,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
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diff --git a/tests/data/vulnerability_test_file_input_miami_landuse.csv b/tests/data/vulnerability_test_file_input_miami_landuse.csv
new file mode 100644
index 00000000..c172271d
--- /dev/null
+++ b/tests/data/vulnerability_test_file_input_miami_landuse.csv
@@ -0,0 +1,55 @@
+FIAT Damage Function Name,Exposure Link,Damage Type,ID,Occupancy,Description,Source
+RES1-1SNB,"Single-Family, Med.-Density (2-5 DU/Gross Acre).",structure,2,RES1-1SNB-S-MostLikely,"Res 1, 1 Story no Basement",USACE - IWR
+RES3A,"Multi-Family, Low-Density (Under 25 DU/Gross Acre).",structure,23,RES3-S-MostLikely,"Condominium, living area on multiple floors",USACE - IWR
+RES1-1SNB,"Single-Family, High Density (Over 5 DU/Gross Acre, other than Townhouses, Duplexes and Mobile Homes).",structure,2,RES1-1SNB-S-MostLikely,"Res 1, 1 Story no Basement",USACE - IWR
+RES2,Vacant Government owned or controlled.,structure,20,RES2-S-MostLikely,Mobile home,USACE - IWR
+RES2,"Vacant, Non-Protected, Privately-Owned.",structure,20,RES2-S-MostLikely,Mobile home,USACE - IWR
+RES1-1SNB,"Single-Family, Low-Density (Under 2 DU/Gross Acre).",structure,2,RES1-1SNB-S-MostLikely,"Res 1, 1 Story no Basement",USACE - IWR
+GOV1 ,"Houses of Worship and Religious, and associated uses (parking, retreat houses, residencies, childcare, etc.).",structure,631,GOV1,"Average government services, structure",USACE - Galveston
+RES3A,Two-Family (Duplexes).,structure,23,RES3-S-MostLikely,"Condominium, living area on multiple floors",USACE - IWR
+EDU1,"Public Schools, Including Playgrounds (K-12, Vocational Ed., Day Care and Child Nurseries).",structure,53,EDU1-S-MostLikely,Average school,USACE - IWR
+GOV1 ,"Nursing homes, Assisted living facilities, and Adult congregate living quarters",structure,631,GOV1,"Average government services, structure",USACE - Galveston
+GOV1 ,Plant Nurseries (Includes Sod Farms and Ornamental Nurseries).,structure,631,GOV1,"Average government services, structure",USACE - Galveston
+RES1-1SNB,"Private Recreational Facilities Associated with private Residential Developments, except marinas/yacht basins, includes landscape and open spaces associated to residential, commercial and office developments.",structure,2,RES1-1SNB-S-MostLikely,"Res 1, 1 Story no Basement",USACE - IWR
+RES1-1SNB,Townhouses.,structure,2,RES1-1SNB-S-MostLikely,"Res 1, 1 Story no Basement",USACE - IWR
+COM8 ,Office Building.,structure,493,COM8,"Average Entertainment/Recreation, Structure",USACE - Galveston
+IND5,"Industrial Intensive, Office type of use",structure,591,IND5,"Average High Technology, structure",USACE - Galveston
+GOV2 ,Governmental/Public Administration (Other than Military or Penal).,structure,640,GOV2,"Average emergency response, Structure",USACE - Galveston
+GOV2 ,"Street right-of-way and entrance features both public and private, and utility easements.",structure,640,GOV2,"Average emergency response, Structure",USACE - Galveston
+GOV2 ,Municipal Operated Parks,structure,640,GOV2,"Average emergency response, Structure",USACE - Galveston
+RES3A,Residential MF-- government-owned or government subsidized multi-family residential or elderly housing,structure,23,RES3-S-MostLikely,"Condominium, living area on multiple floors",USACE - IWR
+COM2 ,"Sales and Services (Wholesale facilities, Spot commercial, strip commercial, neighborhood shopping centers/plazas). Excludes office facilities.",structure,341,COM2,"Average wholesale, Structure",USACE - Galveston
+EDU1 ,"Private Schools, Including Playgrounds (K-12, Vocational Ed., Day Care and Child Nurseries).",structure,53,EDU1-S-MostLikely,Average school,USACE - IWR
+GOV2 ,"Hospitals, clinics, medical offices and/or dental facilities",structure,640,GOV2,"Average emergency response, Structure",USACE - Galveston
+COM2 ,Shopping Centers (Regional and Community).,structure,341,COM2,"Average wholesale, Structure",USACE - Galveston
+IND3 ,Water Supply Plants.,structure,575,IND3,"Average Food/Drug/Chem, Structure",USACE - Galveston
+COM8 ,"Sports Stadiums, Arenas, and Tracks.",structure,493,COM8,"Average Entertainment/Recreation, Structure",USACE - Galveston
+GOV2 ,County Operated Parks.,structure,640,GOV2,"Average emergency response, Structure",USACE - Galveston
+GOV2 ,"Other Nature Preserves and Protected Areas (State Mangrove Preserves, Turkey Point Wilderness Area, Great Cypress Swamp Preserves, and acquired government owned EEL sites).",structure,640,GOV2,"Average emergency response, Structure",USACE - Galveston
+RES1-1SNB,"Single-Family, Med.-Density (2-5 DU/Gross Acre).",content,2,RES1-1SNB-S-MostLikely,"Res 1, 1 Story no Basement",USACE - IWR
+RES3A,"Multi-Family, Low-Density (Under 25 DU/Gross Acre).",content,23,RES3-S-MostLikely,"Condominium, living area on multiple floors",USACE - IWR
+RES1-1SNB,"Single-Family, High Density (Over 5 DU/Gross Acre, other than Townhouses, Duplexes and Mobile Homes).",content,2,RES1-1SNB-S-MostLikely,"Res 1, 1 Story no Basement",USACE - IWR
+RES2,Vacant Government owned or controlled.,content,20,RES2-S-MostLikely,Mobile home,USACE - IWR
+RES2,"Vacant, Non-Protected, Privately-Owned.",content,20,RES2-S-MostLikely,Mobile home,USACE - IWR
+RES1-1SNB,"Single-Family, Low-Density (Under 2 DU/Gross Acre).",content,2,RES1-1SNB-S-MostLikely,"Res 1, 1 Story no Basement",USACE - IWR
+GOV1 ,"Houses of Worship and Religious, and associated uses (parking, retreat houses, residencies, childcare, etc.).",content,631,GOV1,"Average government services, structure",USACE - Galveston
+RES3A,Two-Family (Duplexes).,content,23,RES3-S-MostLikely,"Condominium, living area on multiple floors",USACE - IWR
+EDU1,"Public Schools, Including Playgrounds (K-12, Vocational Ed., Day Care and Child Nurseries).",content,53,EDU1-S-MostLikely,Average school,USACE - IWR
+GOV1 ,"Nursing homes, Assisted living facilities, and Adult congregate living quarters",content,631,GOV1,"Average government services, structure",USACE - Galveston
+GOV1 ,Plant Nurseries (Includes Sod Farms and Ornamental Nurseries).,content,631,GOV1,"Average government services, structure",USACE - Galveston
+RES1-1SNB,"Private Recreational Facilities Associated with private Residential Developments, except marinas/yacht basins, includes landscape and open spaces associated to residential, commercial and office developments.",content,2,RES1-1SNB-S-MostLikely,"Res 1, 1 Story no Basement",USACE - IWR
+RES1-1SNB,Townhouses.,content,2,RES1-1SNB-S-MostLikely,"Res 1, 1 Story no Basement",USACE - IWR
+COM8 ,Office Building.,content,493,COM8,"Average Entertainment/Recreation, Structure",USACE - Galveston
+IND5,"Industrial Intensive, Office type of use",content,591,IND5,"Average High Technology, structure",USACE - Galveston
+GOV2 ,Governmental/Public Administration (Other than Military or Penal).,content,640,GOV2,"Average emergency response, Structure",USACE - Galveston
+GOV2 ,"Street right-of-way and entrance features both public and private, and utility easements.",content,640,GOV2,"Average emergency response, Structure",USACE - Galveston
+GOV2 ,Municipal Operated Parks,content,640,GOV2,"Average emergency response, Structure",USACE - Galveston
+RES3A,Residential MF-- government-owned or government subsidized multi-family residential or elderly housing,content,23,RES3-S-MostLikely,"Condominium, living area on multiple floors",USACE - IWR
+COM2 ,"Sales and Services (Wholesale facilities, Spot commercial, strip commercial, neighborhood shopping centers/plazas). Excludes office facilities.",content,341,COM2,"Average wholesale, Structure",USACE - Galveston
+EDU1 ,"Private Schools, Including Playgrounds (K-12, Vocational Ed., Day Care and Child Nurseries).",content,53,EDU1-S-MostLikely,Average school,USACE - IWR
+GOV2 ,"Hospitals, clinics, medical offices and/or dental facilities",content,640,GOV2,"Average emergency response, Structure",USACE - Galveston
+COM2 ,Shopping Centers (Regional and Community).,content,341,COM2,"Average wholesale, Structure",USACE - Galveston
+IND3 ,Water Supply Plants.,content,575,IND3,"Average Food/Drug/Chem, Structure",USACE - Galveston
+COM8 ,"Sports Stadiums, Arenas, and Tracks.",content,493,COM8,"Average Entertainment/Recreation, Structure",USACE - Galveston
+GOV2 ,County Operated Parks.,content,640,GOV2,"Average emergency response, Structure",USACE - Galveston
+GOV2 ,"Other Nature Preserves and Protected Areas (State Mangrove Preserves, Turkey Point Wilderness Area, Great Cypress Swamp Preserves, and acquired government owned EEL sites).",content,640,GOV2,"Average emergency response, Structure",USACE - Galveston
diff --git a/tests/test_SVI.py b/tests/test_SVI.py
deleted file mode 100644
index 3d1c3437..00000000
--- a/tests/test_SVI.py
+++ /dev/null
@@ -1,60 +0,0 @@
-from hydromt_fiat.fiat import FiatModel
-from hydromt.log import setuplog
-from pathlib import Path
-import pytest
-import shutil
-
-EXAMPLEDIR = Path(
- "P:/11207949-dhs-phaseii-floodadapt/Model-builder/Delft-FIAT/local_test_database"
-)
-DATADIR = Path().absolute() / "hydromt_fiat" / "data"
-
-_cases = {
- "Test_SVI": {
- "data_catalogue": DATADIR / "hydromt_fiat_catalog_USA.yml",
- "folder": "Test_SVI",
- "configuration": {
- "setup_social_vulnerability_index": {
- "census_key": "495a349ce22bdb1294b378fb199e4f27e57471a9",
- "codebook_fn": "social_vulnerability",
- "state_abbreviation": "SC",
- "blockgroup_fn": "blockgroup_shp_data",
- }
- },
- }
-}
-
-
-@pytest.mark.parametrize("case", list(_cases.keys()))
-# @pytest.mark.skip(reason="Needs to be updated")
-def test_SVI(case):
- # Read model in examples folder.
- root = EXAMPLEDIR.joinpath(_cases[case]["folder"])
- if root.exists():
- shutil.rmtree(root)
- logger = setuplog("hydromt_fiat", log_level=10)
- data_libs = EXAMPLEDIR.joinpath(_cases[case]["data_catalogue"])
- hyfm = FiatModel(root=root, mode="w", data_libs=data_libs, logger=logger)
- # config = configread(_cases[case]["configuration"])
-
- # Now we will add data from the user to the data catalog.
- to_add = {
- "blockgroup_shp_data": {
- "path": str(
- DATADIR
- / "social_vulnerability"
- / "test_blockgroup_shp"
- / "tl_2022_45_bg.shp"
- ),
- "data_type": "GeoDataFrame",
- "driver": "vector",
- "crs": 4326,
- "category": "social_vulnerability",
- }
- }
-
- hyfm.data_catalog.from_dict(to_add)
- hyfm.build(opt=_cases[case]["configuration"])
-
- assert hyfm
- print("hi")
diff --git a/tests/test_SVI_exposure.py b/tests/test_SVI_exposure.py
index b6b6b504..2f0c9b82 100644
--- a/tests/test_SVI_exposure.py
+++ b/tests/test_SVI_exposure.py
@@ -45,24 +45,23 @@
"output_vector_name": "spatial.gpkg",
},
"setup_vulnerability": {
- "vulnerability_fn": "hazus_vulnerability_curves",
- "vulnerability_identifiers_and_linking_fn": ".\\examples\\data\\vulnerability_test_file_input.csv",
+ "vulnerability_fn": "default_vulnerability_curves",
+ "vulnerability_identifiers_and_linking_fn": "default_hazus_iwr_linking",
"functions_mean": "default",
"functions_max": ["AGR1"],
"unit": "feet",
},
- "setup_exposure_vector": {
+ "setup_exposure_buildings": {
"asset_locations": "NSI",
"occupancy_type": "NSI",
"max_potential_damage": "NSI",
- "ground_floor_height": 1,
+ "ground_floor_height": "NSI",
"unit": "ft",
},
"setup_social_vulnerability_index": {
"census_key": "495a349ce22bdb1294b378fb199e4f27e57471a9",
"codebook_fn": "social_vulnerability",
- "state_abbreviation": "SC",
- "blockgroup_fn": "blockgroup_shp_data",
+ "year_data": 2021,
},
},
}
@@ -78,27 +77,19 @@ def test_SVI_exposure(case):
shutil.rmtree(root)
logger = setuplog("hydromt_fiat", log_level=10)
data_libs = EXAMPLEDIR.joinpath(_cases[case]["data_catalogue"])
- hyfm = FiatModel(root=root, mode="w", data_libs=data_libs, logger=logger)
-
- # Now we will add data from the user to the data catalog.
- to_add = {
- "blockgroup_shp_data": {
- "path": str(
- DATADIR
- / "social_vulnerability"
- / "test_blockgroup_shp"
- / "tl_2022_45_bg.shp"
- ),
- "data_type": "GeoDataFrame",
- "driver": "vector",
- "crs": 4326,
- "category": "social_vulnerability",
- }
- }
+ fm = FiatModel(root=root, mode="w", data_libs=data_libs, logger=logger)
region = gpd.GeoDataFrame.from_features(_cases[case]["region"], crs=4326)
+ fm.build(region={"geom": region}, opt=_cases[case]["configuration"])
+ fm.write()
+
+ # Check if the exposure data exists
+ assert root.joinpath("exposure", "buildings.gpkg").exists()
+ assert root.joinpath("exposure", "exposure.csv").exists()
+ assert root.joinpath("exposure", "region.gpkg").exists()
- hyfm.data_catalog.from_dict(to_add)
- hyfm.build(region={"geom": region}, opt=_cases[case]["configuration"])
+ # Check if the social vulnerability data exists
+ assert root.joinpath("exposure", "SVI", "social_vulnerability_scores.csv").exists()
- assert hyfm
+ # Check if the vulnerability data exists
+ assert root.joinpath("vulnerability", "vulnerability_curves.csv").exists()
\ No newline at end of file
diff --git a/tests/test_aggregation_areas.py b/tests/test_aggregation_areas.py
new file mode 100644
index 00000000..4ccf6ccd
--- /dev/null
+++ b/tests/test_aggregation_areas.py
@@ -0,0 +1,82 @@
+import shutil
+from pathlib import Path
+from typing import Sequence
+
+import pytest
+from _pytest.mark.structures import ParameterSet
+from hydromt.log import setuplog
+
+from hydromt_fiat.fiat import FiatModel
+from hydromt_fiat.workflows.exposure_vector import ExposureVector
+from hydromt_fiat.workflows.vulnerability import Vulnerability
+
+# Load Data
+EXAMPLEDIR = Path().resolve() / "examples" / "data" / "aggregation_zones"
+
+_cases = {
+ "aggregation_test_1": {
+ "new_root": EXAMPLEDIR / "fiat_model_aggregation1",
+ "configuration": {
+ "setup_aggregation_areas": {
+ "aggregation_area_fn": EXAMPLEDIR / "aggregation_zones" / "base_zones.gpkg",
+ "attribute_names": "ZONE_BASE",
+ "label_names": "Zoning_map",
+ }
+ },
+ },
+ "aggregation_test_2": {
+ "new_root": EXAMPLEDIR / "fiat_model_aggregation2",
+ "configuration": {
+ "setup_aggregation_areas": {
+ "aggregation_area_fn": [
+ EXAMPLEDIR / "aggregation_zones" / "base_zones.gpkg",
+ EXAMPLEDIR / "aggregation_zones" / "land_use.gpkg",
+ EXAMPLEDIR / "aggregation_zones" / "accomodation_type.gpkg",
+ ],
+ "attribute_names": ["ZONE_BASE", "LAND_USE","ACCOM"],
+ "label_names": ["Zoning_map", "Land_use_map","Accomodation_Zone"],
+ }
+ },
+ },
+}
+
+
+
+# Set up Fiat Model
+@pytest.mark.parametrize("case", list(_cases.keys()))
+def test_aggregation_areas(case: ParameterSet | Sequence[object] | object):
+ # Read model in examples folder.
+ root = EXAMPLEDIR / "fiat_model"
+ if _cases[case]["new_root"].exists():
+ shutil.rmtree(_cases[case]["new_root"])
+ logger = setuplog("hydromt_fiat", log_level=10)
+
+ fm = FiatModel(root=root, mode="r", logger=logger)
+ fm.read()
+
+ fm.build(write=False, opt=_cases[case]["configuration"])
+ fm.set_root(_cases[case]["new_root"])
+ fm.write()
+
+ # Check if the exposure object exists
+ assert isinstance(fm.exposure, ExposureVector)
+
+ # Check if the exposure database exists
+ assert not fm.exposure.exposure_db.empty
+
+ # Check if the vulnerability object exists
+ assert isinstance(fm.vulnerability, Vulnerability)
+
+ # Check if the vulnerability functions exist
+ assert len(fm.vulnerability.functions) > 0
+
+ # Check if the additional_attributes folder exists
+ assert root.joinpath("additional_attributes").exists()
+
+ # Check if the files are copied to the right folder
+ aggregation_area_fn = _cases[case]["configuration"]["setup_aggregation_areas"]["aggregation_area_fn"]
+ if isinstance(aggregation_area_fn, Path):
+ aggregation_area_fn = [aggregation_area_fn]
+
+ for a in aggregation_area_fn:
+ assert root.joinpath("additional_attributes", a.name).exists()
\ No newline at end of file
diff --git a/tests/test_building_footprints.py b/tests/test_building_footprints.py
new file mode 100644
index 00000000..42e78c77
--- /dev/null
+++ b/tests/test_building_footprints.py
@@ -0,0 +1,48 @@
+from typing import Sequence
+from _pytest.mark.structures import ParameterSet
+from hydromt_fiat.fiat import FiatModel
+from hydromt.log import setuplog
+from pathlib import Path
+import pytest
+import shutil
+
+EXAMPLEDIR = Path().resolve() / "examples" / "data" / "building_footprints"
+
+#Create test
+_cases = {
+ "join_building_footprints_to_exposure": {
+ "root": EXAMPLEDIR / "fiat_model",
+ "new_root": EXAMPLEDIR / "fiat_model_bfs",
+ "configuration": {
+ "setup_building_footprint": {
+ "building_footprint_fn": EXAMPLEDIR / "building_footprints" / "building_footprints.gpkg", # Datasource: https://github.com/microsoft/USBuildingFootprints
+ "attribute_name": "BF_FID",
+ }
+ },
+ }
+}
+
+# Set up Fiat Model
+@pytest.mark.parametrize("case", list(_cases.keys()))
+def test_building_footprints(case: ParameterSet | Sequence[object] | object):
+ # Read model in examples folder.
+ if _cases[case]["new_root"].exists():
+ shutil.rmtree(_cases[case]["new_root"])
+ logger = setuplog("hydromt_fiat", log_level=10)
+
+ fm = FiatModel(root=_cases[case]["root"], mode="r", logger=logger)
+ fm.read()
+ exposure_orig = fm.exposure.exposure_db.copy()
+
+ fm.build(write=False, opt=_cases[case]["configuration"])
+ fm.set_root(_cases[case]["new_root"])
+ fm.write()
+
+ # Check if the BF_FID column is added
+ assert "BF_FID" in fm.exposure.exposure_db.columns
+
+ # Check for Object ID duplicates
+ assert fm.exposure.exposure_db["Object ID"].duplicated().sum() == 0
+
+ # Check original exposure is same length new exposure
+ assert len(fm.exposure.exposure_db["Object ID"]) == len(exposure_orig["Object ID"])
\ No newline at end of file
diff --git a/tests/test_create_step_function.py b/tests/test_create_step_function.py
new file mode 100644
index 00000000..08d54b9f
--- /dev/null
+++ b/tests/test_create_step_function.py
@@ -0,0 +1,18 @@
+# Unit test
+
+from hydromt_fiat.workflows.vulnerability import Vulnerability
+
+def test_create_step_function():
+ x = Vulnerability()
+ name = "roads"
+ min_hazard_input = 0
+ max_hazard_input = 15
+ threshold_value = 0.5
+ step_hazard_value = 2
+ x.create_step_function("roads_2")
+ x.create_step_function(name, threshold_value, min_hazard_input, max_hazard_input, step_hazard_value)
+
+
+
+ assert x.functions == {'roads_2': [0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], 'roads': [0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}
+ assert x.hazard_values == [0.0, 0.49, 0.5, 0.59, 0.6, 1, 2.0, 3, 4.0, 5, 6.0, 7, 8.0, 9, 10.0, 11, 13, 15]
diff --git a/tests/test_equity_data.py b/tests/test_equity_data.py
new file mode 100644
index 00000000..c8260a1c
--- /dev/null
+++ b/tests/test_equity_data.py
@@ -0,0 +1,94 @@
+from hydromt_fiat.fiat import FiatModel
+from hydromt.log import setuplog
+from pathlib import Path
+import pytest
+import shutil
+import geopandas as gpd
+
+EXAMPLEDIR = Path(
+ "P:/11207949-dhs-phaseii-floodadapt/Model-builder/Delft-FIAT/local_test_database"
+)
+DATADIR = Path().absolute() / "hydromt_fiat" / "data"
+
+_region = {
+ "type": "FeatureCollection",
+ "features": [
+ {
+ "type": "Feature",
+ "properties": {},
+ "geometry": {
+ "coordinates": [
+ [
+ [-80.0808, 32.7005],
+ [-79.8756, 32.8561],
+ [-79.8756, 32.7005],
+ [-80.0808, 32.8561],
+ [-80.0808, 32.7005],
+ ]
+ ],
+ "type": "Polygon",
+ },
+ }
+ ],
+}
+
+_cases = {
+ "Test_equity_data": {
+ "data_catalogue": DATADIR / "hydromt_fiat_catalog_USA.yml",
+ "folder": "Test_equity_data",
+ "region": _region,
+ "configuration": {
+ "setup_global_settings": {"crs": "epsg:4326"},
+ "setup_output": {
+ "output_dir": "output",
+ "output_csv_name": "output.csv",
+ "output_vector_name": "spatial.gpkg",
+ },
+ "setup_vulnerability": {
+ "vulnerability_fn": "default_vulnerability_curves",
+ "vulnerability_identifiers_and_linking_fn": "default_hazus_iwr_linking",
+ "functions_mean": "default",
+ "functions_max": ["AGR1"],
+ "unit": "feet",
+ },
+ "setup_exposure_buildings": {
+ "asset_locations": "NSI",
+ "occupancy_type": "NSI",
+ "max_potential_damage": "NSI",
+ "ground_floor_height": "NSI",
+ "unit": "ft",
+ },
+ "setup_equity_data": {
+ "census_key": "495a349ce22bdb1294b378fb199e4f27e57471a9",
+ "year_data": 2021,
+ },
+ },
+ }
+}
+
+
+@pytest.mark.parametrize("case", list(_cases.keys()))
+# @pytest.mark.skip(reason="Needs to be updated")
+def test_equity_data(case):
+ # Read model in examples folder.
+ root = EXAMPLEDIR.joinpath(_cases[case]["folder"])
+ if root.exists():
+ shutil.rmtree(root)
+ logger = setuplog("hydromt_fiat", log_level=10)
+ data_libs = EXAMPLEDIR.joinpath(_cases[case]["data_catalogue"])
+ fm = FiatModel(root=root, mode="w", data_libs=data_libs, logger=logger)
+
+ region = gpd.GeoDataFrame.from_features(_cases[case]["region"], crs=4326)
+ fm.build(region={"geom": region}, opt=_cases[case]["configuration"])
+ fm.write()
+
+ # Check if the exposure data exists
+ assert root.joinpath("exposure", "buildings.gpkg").exists()
+ assert root.joinpath("exposure", "exposure.csv").exists()
+ assert root.joinpath("exposure", "region.gpkg").exists()
+
+ # Check if the equity data exists
+ assert root.joinpath("equity", "equity_data.csv").exists()
+
+ # Check if the vulnerability data exists
+ assert root.joinpath("vulnerability", "vulnerability_curves.csv").exists()
diff --git a/tests/test_exposure_api.py b/tests/test_exposure_api.py
index 2f56af53..9205e720 100644
--- a/tests/test_exposure_api.py
+++ b/tests/test_exposure_api.py
@@ -11,7 +11,7 @@ def test_hydromt_fiat_vm():
)
view_model = HydroMtViewModel(path_to_database, path_to_datacatalog)
- view_model.build_config_ini()
+ view_model.build_config_yaml()
view_model.save_data_catalog()
print("exit")
diff --git a/tests/test_ground_elevation.py b/tests/test_ground_elevation.py
new file mode 100644
index 00000000..e35c8ded
--- /dev/null
+++ b/tests/test_ground_elevation.py
@@ -0,0 +1,74 @@
+from hydromt_fiat.fiat import FiatModel
+from hydromt.log import setuplog
+from pathlib import Path
+import numpy as np
+import pytest
+import shutil
+import copy
+import rasterio
+import numpy as np
+
+
+EXAMPLEDIR = Path().absolute() / "examples" / "data" / "update_ground_elevation"
+DATADIR = Path().absolute() / "hydromt_fiat" / "data"
+DATADIR = Path(
+ "P:/11207949-dhs-phaseii-floodadapt/FloodAdapt/Test_data/Database_env_fix/static/dem"
+)
+
+_cases = {
+ "update_ground_elevation_with_dem": {
+ "dir": "fiat_model",
+ "new_root": EXAMPLEDIR / "test_update_ground_elevation",
+ "ground_elevation_file": DATADIR
+ / "charleston_14m.tif",
+ },
+}
+
+
+@pytest.mark.parametrize("case", list(_cases.keys()))
+def test_ground_elevation(case):
+ # Read model in examples folder.
+ root = EXAMPLEDIR.joinpath(_cases[case]["dir"])
+ logger = setuplog("hydromt_fiat", log_level=10)
+
+ fm = FiatModel(
+ root=root, mode="r", logger=logger
+ )
+
+ fm.read()
+
+ original_exposure = copy.deepcopy(fm.exposure.exposure_db)
+ unique_ge_original = original_exposure["Ground Elevation"].unique()
+
+ fm.exposure.setup_ground_elevation(
+ _cases[case]["ground_elevation_file"],
+ )
+
+ # Remove the new root folder if it already exists
+ if _cases[case]["new_root"].exists():
+ shutil.rmtree(_cases[case]["new_root"])
+
+ # Set the new root and write the model
+ fm.set_root(_cases[case]["new_root"])
+ fm.write()
+
+ # Check the values are updated. This will only work if the original data has "Ground Elevation" column
+ unique_ge_new = fm.exposure.exposure_db["Ground Elevation"].unique()
+ assert not np.array_equal(unique_ge_original, unique_ge_new), "The Ground Elevation is the same"
+
+ # Check if the updated values are not null
+ not_null_values = fm.exposure.exposure_db["Ground Elevation"].notnull()
+ assert not_null_values.all(), "Warning: There are null values in 'Ground Elevation' column."
+
+ # Check if the calculated values are within the maximun and minimun value of the original daster file. This function could be used to calculate
+ # Ground Elevation itself in case the ground_elevation_from_dem() function in gis.py is not accurate enough
+ raster_file_path = _cases[case]["ground_elevation_file"]
+ with rasterio.open(raster_file_path) as src:
+ nodata_value = src.nodatavals[0]
+ raster_data = src.read(1)
+ valid_values = raster_data[raster_data != nodata_value]
+ #mean_value_excluding_nodata = np.nanmean(valid_values)
+ max_value_excluding_nodata = np.nanmax(valid_values)
+ min_value_excluding_nodata = np.nanmin(valid_values)
+ valid_values = (fm.exposure.exposure_db["Ground Elevation"] >= min_value_excluding_nodata) & (fm.exposure.exposure_db["Ground Elevation"] <= max_value_excluding_nodata)
+ assert valid_values.all(), "The Ground Elevation is beyond the maximun and minimun values of the provided DEM"
diff --git a/tests/test_hazard.py b/tests/test_hazard.py
deleted file mode 100644
index b5b942cb..00000000
--- a/tests/test_hazard.py
+++ /dev/null
@@ -1,48 +0,0 @@
-from hydromt_fiat.fiat import FiatModel
-from hydromt_fiat.workflows.hazard import *
-from hydromt.config import configread
-from pathlib import Path
-import pytest
-from hydromt.log import setuplog
-
-DATASET = Path(
- "P:/11207949-dhs-phaseii-floodadapt/Model-builder/Delft-FIAT/local_test_database"
-)
-
-_cases = {
- "test_hazard": {
- "folder": "test_hazard_1",
- "ini": "test_hazard.ini",
- "catalog": "fiat_catalog.yml",
- }
-}
-
-
-@pytest.mark.parametrize("case", list(_cases.keys()))
-def test_hazard(case):
- # Read model in examples folder.
- root = DATASET.joinpath(_cases[case]["folder"])
- config_fn = DATASET.joinpath(_cases[case]["ini"])
- data_libs = DATASET.joinpath(_cases[case]["catalog"])
-
- logger = setuplog("hydromt_fiat", log_level=10)
-
- hyfm = FiatModel(
- root=root, mode="w", data_libs=data_libs, config_fn=config_fn, logger=logger
- )
-
- map_fn = configread(config_fn)["setup_hazard"]["map_fn"]
- map_type = configread(config_fn)["setup_hazard"]["map_type"]
- rp = configread(config_fn)["setup_hazard"]["rp"]
- crs = configread(config_fn)["setup_hazard"]["crs"]
- nodata = configread(config_fn)["setup_hazard"]["nodata"]
- var = configread(config_fn)["setup_hazard"]["var"]
- chunks = configread(config_fn)["setup_hazard"]["chunks"]
- configread(config_fn)["setup_hazard"]["name_catalog"]
- configread(config_fn)["setup_hazard"]["risk_output"]
- configread(config_fn)["setup_config"]["hazard_type"]
-
- params = check_parameters_type(map_fn, map_type, rp, crs, nodata, var, chunks)
- check_parameters_size(params)
-
- assert params
diff --git a/tests/test_integration.py b/tests/test_integration.py
index 81d376fb..6df264a6 100644
--- a/tests/test_integration.py
+++ b/tests/test_integration.py
@@ -44,18 +44,18 @@
"output_vector_name": "spatial.gpkg",
},
"setup_vulnerability": {
- "vulnerability_fn": "hazus_vulnerability_curves",
- "vulnerability_identifiers_and_linking_fn": ".\\examples\\data\\vulnerability_test_file_input.csv",
+ "vulnerability_fn": "default_vulnerability_curves",
+ "vulnerability_identifiers_and_linking_fn": "default_hazus_iwr_linking",
"functions_mean": "default",
"functions_max": ["AGR1"],
"unit": "feet",
"step_size": 0.01,
},
- "setup_exposure_vector": {
+ "setup_exposure_buildings": {
"asset_locations": "NSI",
"occupancy_type": "NSI",
"max_potential_damage": "NSI",
- "ground_floor_height": 1,
+ "ground_floor_height": "NSI",
"unit": "ft",
},
"setup_hazard": {
@@ -68,7 +68,6 @@
"nodata": -99999, # value that is assigned as nodata (optional)
"var": None, # hazard variable name in NetCDF input files (optional)
"chunks": "auto", # chunk sizes along each dimension used to load the hazard file into a dask array (default is 'auto') (optional)
- "name_catalog": "flood_maps",
"risk_output": False,
},
},
@@ -85,18 +84,18 @@
"output_vector_name": "spatial.gpkg",
},
"setup_vulnerability": {
- "vulnerability_fn": "hazus_vulnerability_curves",
- "vulnerability_identifiers_and_linking_fn": ".\\examples\\data\\vulnerability_test_file_input.csv",
+ "vulnerability_fn": "default_vulnerability_curves",
+ "vulnerability_identifiers_and_linking_fn": "default_hazus_iwr_linking",
"functions_mean": "default",
"functions_max": ["AGR1"],
"unit": "feet",
"step_size": 0.01,
},
- "setup_exposure_vector": {
+ "setup_exposure_buildings": {
"asset_locations": "NSI",
"occupancy_type": "NSI",
"max_potential_damage": "NSI",
- "ground_floor_height": 1,
+ "ground_floor_height": "NSI",
"unit": "ft",
},
"setup_hazard": {
@@ -123,7 +122,6 @@
"nodata": -99999, # value that is assigned as nodata (optional)
"var": None, # hazard variable name in NetCDF input files (optional)
"chunks": "auto", # chunk sizes along each dimension used to load the hazard file into a dask array (default is 'auto') (optional)
- "name_catalog": "flood_maps",
"risk_output": True,
},
},
@@ -148,7 +146,7 @@ def test_integration(case):
fm.write()
# Check if the exposure data exists
- assert root.joinpath("exposure", "exposure.gpkg").exists()
+ assert root.joinpath("exposure", "buildings.gpkg").exists()
assert root.joinpath("exposure", "exposure.csv").exists()
assert root.joinpath("exposure", "region.gpkg").exists()
@@ -159,4 +157,4 @@ def test_integration(case):
assert root.joinpath("hazard").exists()
# Check if the output data folder exists
- assert root.joinpath("output").exists()
\ No newline at end of file
+ assert root.joinpath("output").exists()
diff --git a/tests/test_integrations_hazard.py b/tests/test_integrations_hazard.py
index e0b18986..2d32b87b 100644
--- a/tests/test_integrations_hazard.py
+++ b/tests/test_integrations_hazard.py
@@ -1,107 +1,190 @@
from hydromt_fiat.fiat import FiatModel
-from hydromt.config import configread
from hydromt.log import setuplog
from pathlib import Path
import pytest
import shutil
-import os
+import geopandas as gpd
EXAMPLEDIR = Path(
"P:/11207949-dhs-phaseii-floodadapt/Model-builder/Delft-FIAT/local_test_database"
-)
+)
-_cases = {
- "integration": {
- "data_catalogue": EXAMPLEDIR / "fiat_catalog.yml",
- "dir": "test_hazard",
- "ini": EXAMPLEDIR / "test_hazard_unique.ini",
- },
+_region = {
+ "type": "FeatureCollection",
+ "features": [
+ {
+ "type": "Feature",
+ "properties": {},
+ "geometry": {
+ "coordinates": [
+ [
+ [ 171.090746817905512, 7.121358418747697 ],
+ [ 171.217560473873988, 7.123661889257901 ],
+ [ 171.217560473873988, 7.123661889257901 ],
+ [ 171.216525686221701, 7.063008661686558 ],
+ [ 171.092077237014792, 7.063384936479417 ],
+ [ 171.090746817905512, 7.121358418747697 ],
+ ]
+ ],
+ "type": "Polygon",
+ },
+ }
+ ],
}
-@pytest.mark.parametrize("case", list(_cases.keys()))
-def test_hazard(case):
- # Read model in examples folder.
- root = EXAMPLEDIR.joinpath(_cases[case]["dir"])
- if root.exists:
- shutil.rmtree(root)
-
- # uncomment to test event analysis from geotiff file
- configuration = {
- "setup_hazard": {
- "map_fn": ["P:/11207949-dhs-phaseii-floodadapt/Model-builder/Delft-FIAT/local_test_database/data/Hazard/Current_prob_event_set_combined_doNothing_withSeaWall_RP=1_max_flood_depth.tif"],
- "map_type": "water_depth",
- "rp": None,
- "crs": None,
- "nodata": -99999,
- "var": None,
- "chunks": "auto",
- "name_catalog": None,
- "risk_output": False,
+_region_charleston = {
+ "type": "FeatureCollection",
+ "features": [
+ {
+ "type": "Feature",
+ "properties": {},
+ "geometry": {
+ "coordinates": [
+ [
+ [ -80.028357024435763, 32.84613574478697 ],
+ [ -79.863521617196227, 32.833781113480647 ],
+ [ -79.854779619756741, 32.734963735677987 ],
+ [ -80.026105579065501, 32.739367988104483 ],
+ [ -80.028357024435763, 32.84613574478697 ],
+ ]
+ ],
+ "type": "Polygon",
+ },
}
- }
+ ],
+}
- # uncomment to test risk analysis from geotiff file
- # configuration = {
- # "setup_hazard": {
- # "map_fn": ["P:/11207949-dhs-phaseii-floodadapt/Model-builder/Delft-FIAT/local_test_database/data/Hazard/Current_prob_event_set_combined_doNothing_withSeaWall_RP=1_max_flood_depth.tif", "P:/11207949-dhs-phaseii-floodadapt/Model-builder/Delft-FIAT/local_test_database/data/Hazard/Current_prob_event_set_combined_doNothing_withSeaWall_RP=2_max_flood_depth.tif", "P:/11207949-dhs-phaseii-floodadapt/Model-builder/Delft-FIAT/local_test_database/data/Hazard/Current_prob_event_set_combined_doNothing_withSeaWall_RP=5_max_flood_depth.tif"],
- # "map_type": "water_depth",
- # "rp": None,
- # "crs": None,
- # "nodata": -99999,
- # "var": None,
- # "chunks": "auto",
- # "name_catalog": None,
- # "risk_output": True,
- # }
- # }
- # for these test data sfincs output data is required in local files
- # uncomment to test event analysis from sfincs output
- # mode = "single"
- # map_path = Path("C:/Users/fuentesm/CISNE/Deltares/FloodAdapt/tests/test_database/charleston/output/simulations/current_extreme12ft_no_measures/overland")
+_cases = {
+ "event_map": {
+ "region": _region_charleston,
+ "dir": "test_event_map",
+ "configuration": {
+ "setup_hazard": {
+ "map_fn": [
+ r"P:\11207949-dhs-phaseii-floodadapt\Model-builder\Delft-FIAT\local_test_database\test_RP_floodmaps\RP_1_maps.nc",
+ ],
+ "map_type": "water_depth",
+ "rp": None,
+ "crs": None,
+ "nodata": -99999,
+ "var": "zsmax",
+ "risk_output": False,
+ }
+ }
+ },
+ "risk_map": {
+ "region": _region_charleston,
+ "dir": "test_risk_map",
+ "configuration": {
+ "setup_hazard": {
+ "map_fn": [
+ r"P:\11207949-dhs-phaseii-floodadapt\Model-builder\Delft-FIAT\local_test_database\test_RP_floodmaps\RP_1_maps.nc",
+ r"P:\11207949-dhs-phaseii-floodadapt\Model-builder\Delft-FIAT\local_test_database\test_RP_floodmaps\RP_50_maps.nc",
+ r"P:\11207949-dhs-phaseii-floodadapt\Model-builder\Delft-FIAT\local_test_database\test_RP_floodmaps\RP_10_maps.nc",
+ r"P:\11207949-dhs-phaseii-floodadapt\Model-builder\Delft-FIAT\local_test_database\test_RP_floodmaps\RP_100_maps.nc",
+ ],
+ "map_type": "water_depth",
+ "rp": None,
+ "crs": None,
+ "nodata": -99999,
+ "var": "zsmax",
+ "risk_output": True,
+ }
+ }
+ },
- # uncomment to test risk analysis from sfincs outputs
- # mode = "risk"
- # map_path = Path("C:/Users/fuentesm/CISNE/Deltares/FloodAdapt/tests/test_database/charleston/output/simulations/current_test_set_no_measures/")
+ "event_map_geotiffs": {
+ "region": _region,
+ "dir": "test_event_map_geotiffs",
+ "configuration": {
+ "setup_hazard": {
+ "map_fn": [
+ r"P:\11207949-dhs-phaseii-floodadapt\Model-builder\Delft-FIAT\local_test_database\test_RP_floodmaps\Swell_Majuro_case_SW_slr_100_RP1_Final.tif",
+ ],
+ "map_type": "water_depth",
+ "rp": None,
+ "crs": None,
+ "nodata": None,
+ "var": None,
+ "risk_output": False,
+ }
+ }
+ },
- # map_fn = []
+ "risk_map_geotiffs": {
+ "region": _region,
+ "dir": "test_risk_map_geotiffs",
+ "configuration": {
+ "setup_hazard": {
+ "map_fn": [
+ r"P:\11207949-dhs-phaseii-floodadapt\Model-builder\Delft-FIAT\local_test_database\test_RP_floodmaps\Swell_Majuro_case_SW_slr_100_RP1_Final.tif",
+ r"P:\11207949-dhs-phaseii-floodadapt\Model-builder\Delft-FIAT\local_test_database\test_RP_floodmaps\Swell_Majuro_case_SW_slr_100_RP10_Final.tif",
+ r"P:\11207949-dhs-phaseii-floodadapt\Model-builder\Delft-FIAT\local_test_database\test_RP_floodmaps\Swell_Majuro_case_SW_slr_100_RP50_Final.tif",
+ ],
+ "map_type": "water_depth",
+ "rp": None,
+ "crs": None,
+ "nodata": None,
+ "var": None,
+ "risk_output": True,
+ }
+ }
+ },
- # if mode == "risk":
- # # check for netcdf
- # for file in os.listdir(str(map_path)):
- # if file.endswith("_maps.nc"):
- # map_fn.append(map_path.joinpath(file))
- # risk_output = True
- # var = "risk_map"
+ "event_map_geotiff2": {
+ "region": _region_charleston,
+ "dir": "test_event_map_geotiff2",
+ "configuration": {
+ "setup_hazard": {
+ "map_fn": [
+ r"P:\11207949-dhs-phaseii-floodadapt\Model-builder\Delft-FIAT\local_test_database\test_RP_floodmaps\kingTide_SLR_max_flood_depth.tif",
+ ],
+ "map_type": "water_depth",
+ "rp": None,
+ "crs": None,
+ "nodata": None,
+ "var": None,
+ "risk_output": False,
+ }
+ }
+ },
- # elif mode == "single":
- # map_fn.append(map_path.joinpath("sfincs_map.nc"))
- # risk_output = False
- # var = "zsmax"
+ "risk_map_geotiff2": {
+ "region": _region_charleston,
+ "dir": "test_risk_map_geotiff2",
+ "configuration": {
+ "setup_hazard": {
+ "map_fn": [
+ r"P:\11207949-dhs-phaseii-floodadapt\Model-builder\Delft-FIAT\local_test_database\test_RP_floodmaps\Current_prob_event_set_combined_doNothing_withSeaWall_RP=2_max_flood_depth.tif",
+ r"P:\11207949-dhs-phaseii-floodadapt\Model-builder\Delft-FIAT\local_test_database\test_RP_floodmaps\Current_prob_event_set_combined_doNothing_withSeaWall_RP=10_max_flood_depth.tif",
+ r"P:\11207949-dhs-phaseii-floodadapt\Model-builder\Delft-FIAT\local_test_database\test_RP_floodmaps\Current_prob_event_set_combined_doNothing_withSeaWall_RP=100_max_flood_depth.tif",
+ ],
+ "map_type": "water_depth",
+ "rp": None,
+ "crs": None,
+ "nodata": None,
+ "var": None,
+ "risk_output": True,
+ }
+ }
+ },
+}
- # configuration = {
- # "setup_hazard": {
- # "map_fn": map_fn, # absolute or relative (with respect to the configuration.ini) path to the hazard file
- # "map_type": "water_depth", # description of the hazard file type
- # "rp": None, # hazard return period in years, required for a risk calculation (optional)
- # "crs": None, # coordinate reference system of the hazard file (optional)
- # "nodata": -999, # value that is assigned as nodata (optional)
- # "var": var, # hazard variable name in NetCDF input files (optional)
- # "chunks": "auto", # chunk sizes along each dimension used to load the hazard file into a dask array (default is 'auto') (optional)
- # "name_catalog": None,
- # "risk_output": risk_output,
- # }
- # }
+@pytest.mark.parametrize("case", list(_cases.keys()))
+def test_hazard(case):
+ # Read model in examples folder.
+ root = EXAMPLEDIR.joinpath(_cases[case]["dir"])
+ if root.exists():
+ shutil.rmtree(root)
logger = setuplog("hydromt_fiat", log_level=10)
- data_catalog_yml = str(_cases[case]["data_catalogue"])
- fm = FiatModel(root=root, mode="w", data_libs=[data_catalog_yml], logger=logger)
- region = fm.data_catalog.get_geodataframe("region", variables=None)
- # opt = configread(_cases[case]["ini"])
- # fm.build(region={"geom": region}, opt=opt)
- fm.build(region={"geom": region}, opt=configuration)
+ fm = FiatModel(root=root, mode="w", logger=logger)
+ region = gpd.GeoDataFrame.from_features(_cases[case]["region"], crs=4326)
+
+ fm.build(region={"geom": region}, opt=_cases[case]["configuration"])
fm.write()
# Check if the hazard folder exists
diff --git a/tests/test_raise_ground_floor_height.py b/tests/test_raise_ground_floor_height.py
index 79e28428..6e784f7e 100644
--- a/tests/test_raise_ground_floor_height.py
+++ b/tests/test_raise_ground_floor_height.py
@@ -5,7 +5,9 @@
import shutil
import pandas as pd
-EXAMPLEDIR = Path("P:/11207949-dhs-phaseii-floodadapt/Model-builder/Delft-FIAT/local_test_database")
+EXAMPLEDIR = Path(
+ "P:/11207949-dhs-phaseii-floodadapt/Model-builder/Delft-FIAT/local_test_database"
+)
_cases = {
"raise_ground_floor_height_geom": {
diff --git a/tests/test_read_write.py b/tests/test_read_write.py
new file mode 100644
index 00000000..673b3863
--- /dev/null
+++ b/tests/test_read_write.py
@@ -0,0 +1,55 @@
+from hydromt_fiat.fiat import FiatModel
+from hydromt.log import setuplog
+from pathlib import Path
+import pytest
+import shutil
+
+
+EXAMPLEDIR = Path(
+ "P:/11207949-dhs-phaseii-floodadapt/Model-builder/Delft-FIAT/local_test_database"
+)
+
+_cases = {
+ "read_read_write_single_file": {
+ "dir": "test_read",
+ "new_root": EXAMPLEDIR / "test_read_write_single",
+ },
+ "read_read_write_multiple_files": {
+ "dir": "test_read_multiple",
+ "new_root": EXAMPLEDIR / "test_read_write_multiple",
+ },
+}
+
+
+@pytest.mark.parametrize("case", list(_cases.keys()))
+def test_read_write(case):
+ # Read model in examples folder.
+ root = EXAMPLEDIR.joinpath(_cases[case]["dir"])
+ logger = setuplog("hydromt_fiat", log_level=10)
+
+ fm = FiatModel(root=root, mode="r", logger=logger)
+ fm.read()
+
+ # Get the name of the spatial exposure data in the model that is read
+ exposure_files = [k for k in fm.config["exposure"]["geom"].keys() if "file" in k]
+ original_exposure_names = set(
+ [
+ fm.config["exposure"]["geom"][f].split("/")[-1].split(".")[0]
+ for f in exposure_files
+ ]
+ )
+
+ # Remove the new root folder if it already exists
+ if _cases[case]["new_root"].exists():
+ shutil.rmtree(_cases[case]["new_root"])
+
+ # Set the new root and write the model
+ fm.set_root(_cases[case]["new_root"])
+ fm.write()
+
+ # Check if the name of the spatial exposure data in the new_root is correct
+ new_exposure_names = set(
+ [f.stem for f in _cases[case]["new_root"].joinpath("exposure").glob("*.gpkg")]
+ )
+
+ assert original_exposure_names.issubset(new_exposure_names)
diff --git a/tests/test_reader.py b/tests/test_reader.py
index 30dbdb8c..f72e393f 100644
--- a/tests/test_reader.py
+++ b/tests/test_reader.py
@@ -13,20 +13,18 @@
_cases = {
"read": {
- "data_catalogue": EXAMPLEDIR / "fiat_catalog.yml",
"dir": "test_read",
},
}
@pytest.mark.parametrize("case", list(_cases.keys()))
-def test_read_fiat_config(case):
+def test_read(case):
# Read model in examples folder.
root = EXAMPLEDIR.joinpath(_cases[case]["dir"])
logger = setuplog("hydromt_fiat", log_level=10)
- data_catalog_yml = str(_cases[case]["data_catalogue"])
- fm = FiatModel(root=root, mode="r", data_libs=[data_catalog_yml], logger=logger)
+ fm = FiatModel(root=root, mode="r", logger=logger)
fm.read()
# Check if the exposure object exists
@@ -39,4 +37,4 @@ def test_read_fiat_config(case):
assert isinstance(fm.vulnerability, Vulnerability)
# Check if the vulnerability functions exist
- assert len(fm.vulnerability.functions) > 0
\ No newline at end of file
+ assert len(fm.vulnerability.functions) > 0
diff --git a/tests/test_roads.py b/tests/test_roads.py
new file mode 100644
index 00000000..8d8d48c1
--- /dev/null
+++ b/tests/test_roads.py
@@ -0,0 +1,99 @@
+from hydromt_fiat.fiat import FiatModel
+from hydromt.log import setuplog
+from pathlib import Path
+import pytest
+import shutil
+import geopandas as gpd
+import pandas as pd
+
+
+EXAMPLEDIR = Path(
+ "P:/11207949-dhs-phaseii-floodadapt/Model-builder/Delft-FIAT/local_test_database"
+)
+DATADIR = Path().absolute() / "hydromt_fiat" / "data"
+
+_region = {
+ "type": "FeatureCollection",
+ "features": [
+ {
+ "type": "Feature",
+ "properties": {},
+ "geometry": {
+ "coordinates": [
+ [
+ [-79.92169686568795, 32.768208904171374],
+ [-79.92169686568795, 32.77745096033627],
+ [-79.94881762529997, 32.77745096033627],
+ [-79.94881762529997, 32.768208904171374],
+ [-79.92169686568795, 32.768208904171374],
+ ]
+ ],
+ "type": "Polygon",
+ },
+ }
+ ],
+}
+_cases = {
+ "roads_from_OSM": {
+ "data_catalogue": DATADIR / "hydromt_fiat_catalog_USA.yml",
+ "dir": "test_roads_from_OSM",
+ "configuration": {
+ "setup_road_vulnerability": {
+ "vertical_unit": "ft",
+ "threshold_value": 0.5,
+ "min_hazard_value": 0,
+ "max_hazard_value": 15,
+ "step_hazard_value": 1,
+ },
+ "setup_exposure_roads": {
+ "roads_fn": "OSM",
+ "road_types": ["motorway", "primary", "secondary", "tertiary"],
+ "road_damage": "default_road_max_potential_damages",
+ "unit": "ft",
+ },
+ },
+ "region": _region,
+ },
+}
+
+
+@pytest.mark.parametrize("case", list(_cases.keys()))
+def test_setup_roads(case):
+ # Read model in examples folder.
+ root = EXAMPLEDIR.joinpath(_cases[case]["dir"])
+ if root.exists():
+ shutil.rmtree(root)
+ data_catalog_yml = str(_cases[case]["data_catalogue"])
+
+ logger = setuplog("hydromt_fiat", log_level=10)
+
+ fm = FiatModel(root=root, mode="w", data_libs=[data_catalog_yml], logger=logger)
+
+ region = gpd.GeoDataFrame.from_features(_cases[case]["region"], crs=4326)
+ fm.build(region={"geom": region}, opt=_cases[case]["configuration"])
+ fm.write()
+
+ # Check if the exposure data exists
+ assert root.joinpath("exposure", "roads.gpkg").exists()
+ assert root.joinpath("exposure", "exposure.csv").exists()
+ assert root.joinpath("exposure", "region.gpkg").exists()
+
+ # Read the resulting exposure data and check if the required columns exist
+ exposure = pd.read_csv(root.joinpath("exposure", "exposure.csv"))
+ required_columns = ['Secondary Object Type', 'Object Name', 'lanes', 'Object ID',
+ 'Primary Object Type', 'Damage Function: Structure',
+ 'Max Potential Damage: Structure', 'Segment Length [m]']
+ assert set(required_columns) == set(exposure.columns)
+
+ # Check if the vulnerability data exists
+ assert root.joinpath("vulnerability", "vulnerability_curves.csv").exists()
+
+ # Check if the hazard folder exists
+ assert root.joinpath("hazard").exists()
+
+ # Check if the output data folder exists
+ assert root.joinpath("output").exists()
+
+ # Check if the output gives the correct solution
+ assert fm.vulnerability.functions == {'roads': [0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
+ assert fm.vulnerability.hazard_values == [0.0, 0.49, 0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
\ No newline at end of file
diff --git a/tests/test_setup_new_composite_areas_ground_elevation.py b/tests/test_setup_new_composite_areas_ground_elevation.py
new file mode 100644
index 00000000..c7bea9c5
--- /dev/null
+++ b/tests/test_setup_new_composite_areas_ground_elevation.py
@@ -0,0 +1,99 @@
+from hydromt_fiat.fiat import FiatModel
+from hydromt.log import setuplog
+from pathlib import Path
+import pytest
+import shutil
+import pandas as pd
+
+EXAMPLEDIR = Path(
+ "P:/11207949-dhs-phaseii-floodadapt/Model-builder/Delft-FIAT/local_test_database"
+)
+
+EXAMPLEDIR = Path().absolute() / "examples" / "data" / "setup_new_composite_area"
+DATADIR = Path().absolute() / "hydromt_fiat" / "data"
+DATADIRDEM = Path(
+ "P:/11207949-dhs-phaseii-floodadapt/FloodAdapt/Test_data/Database_env_fix/static/dem"
+)
+
+_cases = {
+ "setup_new_composite_area_datum": {
+ "dir": "test_read",
+ "new_root": EXAMPLEDIR / "test_setup_new_composite_area_datum",
+ "composite_areas": DATADIR / "new_composite_areas" / "new_development_area_test.gpkg",
+ "type": "datum",
+ "path_ref": None,
+ "attr_ref": None,
+ "ground_elevation_file": None,
+ "aggregation_area_fn": None,
+ "attribute_names": None,
+ "label_names": None,
+ },
+ "setup_new_composite_area_geom": {
+ "dir": "test_read",
+ "new_root": EXAMPLEDIR / "test_setup_new_composite_area_geom",
+ "composite_areas": DATADIR / "new_composite_areas" / "new_development_area_test.gpkg",
+ "type": "geom",
+ "path_ref": DATADIR / "new_composite_areas" / "reference_groundHeight_test.shp",
+ "attr_ref": "bfe",
+ "ground_elevation_file": None,
+ "aggregation_area_fn": None,
+ "attribute_names": None,
+ "label_names": None,
+ },
+ "setup_new_composite_area_elevation": {
+ "dir": "test_read",
+ "new_root": EXAMPLEDIR / "test_setup_new_composite_area_elevation",
+ "composite_areas": DATADIR / "new_composite_areas" / "new_development_area_test.gpkg",
+ "type": "datum",
+ "path_ref": None,
+ "attr_ref": None,
+ "ground_elevation_file": DATADIRDEM
+ / "charleston_14m.tif",
+ "aggregation_area_fn": EXAMPLEDIR.joinpath("test_read", "exposure", "aggregation_areas", "block_groups.gpkg"),
+ "attribute_names": "GEOID_short",
+ "label_names": "Aggregation Label: Census Block",
+ },
+}
+
+@pytest.mark.parametrize("case", list(_cases.keys()))
+def test_setup_new_composite_areas_ground_elevation(case):
+ # Read model in examples folder.
+ root = EXAMPLEDIR.joinpath(_cases[case]["dir"])
+ logger = setuplog("hydromt_fiat", log_level=10)
+
+ fm = FiatModel(root=root, mode="r", logger=logger)
+ fm.read()
+
+ # store original exposure
+ exposure_original = fm.exposure.exposure_db
+
+ fm.exposure.setup_new_composite_areas(
+ percent_growth=10,
+ geom_file=str(_cases[case]["composite_areas"]),
+ ground_floor_height=2,
+ damage_types=["Structure", "Content"],
+ vulnerability=fm.vulnerability,
+ elevation_reference=_cases[case]["type"],
+ path_ref=_cases[case]["path_ref"],
+ attr_ref=_cases[case]["attr_ref"],
+ ground_elevation=_cases[case]["ground_elevation_file"],
+ aggregation_area_fn=_cases[case]["aggregation_area_fn"],
+ attribute_names=_cases[case]["attribute_names"],
+ label_names=_cases[case]["label_names"],
+ )
+
+ if _cases[case]["new_root"].exists():
+ shutil.rmtree(_cases[case]["new_root"])
+
+ fm.set_root(_cases[case]["new_root"])
+ fm.write()
+
+ # read modified exposure
+ exposure_modified = pd.read_csv(
+ _cases[case]["new_root"] / "exposure" / "exposure.csv"
+ )
+
+ exposure_modified = fm.exposure.exposure_db
+
+ # check if the new development area was added
+ assert len(exposure_modified) > len(exposure_original), 'The composite areas were not added'
diff --git a/tests/test_update_ground_floor_height.py b/tests/test_update_ground_floor_height.py
new file mode 100644
index 00000000..6a5077eb
--- /dev/null
+++ b/tests/test_update_ground_floor_height.py
@@ -0,0 +1,73 @@
+from hydromt_fiat.fiat import FiatModel
+from hydromt.log import setuplog
+from pathlib import Path
+import pytest
+import shutil
+import copy
+
+
+EXAMPLEDIR = Path().absolute() / "examples" / "data" / "update_ground_floor_height"
+DATADIR = Path().absolute() / "hydromt_fiat" / "data"
+
+_cases = {
+ "update_ground_floor_height_with_points": {
+ "dir": "fiat_model",
+ "new_root": EXAMPLEDIR / "test_update_ground_floor_height_points",
+ "data_catalog": DATADIR / "hydromt_fiat_catalog_USA.yml",
+ "ground_floor_height_file": EXAMPLEDIR
+ / "fake_elevation_certificates.gpkg",
+ "attribute": "FFE",
+ "method": "nearest",
+ "max_dist": 50,
+ },
+ "update_ground_floor_height_with_polygons": {
+ "dir": "fiat_model",
+ "new_root": EXAMPLEDIR / "test_update_ground_floor_height_polygons",
+ "data_catalog": DATADIR / "hydromt_fiat_catalog_USA.yml",
+ "ground_floor_height_file": EXAMPLEDIR
+ / "fake_update_ground_floor_height.gpkg",
+ "attribute": "groundfloorheight",
+ "method": "intersection",
+ "max_dist": None,
+ },
+}
+
+
+@pytest.mark.parametrize("case", list(_cases.keys()))
+def test_update_ground_floor_height(case):
+ # Read model in examples folder.
+ root = EXAMPLEDIR.joinpath(_cases[case]["dir"])
+ logger = setuplog("hydromt_fiat", log_level=10)
+
+ fm = FiatModel(
+ root=root, mode="r", data_libs=[_cases[case]["data_catalog"]], logger=logger
+ )
+ fm.read()
+
+ original_exposure = copy.deepcopy(fm.exposure.exposure_db)
+ unique_gfh_original = original_exposure["Ground Floor Height"].unique()
+
+ fm.exposure.setup_ground_floor_height(
+ _cases[case]["ground_floor_height_file"],
+ _cases[case]["attribute"],
+ _cases[case]["method"],
+ _cases[case]["max_dist"]
+ )
+
+ # Remove the new root folder if it already exists
+ if _cases[case]["new_root"].exists():
+ shutil.rmtree(_cases[case]["new_root"])
+
+ # Set the new root and write the model
+ fm.set_root(_cases[case]["new_root"])
+ fm.write()
+
+ # Check if the new ground floor height is different from the original one
+ unique_gfh_new = fm.exposure.exposure_db["Ground Floor Height"].unique()
+ assert any(unique_gfh_original != unique_gfh_new), "The Ground Floor Height is the same"
+
+ # # Check if the Ground Floor Heigh attribute is set correctly
+ # ground_floor_height = gpd.read_file(_cases[case]["ground_floor_height_file"])
+ # nearest_utm = utm_crs(ground_floor_height.total_bounds)
+ # ground_floor_height = ground_floor_height.to_crs(nearest_utm)
+ # ground_floor_height.geometry = ground_floor_height.geometry.buffer(_cases[case]["max_dist"])
diff --git a/tests/test_update_max_potential_damage.py b/tests/test_update_max_potential_damage.py
index e9302bcc..695aecdb 100644
--- a/tests/test_update_max_potential_damage.py
+++ b/tests/test_update_max_potential_damage.py
@@ -62,10 +62,12 @@ def test_update_max_potential_damage(case):
# check if the max potential damage is updated
updated_max_pot_damage.reset_index(inplace=True, drop=True)
- updated_max_pot_damage["Object Name"] = updated_max_pot_damage["Object Name"].astype(int)
+ updated_max_pot_damage["Object Name"] = updated_max_pot_damage[
+ "Object Name"
+ ].astype(int)
pd.testing.assert_frame_equal(
updated_max_pot_damage,
exposure_modified,
check_dtype=False,
check_column_type=False,
- )
\ No newline at end of file
+ )
diff --git a/tests/test_update_max_potential_damage_user_data.py b/tests/test_update_max_potential_damage_user_data.py
new file mode 100644
index 00000000..2824b21d
--- /dev/null
+++ b/tests/test_update_max_potential_damage_user_data.py
@@ -0,0 +1,73 @@
+from hydromt_fiat.fiat import FiatModel
+from hydromt.log import setuplog
+from pathlib import Path
+import pytest
+import shutil
+import copy
+import numpy as np
+
+
+EXAMPLEDIR = Path().absolute() / "examples" / "data" / "update_max_potential_damage"
+DATADIR = Path().absolute() / "hydromt_fiat" / "data"
+
+_cases = {
+ "update_max_potential_damage_with_points": {
+ "dir": "fiat_model",
+ "new_root": EXAMPLEDIR / "test_update_max_potential_damage_points",
+ "data_catalog": DATADIR / "hydromt_fiat_catalog_USA.yml",
+ "max_potential_damage_file": EXAMPLEDIR
+ / "fake_max_potential_damage_points.gpkg",
+ "damage_types": "content",
+ "attribute": "maxpotential_content",
+ "method": "nearest",
+ "max_dist": 50,
+ },
+ "update_max_potential_damage_with_polygons": {
+ "dir": "fiat_model",
+ "new_root": EXAMPLEDIR / "test_update_max_potential_damage_polygons",
+ "data_catalog": DATADIR / "hydromt_fiat_catalog_USA.yml",
+ "max_potential_damage_file": EXAMPLEDIR
+ / "fake_max_potential_damage_polygons.gpkg",
+ "damage_types": "structure",
+ "attribute": "maxpotential_structure",
+ "method": "intersection",
+ "max_dist": None,
+ },
+}
+
+@pytest.mark.parametrize("case", list(_cases.keys()))
+def test_update_max_potential_damage(case):
+ # Read model in examples folder.
+ root = EXAMPLEDIR.joinpath(_cases[case]["dir"])
+ logger = setuplog("hydromt_fiat", log_level=10)
+
+ fm = FiatModel(
+ root=root, mode="r", data_libs=[_cases[case]["data_catalog"]], logger=logger
+ )
+ fm.read()
+
+ target_column = f"Max Potential Damage: {_cases[case]['damage_types'].capitalize()}"
+
+ original_exposure = copy.deepcopy(fm.exposure.exposure_db)
+ unique_mp_original = original_exposure[target_column].unique()
+
+ fm.exposure.setup_max_potential_damage(
+ max_potential_damage=_cases[case]["max_potential_damage_file"],
+ damage_types=_cases[case]["damage_types"],
+ attr_name=_cases[case]["attribute"],
+ method=_cases[case]["method"],
+ max_dist=_cases[case]["max_dist"]
+ )
+
+ # Remove the new root folder if it already exists
+ if _cases[case]["new_root"].exists():
+ shutil.rmtree(_cases[case]["new_root"])
+
+ # Set the new root and write the model
+ fm.set_root(_cases[case]["new_root"])
+ fm.write()
+
+ # Check if the new maximun potential damage is different from the original one
+ unique_mp_new = fm.exposure.exposure_db[target_column].unique()
+ assert not np.array_equal(unique_mp_original, unique_mp_new), "The maximun potential damage is the same"
+
diff --git a/tests/test_vulnerability.py b/tests/test_vulnerability.py
index ab8e2165..45f4ce3d 100644
--- a/tests/test_vulnerability.py
+++ b/tests/test_vulnerability.py
@@ -16,8 +16,8 @@
"dir": "test_hazus_vulnerability_curves",
"configuration": {
"setup_vulnerability": {
- "vulnerability_fn": "hazus_vulnerability_curves",
- "vulnerability_identifiers_and_linking_fn": ".\\examples\\data\\vulnerability_test_file_input.csv",
+ "vulnerability_fn": "default_vulnerability_curves",
+ "vulnerability_identifiers_and_linking_fn": "default_hazus_iwr_linking",
"functions_mean": "default",
"functions_max": ["AGR1"],
"unit": "feet",
@@ -43,14 +43,19 @@ def test_hazus_vulnerability_curves(case):
fm.write()
# Check if the vulnerability data exists
- vulnerability_curves_path = root.joinpath("vulnerability", "vulnerability_curves.csv")
+ vulnerability_curves_path = root.joinpath(
+ "vulnerability", "vulnerability_curves.csv"
+ )
assert vulnerability_curves_path.exists()
- # Check if the vulnerability data is correct by comparing it to data that is known
+ # Check if the vulnerability data is correct by comparing it to data that is known
# to be correct
vulnerability_curves = pd.read_csv(vulnerability_curves_path)
correct_vulnerability_curves = pd.read_csv(
- Path(__file__).parent / "data" / "correct" / "test_hazus_vulnerability_curves.csv"
+ Path(__file__).parent
+ / "data"
+ / "correct"
+ / "test_hazus_vulnerability_curves.csv"
)
- assert vulnerability_curves.equals(correct_vulnerability_curves)
\ No newline at end of file
+ assert vulnerability_curves.equals(correct_vulnerability_curves)
diff --git a/tests/test_vulnerability_exposure.py b/tests/test_vulnerability_exposure.py
index 5ca7b2da..e8ec88d4 100644
--- a/tests/test_vulnerability_exposure.py
+++ b/tests/test_vulnerability_exposure.py
@@ -44,19 +44,19 @@
"output_vector_name": "spatial.gpkg",
},
"setup_vulnerability": {
- "vulnerability_fn": "hazus_vulnerability_curves",
- "vulnerability_identifiers_and_linking_fn": ".\\examples\\data\\vulnerability_test_file_input.csv",
+ "vulnerability_fn": "default_vulnerability_curves",
+ "vulnerability_identifiers_and_linking_fn": "default_hazus_iwr_linking",
"functions_mean": "default",
"functions_max": ["AGR1"],
"unit": "feet",
"step_size": 0.1,
},
- "setup_exposure_vector": {
+ "setup_exposure_buildings": {
"asset_locations": "NSI",
"occupancy_type": "NSI",
"max_potential_damage": "NSI",
- "damage_types": ["structure"],
- "ground_floor_height": 1,
+ "damage_types": ["structure", "content"],
+ "ground_floor_height": "NSI",
"unit": "ft",
},
},
@@ -73,31 +73,25 @@
"output_vector_name": "spatial.gpkg",
},
"setup_vulnerability": {
- "vulnerability_fn": "hazus_vulnerability_curves",
- "vulnerability_identifiers_and_linking_fn": ".\\examples\\data\\vulnerability_test_file_input.csv",
+ "vulnerability_fn": "default_vulnerability_curves",
+ "vulnerability_identifiers_and_linking_fn": "default_hazus_iwr_linking",
"functions_mean": "default",
"functions_max": ["AGR1"],
"unit": "feet",
"step_size": 0.1,
},
- "setup_exposure_vector": {
+ "setup_exposure_buildings": {
"asset_locations": "NSI",
"occupancy_type": "NSI",
"max_potential_damage": "NSI",
- "damage_types": ["structure"],
- "ground_floor_height": 1,
+ "damage_types": ["structure", "content"],
+ "ground_floor_height": "NSI",
"unit": "ft",
},
"setup_social_vulnerability_index": {
"census_key": "495a349ce22bdb1294b378fb199e4f27e57471a9",
"codebook_fn": "social_vulnerability",
- "state_abbreviation": "SC",
- "blockgroup_fn": str(
- DATADIR
- / "social_vulnerability"
- / "test_blockgroup_shp"
- / "tl_2022_45_bg.shp"
- ),
+ "year_data": 2021,
},
},
"region": _region,
@@ -122,7 +116,7 @@ def test_vulnerability_exposure_NSI(case):
fm.write()
# Check if the exposure data exists
- assert root.joinpath("exposure", "exposure.gpkg").exists()
+ assert root.joinpath("exposure", "buildings.gpkg").exists()
assert root.joinpath("exposure", "exposure.csv").exists()
assert root.joinpath("exposure", "region.gpkg").exists()
diff --git a/tests/test_vulnerability_exposure_add_to_data_catalog.py b/tests/test_vulnerability_exposure_add_to_data_catalog.py
index a975ded9..74aaa84d 100644
--- a/tests/test_vulnerability_exposure_add_to_data_catalog.py
+++ b/tests/test_vulnerability_exposure_add_to_data_catalog.py
@@ -4,6 +4,7 @@
import pytest
import shutil
import geopandas as gpd
+import pandas as pd
EXAMPLEDIR = Path(
@@ -45,24 +46,52 @@
"output_vector_name": "spatial.gpkg",
},
"setup_vulnerability": {
- "vulnerability_fn": "hazus_vulnerability_curves",
- "vulnerability_identifiers_and_linking_fn": ".\\examples\\data\\vulnerability_test_file_input_miami_landuse.csv",
+ "vulnerability_fn": "default_vulnerability_curves",
+ "vulnerability_identifiers_and_linking_fn": "tests/data/vulnerability_test_file_input_miami_landuse.csv",
"unit": "feet",
},
- "setup_exposure_vector": {
+ "setup_exposure_buildings": {
"asset_locations": "OSM",
"occupancy_type": "landuse_miami_dade",
"occupancy_type_field": "DESCR",
"max_potential_damage": "hazus_max_potential_damages",
"ground_floor_height": 1,
"unit": "ft",
+ "damage_types": ["structure", "content"],
+ },
+ },
+ "region": _region,
+ },
+ "vulnerability_and_exposure_only_local_data": {
+ "data_catalogue": DATADIR / "hydromt_fiat_catalog_USA.yml",
+ "dir": "vulnerability_and_exposure_only_local_data",
+ "configuration": {
+ "setup_global_settings": {"crs": "epsg:4326"},
+ "setup_output": {
+ "output_dir": "output",
+ "output_csv_name": "output.csv",
+ "output_vector_name": "spatial.gpkg",
+ },
+ "setup_vulnerability": {
+ "vulnerability_fn": "default_vulnerability_curves",
+ "vulnerability_identifiers_and_linking_fn": "tests/data/vulnerability_test_file_input_miami_landuse.csv",
+ "unit": "feet",
+ },
+ "setup_exposure_buildings": {
+ "asset_locations": "osm_building_footprints",
+ "occupancy_type": "landuse_miami_dade",
+ "occupancy_type_field": "DESCR",
+ "max_potential_damage": "hazus_max_potential_damages",
+ "ground_floor_height": 1,
+ "unit": "ft",
+ "damage_types": ["structure", "content"],
},
},
"region": _region,
},
}
-@pytest.mark.skip(reason="Th functionalities to test with this test are not yet finalized")
+
@pytest.mark.parametrize("case", list(_cases.keys()))
def test_vulnerability_and_exposure_add_to_data_catalog(case):
# Read model in examples folder.
@@ -75,7 +104,7 @@ def test_vulnerability_and_exposure_add_to_data_catalog(case):
fm = FiatModel(root=root, mode="w", data_libs=[data_catalog_yml], logger=logger)
- # Now we will add data from the user to the data catalog.
+ # Add land use data from the user to the data catalog.
to_add = {
"landuse_miami_dade": {
"path": str(EXAMPLEDIR / "miami_data" / "landuse_small.gpkg"),
@@ -87,20 +116,44 @@ def test_vulnerability_and_exposure_add_to_data_catalog(case):
}
fm.data_catalog.from_dict(to_add)
+ # Add building footprint data from the user to the data catalog.
+ to_add = {
+ "osm_building_footprints": {
+ "path": str(
+ EXAMPLEDIR
+ / "data"
+ / "local_exposure_data_miami"
+ / "osm_building_footprints.geojson"
+ ),
+ "data_type": "GeoDataFrame",
+ "driver": "vector",
+ "crs": 4326,
+ "category": "exposure",
+ }
+ }
+ fm.data_catalog.from_dict(to_add)
+
region = gpd.GeoDataFrame.from_features(_cases[case]["region"], crs=4326)
fm.build(region={"geom": region}, opt=_cases[case]["configuration"])
fm.write()
# Check if the exposure data exists
- assert root.joinpath("exposure", "exposure.gpkg").exists()
+ assert root.joinpath("exposure", "buildings.gpkg").exists()
assert root.joinpath("exposure", "exposure.csv").exists()
assert root.joinpath("exposure", "region.gpkg").exists()
+ # Check if the exposure data contains the columns that should be added
+ exposure = pd.read_csv(root.joinpath("exposure", "exposure.csv"))
+ required_columns = [
+ "Object ID",
+ "Primary Object Type",
+ "Ground Floor Height",
+ "Extraction Method",
+ "Damage Function: Structure",
+ "Damage Function: Content",
+ ]
+ assert all([col in exposure.columns for col in required_columns])
+ assert "geometry" not in exposure.columns
+
# Check if the vulnerability data exists
assert root.joinpath("vulnerability", "vulnerability_curves.csv").exists()
-
- # Check if the hazard folder exists
- assert root.joinpath("hazard").exists()
-
- # Check if the output data folder exists
- assert root.joinpath("output").exists()
\ No newline at end of file
diff --git a/tests/test_vulnerability_exposure_global_default.py b/tests/test_vulnerability_exposure_global_default.py
index 64bb41ea..832d4942 100644
--- a/tests/test_vulnerability_exposure_global_default.py
+++ b/tests/test_vulnerability_exposure_global_default.py
@@ -55,11 +55,12 @@
"vulnerability_identifiers_and_linking_fn": "jrc_vulnerability_curves_linking",
"unit": "m",
},
- "setup_exposure_vector": {
+ "setup_exposure_buildings": {
"asset_locations": "OSM",
"occupancy_type": "OSM",
"max_potential_damage": "jrc_damage_values",
"ground_floor_height": 0,
+ "damage_types": ["structure", "content"],
"unit": "m",
"country": "United Kingdom",
},
@@ -85,7 +86,7 @@ def test_vulnerability_and_exposure_global_default(case):
fm.write()
# Check if the exposure data exists
- assert root.joinpath("exposure", "exposure.gpkg").exists()
+ assert root.joinpath("exposure", "buildings.gpkg").exists()
assert root.joinpath("exposure", "exposure.csv").exists()
assert root.joinpath("exposure", "region.gpkg").exists()
@@ -96,4 +97,4 @@ def test_vulnerability_and_exposure_global_default(case):
assert root.joinpath("hazard").exists()
# Check if the output data folder exists
- assert root.joinpath("output").exists()
\ No newline at end of file
+ assert root.joinpath("output").exists()
diff --git a/tests/test_vulnerability_expousure_with_user_dem_data.py b/tests/test_vulnerability_expousure_with_user_dem_data.py
new file mode 100644
index 00000000..ec3436f9
--- /dev/null
+++ b/tests/test_vulnerability_expousure_with_user_dem_data.py
@@ -0,0 +1,114 @@
+from hydromt_fiat.fiat import FiatModel
+from hydromt.log import setuplog
+from pathlib import Path
+import pytest
+import shutil
+import geopandas as gpd
+import rasterio
+import numpy as np
+
+EXAMPLEDIR = Path(
+ "P:/11207949-dhs-phaseii-floodadapt/Model-builder/Delft-FIAT/local_test_database"
+)
+EXAMPLEDIR = Path().absolute() / "examples" / "data" / "setup_exposure_buildings_with_dem_data"
+DATADIR = Path().absolute() / "hydromt_fiat" / "data"
+DATADIRDEM = Path(
+ "P:/11207949-dhs-phaseii-floodadapt/FloodAdapt/Test_data/Database_env_fix/static/dem"
+)
+
+_region = {
+ "type": "FeatureCollection",
+ "features": [
+ {
+ "type": "Feature",
+ "properties": {},
+ "geometry": {
+ "coordinates": [
+ [
+ [-79.92169686568795, 32.768208904171374],
+ [-79.92169686568795, 32.77745096033627],
+ [-79.94881762529997, 32.77745096033627],
+ [-79.94881762529997, 32.768208904171374],
+ [-79.92169686568795, 32.768208904171374],
+ ]
+ ],
+ "type": "Polygon",
+ },
+ }
+ ],
+}
+_cases = {
+ "vulnerability_and_exposure_NSI_dem_data": {
+ "data_catalogue": DATADIR / "hydromt_fiat_catalog_USA.yml",
+ "dir": "test_vulnerability_and_exposure_NSI_with_dem_data",
+ "configuration": {
+ "setup_global_settings": {"crs": "epsg:4326"},
+ "setup_output": {
+ "output_dir": "output",
+ "output_csv_name": "output.csv",
+ "output_vector_name": "spatial.gpkg",
+ },
+ "setup_vulnerability": {
+ "vulnerability_fn": "default_vulnerability_curves",
+ "vulnerability_identifiers_and_linking_fn": "default_hazus_iwr_linking",
+ "functions_mean": "default",
+ "functions_max": ["AGR1"],
+ "unit": "feet",
+ "step_size": 0.1,
+ },
+ "setup_exposure_buildings": {
+ "asset_locations": "NSI",
+ "occupancy_type": "NSI",
+ "max_potential_damage": "NSI",
+ "damage_types": ["structure", "content"],
+ "ground_floor_height": "NSI",
+ "ground_elevation_file": DATADIRDEM
+ / "charleston_14m.tif",
+ "unit": "ft",
+ },
+ },
+ "region": _region,
+ },
+
+}
+
+
+@pytest.mark.parametrize("case", list(_cases.keys()))
+def test_vulnerability_exposure_NSI_dem_data(case):
+ # Read model in examples folder.
+ root = EXAMPLEDIR.joinpath(_cases[case]["dir"])
+ if root.exists():
+ shutil.rmtree(root)
+
+ logger = setuplog("hydromt_fiat", log_level=10)
+ data_catalog_yml = str(_cases[case]["data_catalogue"])
+
+ fm = FiatModel(
+ root=root,
+ mode="w",
+ data_libs=[data_catalog_yml],
+ logger=logger
+ )
+
+ region = gpd.GeoDataFrame.from_features(_cases[case]["region"], crs=4326)
+ fm.build(region={"geom": region}, opt=_cases[case]["configuration"])
+ fm.write()
+
+ # Check if the updated values are not null
+ not_null_values = fm.exposure.exposure_db["Ground Elevation"].notnull()
+ assert not_null_values.all(), "Warning: There are null values in 'Ground Elevation' column."
+
+
+ # Check if the calculated values are within the maximun and minimun value of the original daster file. This function could be used to calculate
+ # Ground Elevation itself in case the ground_elevation_from_dem() function in gis.py is not accurate enough
+ raster_file_path = _cases[case]["configuration"]["setup_exposure_buildings"]["ground_elevation_file"]
+ with rasterio.open(raster_file_path) as src:
+ nodata_value = src.nodatavals[0]
+ raster_data = src.read(1)
+ valid_values = raster_data[raster_data != nodata_value]
+ #mean_value_excluding_nodata = np.nanmean(valid_values)
+ max_value_excluding_nodata = np.nanmax(valid_values)
+ min_value_excluding_nodata = np.nanmin(valid_values)
+ valid_values = (fm.exposure.exposure_db["Ground Elevation"] >= min_value_excluding_nodata) & (fm.exposure.exposure_db["Ground Elevation"] <= max_value_excluding_nodata)
+ assert valid_values.all(), "The Ground Elevation is beyond the maximun and minimun values of the provided DEM"
+
diff --git a/tests/test_vulnerability_from_csvs.py b/tests/test_vulnerability_from_csvs.py
index 977c1d13..792698b1 100644
--- a/tests/test_vulnerability_from_csvs.py
+++ b/tests/test_vulnerability_from_csvs.py
@@ -38,10 +38,12 @@ def test_csvs(case):
fm.write()
# Check if the vulnerability data exists
- vulnerability_curves_path = root.joinpath("vulnerability", "vulnerability_curves.csv")
+ vulnerability_curves_path = root.joinpath(
+ "vulnerability", "vulnerability_curves.csv"
+ )
assert vulnerability_curves_path.exists()
- # Check if the vulnerability data is correct by comparing it to data that is known
+ # Check if the vulnerability data is correct by comparing it to data that is known
# to be correct
vulnerability_curves = pd.read_csv(vulnerability_curves_path)
correct_vulnerability_curves = pd.read_csv(