Merge pull request #170 from Deltares/#133_Spatialbuildingfootprint

#133 spatialbuildingfootprint

.gitignore

@@ -122,5 +122,6 @@ cache
 /examples/FIAT_database
 hydromt_fiat/data/damage_functions/flooding/AllDDF_HAZUS_fractions.xlsx
 hydromt_fiat/data/damage_functions/flooding/~$AllDDF_HAZUS_fractions.xlsx
+examples/data/building_footprints/fiat_model_bfs
 examples/data/aggregation_zones_example/aggregation_test_1_output
-examples/data/aggregation_zones_example/aggregation_test_2_output
+examples/data/aggregation_zones_example/aggregation_test_2_output

examples/data/building_footprints/fiat_model/settings.toml

@@ -0,0 +1,20 @@
+[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.geom]
+csv = "./exposure/exposure.csv"
+crs = "EPSG:4326"
+file1 = "./exposure/buildings.gpkg"

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
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hydromt_fiat/fiat.py

@@ -22,6 +22,7 @@
 from hydromt_fiat.workflows.social_vulnerability_index import SocialVulnerabilityIndex
 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
 
 __all__ = ["FiatModel"]
 
@@ -566,10 +567,49 @@ def setup_aggregation_areas(
         attribute_names: Union[List[str], str],
         label_names: Union[List[str], str],
     ):
+        """_summary_
+
+        Parameters
+        ----------
+        exposure_gdf : gpd.GeoDataFrame
+            Exposure data to join the aggregation areas to as "Aggregation 
+        Label: `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.
+
+        """
+
         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
         )
+    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,
+        )
 
     # Update functions
     def update_all(self):

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

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"])