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web has a lot of geodata available. The Open GeoSpatial Consortium ([OGC](https://www.ogc.org/)) has specified standard protocols for geo-webservices, such as [Web Feature Service](http://www.opengeospatial.org/standards/wfs) (WFS) and [Web Map Service](http://www.opengeospatial.org/standards/wms) (WMS). The standard web service protocols make it easy to access data. For example, the following WFS provides data from the central bureau of statistics, containing statistics about each postal code and is provided by the Dutch Kadaster: +The web has a lot of geodata available. The Open GeoSpatial Consortium ([OGC](https://www.ogc.org/)) has specified standard protocols for geo-webservices, such as [Web Feature Service](http://www.opengeospatial.org/standards/wfs) (WFS) and [Web Map Service](http://www.opengeospatial.org/standards/wms) (WMS). The standard web service protocols make it easy to access data. For example, the following WFS provided by Rijkswaterstaat on roads and is extracted from the Dutch national database of roads in the Netherlands: + + ```{Python, eval=FALSE} from owslib.wfs import WebFeatureService # Put the WFS url in a variable -wfsUrl = 'https://service.pdok.nl/cbs/postcode6/2022/wfs/v1_0?' +wfsUrl = 'https://geo.rijkswaterstaat.nl/services/ogc/gdr/nwb_wegen/ows?service=WFS&request=getcapabilities&version=2.0.0 ' # Create a WFS object wfs = WebFeatureService(url=wfsUrl, version='2.0.0') @@ -316,7 +318,7 @@ print(list(wfs.contents)) WFS give access to data in vector format and allow a quick view of the data making geodata accessible for everyone. If you want to do a large analysis, it is better to download geodata from other available repositories and not from a WFS, as it typically has limits on the number of features that can be requested, such as 100 or 1000 features. In the WFS above, they are very generous with a limit of max 15.000 features per request. -Load some postal code geometries from the WFS service for the campus area and plot them: +Load some roads from the WFS service for the campus area and plot them: ```{Python, eval=FALSE} # Define center point and create bbox for study area @@ -324,26 +326,26 @@ x, y = (173994.1578792833, 444133.60329471016) xmin, xmax, ymin, ymax = x - 1000, x + 350, y - 1000, y + 350 # Get the features for the study area (using the wfs from the previous code block) -response = wfs.getfeature(typename=list(wfs.contents)[0], bbox=(xmin, ymin, xmax, ymax)) +response = wfs.getfeature(typename=list(wfs.contents)[-1], bbox=(xmin, ymin, xmax, ymax)) # Save them to disk -with open('data/postal_codes.gml', 'wb') as file: +with open('data/Roads.gml', 'wb') as file: file.write(response.read()) -# Read again with GeoPandas -pc_gdf = gpd.read_file('data/postal_codes.gml') +# Read in again with GeoPandas +roadsGDF = gpd.read_file('data/Roads.gml') # Inspect and plot to get a quick view -print(type(pc_gdf)) -pc_gdf.plot() +print(type(roadsGDF)) +roadsGDF.plot() plt.show() -``` +``` -Roads in Wageningen +Roads in Wageningen ```{block, type="alert alert-success"} -> **Question 4**: How many roads are there in the resulting GeoDataFrame (hint: _len()_ or _.info()_)? Do we miss roads in the extent? +> **Question 4**: How many roads are there in the resulting GeoDataFrame (hint: len() or .info())? Do we miss roads in the extent? ``` Now let's load some buildings from another WFS service (BAG) and plot them too. @@ -372,18 +374,18 @@ buildingsGDF = gpd.GeoDataFrame.from_features(data['features']) buildingsGDF.crs = 28992 # Plot roads and buildings together -pc_layer = pc_gdf.plot(color='grey') -buildingsGDF.plot(ax=pc_layer, color='red') +roadlayer = roadsGDF.plot(color='grey') +buildingsGDF.plot(ax=roadlayer, color='red') # Set the limits of the x and y axis -pc_layer.set_xlim(xmin, xmax) -pc_layer.set_ylim(ymin, ymax) +roadlayer.set_xlim(xmin, xmax) +roadlayer.set_ylim(ymin, ymax) # Save the figure to disk -plt.savefig('./output/postalcodes_buildings.png') +plt.savefig('./output/BuildingsAndRoads.png') ``` -Buildings in Wageningen +Buildings in Wageningen ```{block, type="alert alert-success"} > **Question 5**: How many buildings do you get? (hint: _len()_) Do you miss buildings? How can we extract missing buildings in our extent? @@ -411,7 +413,7 @@ Columns can be selected using the name of the column. Let us take a look at the print(buildingsGDF['bouwjaar']) ``` -For selecting rows, GeoPandas inherits the pandas methods for selecting data: label based indexing with `loc`, and integer position based indexing with `iloc`, which apply to both `GeoSeries` and `GeoDataFrame` objects. For more information on indexing/selecting, see the [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html). In addition to these, GeoPandas provides coordinate based indexing with the `cx` indexer, which slices using a bounding box. +For selecting rows, GeoPandas inherits the pandas methods for selecting data: label-based indexing with `loc`, and integer-position- based indexing with `iloc`, which apply to both `GeoSeries` and `GeoDataFrame` objects. For more information on indexing/selecting, see the [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html). In addition to these, GeoPandas provides coordinate based indexing with the `cx` indexer, which slices using a bounding box. Let us select buildings (rows) with a larger surface area than 1000 m2 with the `.loc` method. @@ -427,7 +429,7 @@ largeBuildingsGDF = buildingsGDF.loc[buildingsGDF.area > 1000, :] largeBuildingsGDF.plot() ``` -When selecting rows based on a conditional rule we can ask pandas to check whether a value from a row is equal to a value. In the example below we select the rows where the buildings are not in use. We do this by checking where the state ('status' in Dutch) is not equal (!=) to in use ('Pand in gebruik'). This returns a boolean array, which we can use to select rows. All rows where this array returns True are selected and the False rows are discarded. +When selecting rows based on a conditional rule we can ask pandas to check whether a value from a row is equal to a specific value. In the example below we select the rows where the buildings are not in use. We do this by checking where the state ('status' in Dutch) is not equal (!=) to in use ('Pand in gebruik'). This returns a boolean array, which we can use to select rows. All rows where this array returns True are selected and the False rows are discarded. ```{Python, eval=FALSE} # Inspect first @@ -447,7 +449,6 @@ newBuildingsGDF = newBuildingsGDF.to_crs(epsg=3857) ax = newBuildingsGDF.plot(figsize=(10, 10), alpha=0.5, edgecolor='k') ctx.add_basemap(ax, source=ctx.providers.OpenStreetMap.Mapnik, zoom=17) ax.set_axis_off() -plt.savefile() ``` Parcels at Wageningen University Campus @@ -456,27 +457,19 @@ plt.savefile() # Geometric manipulations -GeoDataFrames and GeoSeries have several [constructive methods](http://geopandas.org/geometric_manipulations.html) to modify the geometry: buffer, boundary, centroid, convex hull, envelope, simplify, unary union, rotate, scale, skew and translate. When modifying the geometries in the DataFrames, it is a good practice to keep track of your geometry types and your geometry data. Have a look at the geometry types of the rivers. +GeoDataFrames and GeoSeries have several [constructive methods](http://geopandas.org/geometric_manipulations.html) to modify the geometry: buffer, boundary, centroid, convex hull, envelope, simplify, unary union, rotate, scale, skew and translate. When modifying the geometries in the DataFrames, it is a good practice to keep track of your geometry types and your geometry data. Have a look at the geometry types of the roads. ```{Python, eval=FALSE} -# We will use a sample data library called geodatasets -import geodatasets - -# For this example we will look at the european rivers -riversGDF = gpd.read_file(geodatasets.get_path('eea.large_rivers')) -rivers_GDF = riversGDF.to_crs(3857) - - -print(type(riversGDF)) -print(type(riversGDF)) -print(riversGDF['geometry']) +print(type(roadsGDF)) +print(type(roadsGDF.geometry)) +print(roadsGDF['geometry']) ``` -Let's create a buffer around the rivers to represent coverage of roads, assuming roads have all a width of 1000 meters. +Let’s create a buffer around the roads to represent coverage of roads, assuming roads have all a width of 3 meters. ```{Python, eval=FALSE} # Buffer of 1.5 m on both sides -riversbufferGDF = gpd.GeoDataFrame(riversGDF, geometry=riversGDF(distance=500)) +roadsPolygonGDF = gpd.GeoDataFrame(roadsGDF, geometry=roadsGDF.buffer(distance=1.5)) # Plot roadsPolygonGDF.plot(color='blue', edgecolor='blue') @@ -485,12 +478,12 @@ roadsPolygonGDF.plot(color='blue', edgecolor='blue') print(roadsPolygonGDF.area.sum()) ``` -As we created buffers around many connected lines, we expect overlap of these buffer features. Therefore, let us merge all road buffer (polygon) features together in a `unary_union` and check again for the total coverage of buffers. +As we created buffers around many connected lines, we expect overlap of these buffer features. Therefore, let us merge all road buffer (polygon) features together and check again for the total coverage of buffers. ```{Python, eval=FALSE} -# Apply unary_union +# Apply unary_all() # This returns a geometry, which we convert to a GeoSeries to be able to apply GeoPandas methods again -roadsUnionGS = gpd.GeoSeries(roadsPolygonGDF.unary_union) +roadsUnionGS = gpd.GeoSeries(roadsPolygonGDF.union_all()) # Check the new total coverage of buffers and compute the overlap print(roadsUnionGS.area) @@ -506,12 +499,16 @@ print('There was an overlap of ' + round((roadsPolygonGDF.area.sum() - roadsUnio ``` GeoPandas can perform various [overlay operations](http://geopandas.org/set_operations.html): intersection, union, symmetrical difference and difference. We will clip the roads with convexed parcels by using intersection. As an example, let us focus on the area around the new buildings on the campus and extract the existing roads close to them. To do so we buffer the new buildings with 100 meter, merge them with a `unary_union` and create a convex hull around the merged (multipolygon) buildings. Finally we clip the roads with this single polygon. + ```{Python, eval=FALSE} -# Re-project +# Specify the coordinate system for roads +roadsPolygonGDF.crs = 28992 + +# Re-project new buildings dataset newBuildingsGDF = newBuildingsGDF.to_crs(epsg=28992) # Buffer, returns geometry, convert to GeoSeries -areaOfInterestGS = gpd.GeoSeries(newBuildingsGDF.buffer(distance=100).unary_union) +areaOfInterestGS = gpd.GeoSeries(newBuildingsGDF.buffer(distance=100).union_all()) # Convex hull, returns a GeoSeries of geometries, convert to GeoDataFrame areaOfInterestGDF = gpd.GeoDataFrame(areaOfInterestGS.convex_hull) @@ -525,8 +522,6 @@ roadsIntersectionGDF = gpd.overlay(areaOfInterestGDF, roadsPolygonGDF, how="inte # Plot the results roadlayer = roadsIntersectionGDF.plot(color='grey', edgecolor='grey') -roadlayer.set_xlim(xmin, xmax) -roadlayer.set_ylim(ymin, ymax) newBuildingsGDF.plot(ax=roadlayer, color='red') ``` @@ -535,8 +530,8 @@ newBuildingsGDF.plot(ax=roadlayer, color='red') In summary, the advantage of GeoPandas is that it allows both geometric and dataframe manipulations/selections. As a result, GeoPandas can for example select the roads within a set bounding box **and** within (and maintained by) Wageningen Municipality. ```{Python, eval=FALSE} -# Put the WFS url in a variable -wfsUrl = 'https://geodata.nationaalgeoregister.nl/nwbwegen/wfs?' +# Put the WFS url in a variable again +wfsUrl = 'https://geo.rijkswaterstaat.nl/services/ogc/gdr/nwb_wegen/ows?service=WFS&request=getcapabilities&version=2.0.0' # Create a WFS object wfs = WebFeatureService(url=wfsUrl, version='2.0.0') @@ -546,7 +541,7 @@ x, y = (173994.1578792833, 444133.60329471016) xmin, xmax, ymin, ymax = x - 3000, x + 3000, y - 3000, y + 3000 # Get the features for the study area -response = wfs.getfeature(typename=list(wfs.contents)[0], bbox=(xmin, ymin, xmax, ymax)) +response = wfs.getfeature(typename=list(wfs.contents)[-1], bbox=(xmin, ymin, xmax, ymax)) roadsGDF = gpd.read_file(response) # Select the roads within Wageningen municipality @@ -569,8 +564,8 @@ import osmnx as ox # Using a geocoder to get the extent city = ox.geocoder.geocode_to_gdf('Wageningen, Netherlands') -ox.plot.plot_footprints(ox.project_gdf(city)) - +ox.plot.plot_footprints(ox.project_gdf(city), color='lightblue', bgcolor='#FFFFFF', + alpha=0.8, edge_color='grey', edge_linewidth=2) # Get bike network and create graph wageningenRoadsGraph = ox.graph.graph_from_place('Wageningen, Netherlands', network_type='bike') @@ -596,7 +591,7 @@ source = ox.distance.nearest_nodes(wageningenRoadsGraph, 5.665779, 51.987817) target = ox.distance.nearest_nodes(wageningenRoadsGraph, 5.662409, 51.964870) # Compute shortest path -shortestroute = ox.distance.shortest_path(G=wageningenRoadsGraph, orig=source, +shortestroute = ox.routing.shortest_path(G=wageningenRoadsGraph, orig=source, dest=target, weight='length') # Plot @@ -622,6 +617,10 @@ campusMap = folium.Map([51.98527485, 5.66370505205543], zoom_start=17) # Re-project buildingsGDF = buildingsGDF.to_crs(4326) + +# Remove Timestamp objects +roadsPolygonGDF = roadsPolygonGDF.drop(columns=['wvk_begdat']) + # Folium does not support Timestamp objects, thus this column has to be dropped roadsPolygonGDF = roadsPolygonGDF.to_crs(4326) # Add the buildings @@ -634,11 +633,13 @@ folium.Choropleth(buildingsGDF, name='Building construction years', # Add the roads folium.GeoJson(roadsPolygonGDF).add_to(campusMap) +# roadsPolygonGDF.explore() + # Add layer control folium.LayerControl().add_to(campusMap) # Save (you can now open the generated .html file from the output directory) -campusMap.save('output/campusMap.html') +campusMap.save('./output/campusMap.html') ``` # More info diff --git a/index.html b/index.html index 1e431ef..c382d00 100644 --- a/index.html +++ b/index.html @@ -61,10 +61,6 @@ img { max-width: 100%; } - svg { - height: auto; - max-width: 100%; - } h1, h2, h3, h4, h5, h6 { margin-top: 1.4em; } @@ -157,11 +153,8 @@ div.columns{display: flex; gap: min(4vw, 1.5em);} div.column{flex: auto; overflow-x: auto;} div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;} - /* The extra [class] is a hack that increases specificity enough to - override a similar rule in reveal.js */ - ul.task-list[class]{list-style: none;} + ul.task-list{list-style: none;} ul.task-list li input[type="checkbox"] { - font-size: inherit; width: 0.8em; margin: 0 0.8em 0.2em -1.6em; vertical-align: middle; @@ -1402,9 +1395,9 @@

Reading from webservices

Service
(WFS) and Web Map Service (WMS). The standard web service protocols make it easy to access data. -For example, the following WFS provides data from the central bureau of -statistics, containing statistics about each postal code and is provided -by the Dutch Kadaster:

+For example, the following WFS provided by Rijkswaterstaat on roads and +is extracted from the Dutch national database of roads in the +Netherlands:

-

Roads in Wageningen

+

Roads in Wageningen

Question 4: How many roads are there in the resulting -GeoDataFrame (hint: len() or .info())? Do we miss -roads in the extent? +GeoDataFrame (hint: len() or .info())? Do we miss roads in the extent?

@@ -1506,17 +1498,17 @@

Reading from webservices

buildingsGDF.crs = 28992 # Plot roads and buildings together -pc_layer = pc_gdf.plot(color='grey') -buildingsGDF.plot(ax=pc_layer, color='red') +roadlayer = roadsGDF.plot(color='grey') +buildingsGDF.plot(ax=roadlayer, color='red') # Set the limits of the x and y axis -pc_layer.set_xlim(xmin, xmax) -pc_layer.set_ylim(ymin, ymax) +roadlayer.set_xlim(xmin, xmax) +roadlayer.set_ylim(ymin, ymax) # Save the figure to disk -plt.savefig('./output/postalcodes_buildings.png') +plt.savefig('./output/BuildingsAndRoads.png')
-

Buildings in Wageningen

+

Buildings in Wageningen

@@ -1555,10 +1547,10 @@

Selecting data

print(buildingsGDF['bouwjaar'])

For selecting rows, GeoPandas inherits the pandas methods for -selecting data: label based indexing with loc, and integer -position based indexing with iloc, which apply to both -GeoSeries and GeoDataFrame objects. For more -information on indexing/selecting, see the loc, and +integer-position- based indexing with iloc, which apply to +both GeoSeries and GeoDataFrame objects. For +more information on indexing/selecting, see the pandas documentation. In addition to these, GeoPandas provides coordinate based indexing with the cx indexer, which slices using a @@ -1580,12 +1572,12 @@

Selecting data

largeBuildingsGDF.plot()

When selecting rows based on a conditional rule we can ask pandas to -check whether a value from a row is equal to a value. In the example -below we select the rows where the buildings are not in use. We do this -by checking where the state (‘status’ in Dutch) is not equal (!=) to in -use (‘Pand in gebruik’). This returns a boolean array, which we can use -to select rows. All rows where this array returns True are selected and -the False rows are discarded.

+check whether a value from a row is equal to a specific value. In the +example below we select the rows where the buildings are not in use. We +do this by checking where the state (‘status’ in Dutch) is not equal +(!=) to in use (‘Pand in gebruik’). This returns a boolean array, which +we can use to select rows. All rows where this array returns True are +selected and the False rows are discarded.

Parcels at Wageningen University Campus

(Figures shown here and in the next section may differ slightly from @@ -1619,31 +1610,23 @@

Geometric manipulations

hull, envelope, simplify, unary union, rotate, scale, skew and translate. When modifying the geometries in the DataFrames, it is a good practice to keep track of your geometry types and your geometry data. -Have a look at the geometry types of the rivers.

+Have a look at the geometry types of the roads.

-
# We will use a sample data library called geodatasets
-import geodatasets
-
-# For this example we will look at the european rivers
-riversGDF = gpd.read_file(geodatasets.get_path('eea.large_rivers'))
-rivers_GDF = riversGDF.to_crs(3857)
-
-
-print(type(riversGDF))
-print(type(riversGDF))
-print(riversGDF['geometry'])
+
print(type(roadsGDF))
+print(type(roadsGDF.geometry))
+print(roadsGDF['geometry'])
-

Let’s create a buffer around the rivers to represent coverage of -roads, assuming roads have all a width of 1000 meters.

+

Let’s create a buffer around the roads to represent coverage of +roads, assuming roads have all a width of 3 meters.

# Buffer of 1.5 m on both sides
-riversbufferGDF = gpd.GeoDataFrame(riversGDF, geometry=riversGDF(distance=500)) 
+roadsPolygonGDF = gpd.GeoDataFrame(roadsGDF, geometry=roadsGDF.buffer(distance=1.5)) 
 
 # Plot
 roadsPolygonGDF.plot(color='blue', edgecolor='blue')
@@ -1653,15 +1636,15 @@ 

Geometric manipulations

As we created buffers around many connected lines, we expect overlap of these buffer features. Therefore, let us merge all road buffer -(polygon) features together in a unary_union and check -again for the total coverage of buffers.

+(polygon) features together and check again for the total coverage of +buffers.

-
# Apply unary_union
+
# Apply unary_all()
 # This returns a geometry, which we convert to a GeoSeries to be able to apply GeoPandas methods again
-roadsUnionGS = gpd.GeoSeries(roadsPolygonGDF.unary_union)
+roadsUnionGS = gpd.GeoSeries(roadsPolygonGDF.union_all())
 
 # Check the new total coverage of buffers and compute the overlap
 print(roadsUnionGS.area)
@@ -1686,7 +1669,7 @@ 

Geometric manipulations

-GeoPandas can perform various GeoPandas can perform various overlay operations: intersection, union, symmetrical difference and difference. We will clip the roads with convexed parcels by using intersection. As an example, @@ -1694,16 +1677,19 @@

Geometric manipulations

extract the existing roads close to them. To do so we buffer the new buildings with 100 meter, merge them with a unary_union and create a convex hull around the merged (multipolygon) buildings. Finally -we clip the roads with this single polygon. +we clip the roads with this single polygon.

-
# Re-project
+
# Specify the coordinate system for roads
+roadsPolygonGDF.crs = 28992
+
+# Re-project new buildings dataset
 newBuildingsGDF = newBuildingsGDF.to_crs(epsg=28992)
 
 # Buffer, returns geometry, convert to GeoSeries
-areaOfInterestGS = gpd.GeoSeries(newBuildingsGDF.buffer(distance=100).unary_union)
+areaOfInterestGS = gpd.GeoSeries(newBuildingsGDF.buffer(distance=100).union_all())
 
 # Convex hull, returns a GeoSeries of geometries, convert to GeoDataFrame
 areaOfInterestGDF = gpd.GeoDataFrame(areaOfInterestGS.convex_hull)
@@ -1717,8 +1703,6 @@ 

Geometric manipulations

# Plot the results roadlayer = roadsIntersectionGDF.plot(color='grey', edgecolor='grey') -roadlayer.set_xlim(xmin, xmax) -roadlayer.set_ylim(ymin, ymax) newBuildingsGDF.plot(ax=roadlayer, color='red')

New buildings at Wageningen University Campus and roads close to it

@@ -1731,8 +1715,8 @@

Geometric manipulations

-
# Put the WFS url in a variable
-wfsUrl = 'https://geodata.nationaalgeoregister.nl/nwbwegen/wfs?'
+
# Put the WFS url in a variable again
+wfsUrl = 'https://geo.rijkswaterstaat.nl/services/ogc/gdr/nwb_wegen/ows?service=WFS&request=getcapabilities&version=2.0.0'
 
 # Create a WFS object
 wfs = WebFeatureService(url=wfsUrl, version='2.0.0')
@@ -1742,7 +1726,7 @@ 

Geometric manipulations

xmin, xmax, ymin, ymax = x - 3000, x + 3000, y - 3000, y + 3000 # Get the features for the study area -response = wfs.getfeature(typename=list(wfs.contents)[0], bbox=(xmin, ymin, xmax, ymax)) +response = wfs.getfeature(typename=list(wfs.contents)[-1], bbox=(xmin, ymin, xmax, ymax)) roadsGDF = gpd.read_file(response) # Select the roads within Wageningen municipality @@ -1772,8 +1756,8 @@

Network analysis

# Using a geocoder to get the extent city = ox.geocoder.geocode_to_gdf('Wageningen, Netherlands') -ox.plot.plot_footprints(ox.project_gdf(city)) - +ox.plot.plot_footprints(ox.project_gdf(city), color='lightblue', bgcolor='#FFFFFF', + alpha=0.8, edge_color='grey', edge_linewidth=2) # Get bike network and create graph wageningenRoadsGraph = ox.graph.graph_from_place('Wageningen, Netherlands', network_type='bike') @@ -1804,7 +1788,7 @@

Network analysis

target = ox.distance.nearest_nodes(wageningenRoadsGraph, 5.662409, 51.964870) # Compute shortest path -shortestroute = ox.distance.shortest_path(G=wageningenRoadsGraph, orig=source, +shortestroute = ox.routing.shortest_path(G=wageningenRoadsGraph, orig=source, dest=target, weight='length') # Plot @@ -1838,6 +1822,10 @@

Interactive visualization

# Re-project buildingsGDF = buildingsGDF.to_crs(4326) + +# Remove Timestamp objects +roadsPolygonGDF = roadsPolygonGDF.drop(columns=['wvk_begdat']) + # Folium does not support Timestamp objects, thus this column has to be dropped roadsPolygonGDF = roadsPolygonGDF.to_crs(4326) # Add the buildings @@ -1850,11 +1838,13 @@

Interactive visualization

# Add the roads folium.GeoJson(roadsPolygonGDF).add_to(campusMap) +# roadsPolygonGDF.explore() + # Add layer control folium.LayerControl().add_to(campusMap) # Save (you can now open the generated .html file from the output directory) -campusMap.save('output/campusMap.html')
+campusMap.save('./output/campusMap.html')

More info

    @@ -1881,9 +1871,5 @@

    More info

- - - - From b267cd5880ba5a4d3f9de0febb9a0821f1e60372 Mon Sep 17 00:00:00 2001 From: Gabriele Tijunaityte Date: Mon, 9 Sep 2024 19:07:39 +0200 Subject: [PATCH 2/2] Fix the yaml file --- index.Rmd | 8 +------- index.html | 10 ++-------- 2 files changed, 3 insertions(+), 15 deletions(-) diff --git a/index.Rmd b/index.Rmd index 4e9d4b4..614e906 100644 --- a/index.Rmd +++ b/index.Rmd @@ -57,20 +57,14 @@ The conda environment we are using today contains more (and larger) packages tha ``` name: vector dependencies: - - python - - numpy - matplotlib - spyder - gdal - shapely - - pandas - - geopandas - - fiona + - geopandas=>1.0 - owslib - - folium - osmnx - contextily - - geodatasets ``` Now, create the environment with: diff --git a/index.html b/index.html index c382d00..a4db999 100644 --- a/index.html +++ b/index.html @@ -1080,20 +1080,14 @@

Setting up the Python file:

name: vector
 dependencies:
-  - python
-  - numpy
   - matplotlib
   - spyder
   - gdal
   - shapely
-  - pandas
-  - geopandas
-  - fiona
+  - geopandas=>1.0
   - owslib
-  - folium
   - osmnx
-  - contextily
-  - geodatasets
+ - contextily

Now, create the environment with: