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import osmnx as ox
%matplotlib inline
ox.config(log_file=True, log_console=True, use_cache=True)
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# from some place name, create a GeoDataFrame containing the geometry of the place
city = ox.gdf_from_place('Walnut Creek, California, USA')
city
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# save the retrieved data as a shapefile
ox.save_gdf_shapefile(city)
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# project the geometry to the appropriate UTM zone (calculated automatically) then plot it
city = ox.project_gdf(city)
fig, ax = ox.plot_shape(city)
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# define a list of place names
place_names = ['Berkeley, California, USA',
'Oakland, California, USA',
'Piedmont, California, USA',
'Emeryville, California, USA',
'Alameda, Alameda County, CA, USA']
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# create a GeoDataFrame with rows for each place in the list
east_bay = ox.gdf_from_places(place_names, gdf_name='east_bay_cities')
east_bay
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# project the geometry to the appropriate UTM zone then plot it
east_bay = ox.project_gdf(east_bay)
fig, ax = ox.plot_shape(east_bay)
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# save the retrieved and projected data as a shapefile
ox.save_gdf_shapefile(east_bay)
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# pass in buffer_dist in meters
city_buffered = ox.gdf_from_place('Walnut Creek, California, USA', buffer_dist=250)
fig, ax = ox.plot_shape(city_buffered)
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# you can buffer multiple places in a single query
east_bay_buffered = ox.gdf_from_places(place_names, gdf_name='east_bay_cities', buffer_dist=250)
fig, ax = ox.plot_shape(east_bay_buffered, alpha=0.7)
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gdf = ox.gdf_from_place('Manhattan, New York, New York, USA')
gdf = ox.project_gdf(gdf)
fig, ax = ox.plot_shape(gdf)
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gdf = ox.gdf_from_place('Cook County, Illinois, United States')
gdf = ox.project_gdf(gdf)
fig, ax = ox.plot_shape(gdf)
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gdf = ox.gdf_from_place('Iowa')
gdf = ox.project_gdf(gdf)
fig, ax = ox.plot_shape(gdf)
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gdf = ox.gdf_from_places(['United Kingdom', 'Ireland'])
gdf = ox.project_gdf(gdf)
fig, ax = ox.plot_shape(gdf)
Be specific and explicit, and sanity check the results. The function logs a warning if you get a point returned instead of a polygon. In the first example below, OSM resolves 'Melbourne, Victoria, Australia' to a single point at the center of the city. In the second example below, OSM correctly resolves 'City of Melbourne, Victoria, Australia' to the entire city and returns its polygon geometry.
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melbourne = ox.gdf_from_place('Melbourne, Victoria, Australia')
melbourne = ox.project_gdf(melbourne)
type(melbourne['geometry'].iloc[0])
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melbourne = ox.gdf_from_place('City of Melbourne, Victoria, Australia')
melbourne = ox.project_gdf(melbourne)
fig, ax = ox.plot_shape(melbourne)
OSM resolves 'Mexico' to Mexico City and returns a single point at the center of the city. Instead we have a couple options:
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mexico = ox.gdf_from_place('Mexico')
mexico = ox.project_gdf(mexico)
type(mexico['geometry'].iloc[0])
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# instead of a string, you can pass a dict containing a structured query
mexico = ox.gdf_from_place({'country':'Mexico'})
mexico = ox.project_gdf(mexico)
fig, ax = ox.plot_shape(mexico)
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# you can pass multiple queries with mixed types (dicts and strings)
mx_gt_tx = ox.gdf_from_places(queries=[{'country':'Mexico'}, 'Guatemala', {'state':'Texas'}])
mx_gt_tx = ox.project_gdf(mx_gt_tx)
fig, ax = ox.plot_shape(mx_gt_tx)
By default, we only request 1 result from OSM. But, we can pass an optional which_result parameter to query OSM for n results and then process/return the nth. If you query 'France', OSM returns the country with all its overseas territories as result #1 and European France alone as result #2. Querying for 'France' returns just the first result (and thus all of France's overseas territories), but passing which_result=2 instead retrieves the top 2 results from OSM and processes/returns the 2nd one (which is European France). You could have also done this to retrieve Mexico the country instead of Mexico City above.
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france = ox.gdf_from_place('France')
fig, ax = ox.plot_shape(france)
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france = ox.gdf_from_place('France', which_result=2)
france = ox.project_gdf(france)
fig, ax = ox.plot_shape(france)
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