![]() To acquire street network GIS data, one must typically track down Tiger/Line roads from the US census bureau, or individual data sets from other countries or cities. You can do this with cities, states, countries or any other geographic entities: Or you can pass multiple places into a single query to save a single shapefile or geopackage from their geometries. Place2 = ox.geocode_to_gdf('Cook County, Illinois') Place1 = ox.geocode_to_gdf('Manhattan, New York City, New York, USA') You can just as easily get other place types, such as neighborhoods, boroughs, counties, states, or nations – any place geometry in OpenStreetMap: With OSMnx, you can download place shapes from OpenStreetMap (as geopandas GeoDataFrames) in one line of Python code – and project them to UTM (zone calculated automatically) and visualize in just one more line of code:Ĭity = ox.geocode_to_gdf('Berkeley, California') But what about for bulk or automated acquisition and analysis? There must be an easier way than clicking through numerous web pages to download shapefiles one at a time. To acquire administrative boundary GIS data, one must typically track down shapefiles online and download them. Get administrative place boundaries and shapefiles ![]()
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