Loading JSON in a GeoDataFrame

Question

I find it difficult to load the following JSON containing GIS data ( https://data.cityofnewyork.us/resource/5rqd-h5ci.json ) into a GeoDataFrame.

The following code does not work when I try to set the geometry.

import requests import geopandas as gpd data = requests.get("https://data.cityofnewyork.us/resource/5rqd-h5ci.json") gdf = gpd.GeoDataFrame(data.json()) gdf = gdf.set_geometry('the_geom') gdf.head() 

Answer 1

It is not possible to set the geometry because the geopandas.GeoDataFrame constructor geopandas.GeoDataFrame not created to handle JSON objects as python data structures. Therefore, he complains that the argument is not a valid geometry object. You have to parse it into something that geopandas.GeoDataFrame can understand, for example shapely.geometry.shape . Here's what ran without errors on my side, Python 3.5.4:

 #!/usr/bin/env python3 import requests import geopandas as gpd from shapely.geometry import shape r = requests.get("https://data.cityofnewyork.us/resource/5rqd-h5ci.json") r.raise_for_status() data = r.json() for d in data: d['the_geom'] = shape(d['the_geom']) gdf = gpd.GeoDataFrame(data).set_geometry('the_geom') gdf.head() 

Disclaimer: I know absolutely nothing about Geo. I did not even know these libraries, and such data existed until I installed geopandas to solve this generosity and read a bit of interactive documentation.

Answer 2

For people who use web mapping libraries ...

If GeoJSON is enclosed in FeatureCollection , as is often the case when exporting to a GeoJSON string using web mapping libraries (in my case, Leaflet), then all you have to do is pass the list from features to from_features() like this:

 import geopandas as gpd study_area = json.loads(""" {"type": "FeatureCollection", "features": [{"type": "Feature", "properties": {}, "geometry": {"type": "Polygon", "coordinates": [[[36.394272, -18.626726], [36.394272, -18.558391], [36.489716, -18.558391], [36.489716, -18.626726], [36.394272, -18.626726]]]}}]} """) gdf = gpd.GeoDataFrame.from_features(study_area["features"]) print(gdf.head()) 

Output:

  geometry 0 POLYGON ((36.394272 -18.626726, 36.394272 -18.... 

Easy peasy.

Answer 3

A more idiomatic way that uses the usual functions GeoDataFrame.from_features inherited from pandas , and its own GeoDataFrame.from_features :

 gdf = gpd.GeoDataFrame(data.json()) # features column does not need to be stored, this is just for illustration gdf['features'] = gdf['the_geom'].apply(lambda x: {'geometry': x, 'properties': {}}) gdf2 = gpd.GeoDataFrame.from_features(gdf['features']) gdf = gdf.set_geometry(gdf2.geometry) gdf.head()