

In [1]:

    
%matplotlib inline

Plotting with Geoplot and GeoPandas

Geoplot <https://residentmario.github.io/geoplot/index.html> is a Python library providing a selection of easy-to-use geospatial visualizations. It is built on top of the lower-level CartoPy <http://scitools.org.uk/cartopy/>, covered in a separate section of this tutorial, and is designed to work with GeoPandas input.

This example is a brief tour of the geoplot API. For more details on the library refer to its documentation <https://residentmario.github.io/geoplot/index.html>_.

First we'll load in the data using GeoPandas.



In [2]:

    
import geopandas

path = geopandas.datasets.get_path('naturalearth_lowres')
df = geopandas.read_file(path)
# Add a column we'll use later
df['gdp_pp'] = df['gdp_md_est'] / df['pop_est']

boroughs = geopandas.read_file(geopandas.datasets.get_path('nybb')).to_crs(epsg='4326')
injurious_collisions = geopandas.read_file(
    "https://github.com/ResidentMario/geoplot-data/raw/master/nyc-injurious-collisions.geojson")

Plotting with Geoplot

We start out by replicating the basic GeoPandas world plot using Geoplot.



In [3]:

    
import geoplot

geoplot.polyplot(df, figsize=(8, 4))









    Out[3]:





<matplotlib.axes._subplots.AxesSubplot at 0x128c29e10>

Geoplot can re-project data into any of the map projections provided by CartoPy (see the list here <http://scitools.org.uk/cartopy/docs/latest/crs/projections.html>_).

This kills the kernel... import geoplot.crs as gcrs ax = geoplot.polyplot(df, projection=gcrs.Orthographic(), figsize=(8, 4)) ax.set_global() ax.outline_patch.set_visible(True)

polyplot is trivial and can only plot the geometries you pass to it. If you want to use color as a visual variable, specify a choropleth. Here we sort GDP per person by country into five buckets by color.



In [5]:

    
geoplot.choropleth(df, hue='gdp_pp', cmap='Greens', figsize=(8, 4))









    



/usr/local/lib/python3.7/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.
  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval






    Out[5]:





<matplotlib.axes._subplots.AxesSubplot at 0x128f6e9e8>

If you want to use size as a visual variable, you want a cartogram. Here are population estimates for countries in Africa.



In [6]:

    
geoplot.cartogram(df[df['continent'] == 'Africa'],
                  scale='pop_est', limits=(0.2, 1), figsize=(7, 8))









    Out[6]:





<matplotlib.axes._subplots.AxesSubplot at 0x12b2d90f0>

If we have data in the shape of points in space, we may generate a three-dimensional heatmap on it using kdeplot. This example also demonstrates how easy it is to stack plots on top of one another.



In [7]:

    
ax = geoplot.kdeplot(injurious_collisions.sample(1000),
                     shade=True, shade_lowest=False,
                     clip=boroughs.geometry)
geoplot.polyplot(boroughs, ax=ax)









    Out[7]:





<matplotlib.axes._subplots.AxesSubplot at 0x12b399e80>

Alternatively, we may partition the space into neighborhoods automatically, using Voronoi tessellation.

Again, these are just some of the plots you can make with Geoplot. There are several other possibilities not covered in this brief introduction. For more examples, refer to the Gallery <https://residentmario.github.io/geoplot/gallery.html>_ in the Geoplot documentation.