Plotting with Geoplot and GeoPandas

Geoplot is a Python library providing a selection of easy-to-use geospatial visualizations. It is built on top of the lower-level 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.

First we’ll load in the data using GeoPandas.

import geopandas
import geoplot

world = geopandas.read_file(
    geopandas.datasets.get_path('naturalearth_lowres')
)
boroughs = geopandas.read_file(
    geoplot.datasets.get_path('nyc_boroughs')
)
collisions = geopandas.read_file(
    geoplot.datasets.get_path('nyc_injurious_collisions')
)

Plotting with Geoplot

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

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

Geoplot can re-project data into any of the map projections provided by CartoPy (see the list here).

# use the Orthographic map projection (e.g. a world globe)
ax = geoplot.polyplot(
    world, projection=geoplot.crs.Orthographic(), figsize=(8, 4)
)
ax.outline_patch.set_visible(True)

Out:

/home/docs/checkouts/readthedocs.org/user_builds/geopandas/conda/v0.6.0/lib/python3.7/site-packages/geoplot/geoplot.py:620: UserWarning: Plot extent lies outside of the Orthographic projection's viewport. Defaulting to global extent.
  'Plot extent lies outside of the Orthographic projection\'s '

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.

geoplot.choropleth(
    world, hue=world['gdp_md_est'] / world['pop_est'],
    cmap='Greens', figsize=(8, 4)
)

Out:

/home/docs/checkouts/readthedocs.org/user_builds/geopandas/conda/v0.6.0/lib/python3.7/site-packages/geoplot/geoplot.py:1573: FutureWarning:     You are passing non-geometry data to the GeoSeries constructor. Currently,
    it falls back to returning a pandas Series. But in the future, we will start
    to raise a TypeError instead.
  return gpd.GeoSeries(var)

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

africa = world.query('continent == "Africa"')
ax = geoplot.cartogram(
    africa, scale='pop_est', limits=(0.2, 1),
    edgecolor='None', figsize=(7, 8)
)
geoplot.polyplot(africa, edgecolor='gray', ax=ax)

If we have data in the shape of points in space, we may generate a three-dimensional heatmap on it using kdeplot.

ax = geoplot.kdeplot(
    collisions, clip=boroughs.geometry,
    shade=True, cmap='Reds',
    projection=geoplot.crs.AlbersEqualArea())
geoplot.polyplot(boroughs, ax=ax, zorder=1)

Alternatively, we may partition the space into neighborhoods automatically, using Voronoi tessellation. This is a good way of visually verifying whether or not a certain data column is spatially correlated.

ax = geoplot.voronoi(
    collisions.head(1000), projection=geoplot.crs.AlbersEqualArea(),
    clip=boroughs.simplify(0.001),
    hue='NUMBER OF PERSONS INJURED', cmap='Reds', k=None,
    legend=True,
    edgecolor='white'
)
geoplot.polyplot(boroughs, edgecolor='black', zorder=1, ax=ax)

These are just some of the plots you can make with Geoplot. There are many other possibilities not covered in this brief introduction. For more examples, refer to the Gallery in the Geoplot documentation.