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 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.
import geoplot geoplot.polyplot(df, figsize=(8, 4))
Geoplot can re-project data into any of the map projections provided by CartoPy (see the list here).
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
we sort GDP per person by country into five buckets by color.
geoplot.choropleth(df, hue='gdp_pp', cmap='Greens', figsize=(8, 4))
If you want to use size as a visual variable, you want a
are population estimates for countries in Africa.
geoplot.cartogram(df[df['continent'] == 'Africa'], scale='pop_est', limits=(0.2, 1), figsize=(7, 8))
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.
ax = geoplot.kdeplot(injurious_collisions.sample(1000), shade=True, shade_lowest=False, clip=boroughs.geometry) geoplot.polyplot(boroughs, ax=ax)
Alternatively, we may partition the space into neighborhoods automatically, using Voronoi tessellation.
ax = geoplot.voronoi( injurious_collisions.sample(1000), hue='NUMBER OF PERSONS INJURED', cmap='Reds', scheme='fisher_jenks', clip=boroughs.geometry, linewidth=0) geoplot.polyplot(boroughs, ax=ax)
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 in the Geoplot documentation.
Total running time of the script: ( 0 minutes 8.803 seconds)