Cartogram of US states by obesity rate

This example cartogram showcases regional trends for obesity in the United States. Rugged mountain states are the healthiest; the deep South, the unhealthiest.

This example inspired by the “Non-Contiguous Cartogram” example in the D3.JS example gallery.

import pandas as pd
import geopandas as gpd
import geoplot as gplt
import as gcrs
import matplotlib.pyplot as plt
import mapclassify as mc

# load the data
obesity_by_state = pd.read_csv(gplt.datasets.get_path('obesity_by_state'), sep='\t')
contiguous_usa = gpd.read_file(gplt.datasets.get_path('contiguous_usa'))
contiguous_usa['Obesity Rate'] = contiguous_usa['state'].map(
    lambda state: obesity_by_state.query("State == @state").iloc[0]['Percent']
scheme = mc.Quantiles(contiguous_usa['Obesity Rate'], k=5)

ax = gplt.cartogram(
    scale='Obesity Rate', limits=(0.75, 1),
    projection=gcrs.AlbersEqualArea(central_longitude=-98, central_latitude=39.5),
    hue='Obesity Rate', cmap='Reds', scheme=scheme,
    legend=True, legend_kwargs={'loc': 'lower right'}, legend_var='hue',
    figsize=(8, 12)
gplt.polyplot(contiguous_usa, facecolor='lightgray', edgecolor='None', ax=ax)

plt.title("Adult Obesity Rate by State, 2013")
plt.savefig("obesity.png", bbox_inches='tight', pad_inches=0.1)

Total running time of the script: ( 0 minutes 2.243 seconds)

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