pythonmatplotlibheatmapgeopandas

Plot heatmap (kdeplot) with geopandas

I have the following data stored in a geopandas.DataFrame object. geometry are polygons and x are the values I want to use as a heat scale.

       id                                           geometry   x
9   01001  POLYGON ((-102.10641 22.06035, -102.10368 22.0...   33
19  01002  POLYGON ((-102.05189 22.29144, -102.05121 22.2...    2
29  01003  POLYGON ((-102.68569 22.09963, -102.69087 22.0...    0
39  01004  POLYGON ((-102.28787 22.41649, -102.28753 22.4...    0
49  01005  POLYGON ((-102.33568 22.05067, -102.33348 22.0...   22

I can use the following code to plot a map and color each polygon according to the value in column x.

t.plot(column='x', cmap='coolwarm', legend=False)
plt.axis('off')
plt.show()

bad heatmap

This is not too bad, but considering I have the polygons and values in a single object, I was wondering if theres a way to turn this plot into a heatmap using geopandas.

Solution

  • I was recommended to use geoplot.

    geoplot.kdeplot expects a geopandas.DataFrame object with one row per Point. That is, something along the lines of:

            PointID                     geometry
0     204403876  POINT (-101.66700 21.11670)
1     204462769  POINT (-101.66700 21.11670)
2     144407530  POINT (-101.66700 21.11670)
3     118631118  POINT (-101.66700 21.11670)
4     118646035  POINT (-101.66700 21.11670)

    And then plot these points over the map, which is passed as a separate object.

    To show this in code, suppose the polygons are stored in df_map and the points are stored in df_points.

    # Import geoplot
import geoplot
import geoplot.crs as gcrs

# Plot heatmap
ax = geoplot.kdeplot(df_points, projection=gcrs.AlbersEqualArea())

# Add polygons
geoplot.polyplot(df_map, ax=ax)

    Which should yield something along the lines of this.

    nice heatmap

    Sadly, I cannot post my results because projection=gcrs.AlbersEqualArea() crashes my session, but I hope this helps someone in the future.