Plotting choropleth maps with Cartopy @ PyData London

Transcript

1. Plotting choropleth maps with Cartopy Alina Solovjova
2. So what is a choropleth map?
3. There are online tools that do this • CartoDB - cartodb.com • Google Fusion Tables - bit.ly/g-fusion • OpenHeatMap - openheatmap.com What about Python??
4. pip install cartopy
5. Let’s plot a map of the world import matplotlib.pyplot as plt import cartopy.crs as ccrs ax = plt.axes(projection=ccrs.PlateCarree()) ax.coastlines() ax.stock_img()
6. I’m only interested in the UK, so let’s zoom in import matplotlib.pyplot as plt import cartopy.crs as ccrs ax = plt.axes(projection=ccrs.PlateCarree()) ax.coastlines() ax.set_extent([-12, 3, 49, 60]) // x0,x1,y0,y1
7. Increase the resolution of the coastline import matplotlib.pyplot as plt import cartopy.crs as ccrs ax = plt.axes(projection=ccrs.PlateCarree()) ax.coastlines(resolution='50m') ax.set_extent([-12, 3, 49, 60])
8. Change the projection to Mercator import matplotlib.pyplot as plt import cartopy.crs as ccrs ax = plt.axes(projection=ccrs.GOOGLE_MERCATOR) ax.coastlines(resolution='50m') ax.set_extent([-12, 3, 49, 60])
10. To add boundaries, we need shapefiles • A format for storing the location, shape, and attributes of geographic features • Found online (we used ONS - bit.ly/ons-boundaries) • Stored as a set of related files (don’t just download the .shp file)
11. Let’s add regional boundaries import matplotlib.pyplot as plt import cartopy.crs as ccrs from cartopy.io.shapereader import Reader from cartopy.feature import ShapelyFeature file = '../uk_regions/uk_regions.shp' ax = plt.axes(projection=ccrs.GOOGLE_MERCATOR) ax.coastlines(resolution='50m') ax.set_extent([-12, 3, 49, 60])
12. Let’s add regional boundaries import matplotlib.pyplot as plt import cartopy.crs as ccrs from cartopy.io.shapereader import Reader from cartopy.feature import ShapelyFeature file = '../uk_regions/uk_regions.shp' ax = plt.axes(projection=ccrs.GOOGLE_MERCATOR) ax.coastlines(resolution='50m') ax.set_extent([-12, 3, 49, 60]) regions = ShapelyFeature(Reader(file).geometries(), ccrs.PlateCarree(), facecolor=‘grey') ax.add_feature(regions)
13. Merge data + region shapes {region.attributes['name_small']: region.geometry for region in Reader(file).records()}
14. Plot the data ax = plt.axes(projection=ccrs.GOOGLE_MERCATOR) ax.coastlines(resolution='50m') ax.set_extent([-12, 3, 49, 60]) norm = matplotlib.colors.Normalize(vmin=-6, vmax=4) cmap = plt.cm.gray_r for i, row in df.iterrows(): region = ShapelyFeature(df['shape'][i], ccrs.PlateCarree(), facecolor= cmap(norm(df[‘dev’][i])), ) ax.add_feature(region)
15. Plot the data ax = plt.axes(projection=ccrs.GOOGLE_MERCATOR) ax.coastlines(resolution='50m') ax.set_extent([-12, 3, 49, 60]) norm = matplotlib.colors.Normalize(vmin=-6, vmax=4) cmap = plt.cm.gray_r for i, row in df.iterrows(): region = ShapelyFeature(df['shape'][i], ccrs.PlateCarree(), facecolor= cmap(norm(df[‘dev’][i])), ) ax.add_feature(region)
16. Plot the data ax = plt.axes(projection=ccrs.GOOGLE_MERCATOR) ax.coastlines(resolution='50m') ax.set_extent([-12, 3, 49, 60]) norm = matplotlib.colors.Normalize(vmin=-6, vmax=4) cmap = plt.cm.gray_r for i, row in df.iterrows(): region = ShapelyFeature(df['shape'][i], ccrs.PlateCarree(), facecolor= cmap(norm(df[‘dev’][i])), ) ax.add_feature(region)
17. Change the colour scheme
18. Add a colorbar
19. Au revoir, France!
20. We’re looking for a data engineer! data@gocardless.com