Jupyter Snippet CB2nd 06_gis

14.6. Manipulating geospatial data with Cartopy

import io
import requests
import zipfile
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.collections as col
from matplotlib.colors import Normalize
import cartopy.crs as ccrs
from cartopy.feature import ShapelyFeature
import cartopy.io.shapereader as shpreader
%matplotlib inline

url = ('https://github.com/ipython-books/'
       'cookbook-2nd-data/blob/master/'
       'africa.zip?raw=true')
r = io.BytesIO(requests.get(url).content)
zipfile.ZipFile(r).extractall('data')
countries = shpreader.Reader(
    'data/ne_10m_admin_0_countries.shp')

africa = [c for c in countries.records()
          if c.attributes['CONTINENT'] == 'Africa']

crs = ccrs.PlateCarree()
extent = [-23.03, 55.20, -37.72, 40.58]

def draw_africa(ax):
    ax.set_extent(extent)
    ax.coastlines()

fig, ax = plt.subplots(
    1, 1, figsize=(6, 8),
    subplot_kw=dict(projection=crs))
draw_africa(ax)

png

def choropleth(ax, attr, cmap_name):
    # We need to normalize the values before we can
    # use the colormap.
    values = [c.attributes[attr] for c in africa]
    norm = Normalize(
        vmin=min(values), vmax=max(values))
    cmap = plt.cm.get_cmap(cmap_name)
    for c in africa:
        v = c.attributes[attr]
        sp = ShapelyFeature(c.geometry, crs,
                            edgecolor='k',
                            facecolor=cmap(norm(v)))
        ax.add_feature(sp)

fig, (ax1, ax2) = plt.subplots(
    1, 2, figsize=(10, 16),
    subplot_kw=dict(projection=crs))
draw_africa(ax1)
choropleth(ax1, 'POP_EST', 'Reds')
ax1.set_title('Population')

draw_africa(ax2)
choropleth(ax2, 'GDP_MD_EST', 'Blues')
ax2.set_title('GDP')

png