color_map_by_population.py

from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import json

# create new figure, axes instances.

# we specify specific sizes for the figure to make it bigger
# DPI is "Dots per inch" -- it's used in the printing world
# The reason we specify the DPI instead of raw pixels is because
# we want the text to be consistently scaled.
my_dpi = 75 
fig = plt.figure(figsize=(3600/my_dpi, 1800/my_dpi), dpi=my_dpi)


m = Basemap()
m.drawcoastlines()

data = json.loads(open("africa_china_airports.json","r").read())

lats = []
lons = []

for airport_id in data:
    if data[airport_id]["country"] == "China":
        lats.append(data[airport_id]["latitude"])
        lons.append(data[airport_id]["longitude"])

# add scatter points for China only to the graph
m.scatter(lons, lats, latlon=True, marker="D",color="r")

lats = []
lons = []
populations = []

country_data= json.loads(open("africa_country_data.json","r").read())
for airport_id in data: 
    country = data[airport_id]["country"]
    if country != "China": 
        lats.append(data[airport_id]["latitude"]) 
        lons.append(data[airport_id]["longitude"])
        populations.append(country_data[country]["population"])

# add scatter points for Africa only to the graph
m.scatter(lons, lats, latlon=True, marker="o",c=populations, cmap='coolwarm') 
plt.savefig('map.png')