Simple Maps in IPython

This notebook demonstrates the basics of mapping data in IPython. All you need is a simple dataset, containing coordinate values.



In [1]:

    
%pylab inline
from pylab import *

pylab.rcParams['figure.figsize'] = (8.0, 6.4)









    



Populating the interactive namespace from numpy and matplotlib



In [2]:

    
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np

And now let's test if the Basemap package is loaded and the graphics displayed correctly.



In [3]:

    
map = Basemap(projection='ortho', lat_0=50, lon_0=-100,
              resolution='l', area_thresh=1000.0)
 
map.drawcoastlines()
 
plt.show()

Now to the cool part!



In [4]:

    
import csv

# Open the cities population data file.
filename = 'city_longlat.csv'

# Create empty lists for the latitudes, longitudes and population.
lats, lons, population = [], [], []

# Read through the entire file, skip the first line,
#  and pull out the data we need.
with open(filename) as f:
    # Create a csv reader object.
    reader = csv.reader(f)
    
    # Ignore the header row.
    next(reader)
    
    # Store the latitudes, longitudes and populations in the appropriate lists.
    for row in reader:
        lats.append(float(row[1]))
        lons.append(float(row[2]))
        population.append(float(row[3]))
 
# --- Build Map ---
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np

def get_marker_color(population):
    if population < 2000000:
        return ('ro')
    elif population < 7000000:
        return ('yo')
    else:
        return ('go')
 
map = Basemap(projection='merc', resolution = 'h', area_thresh = 1000.0,
              lat_0=0, lon_0=-130,
             llcrnrlon=-18.968978, llcrnrlat=33.679432,
    urcrnrlon=41.968945, urcrnrlat=58.940191)
map.drawcoastlines()
map.drawcountries()
map.bluemarble()
map.drawmapboundary()
map.drawmeridians(np.arange(0, 360, 30))
map.drawparallels(np.arange(-90, 90, 30))

for lons, lats, population in zip(lons, lats, population):
    x,y = map(lats, lons)
    marker_string = get_marker_color(population)
    map.plot(x, y, marker_string, markersize=population/150000)

title_string = "Most Populous Cities in Europe"

figsize(18, 12)
plt.title(title_string)
plt.show()

The green circle represents the cities with population above 7 million inhabitants, the yellow between 2 and 7 million, and the red represents all others.