Mapping Hacks

Map Numerical Data the Easy Way #83

Chapter 7, Names and Places

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HACK

The following Python code harvests the population data by downloading the

page and scraping the HTML. The output is, interestingly, another Python

script, which contains the population growth values and can be imported

into yet another Python script to generate the imagery. A more advanced

version of this hack might save the data in a database somewhere for later

use, but…this is a hack, after all:

import urllib

res = [ ]

html = urllib.urlopen('http://www.cia.gov/cia/publications/factbook/fields/

2002.html').read( )

for tag in html.split('class="CountryLink">')[1:]:

country, tag = tag.split('</a',1)

growth = tag.split('class="Normal">',1)[1].split('%')[0].strip( )

if growth[0]= ='N': growth = None

else: growth = float(growth)

res.append( [country,growth] )

print "countryList = %s" % `res`

Note the use of the backticks in the last line, which causes Python to pro-

duce the representation of the associated list in Python code. We save the

Python code generated by this script as countryList.py:

$ python getCountryList.py > countryList.py

Tying It All Together

Now, fire up your favorite image editor, load a world map with countries on

it, and give country

i in the produced list RGB color (i, 255 - i, 238). Yes, it

might take some time, and each country needs to be done in the exact order

it’s listed in your data set, but the task