(The site is in German…I’ll trust any of you German readers to let me know if the site actually says that Visualizing Data is the dumbest book he’s ever read.)

Also helpful to note that he used Python for preprocessing the data. He doesn’t bother implementing a map projection, as done in the book, but the Python code is a useful example of using another language when appropriate, and how the syntax differs from Processing:

# Convert opengeodb data for zipdecode
fd = open('PLZ.tab')
out = []
minlat = minlon = 180
maxlat = maxlon = 0

for line in fd:
    line = line.strip()
    if not line or line.startswith('#'):
        continue
    parts = line.split('\t')
    dummy, plz, lat, lon, name = parts
    out.append([plz, lat, lon, name])
    minlat = min([float(lat), minlat])
    minlon = min([float(lon), minlon])
    maxlat = max([float(lat), maxlat])
    maxlon = max([float(lon), maxlon])

print "# %d,%f,%f,%f,%f" % (len(out), minlat, maxlat, minlon, maxlon)
for data in out:
    plz, lat, lon, name = data
    print '\t'.join([plz, str(float(lat)), str(float(lon)), name])

In the book, I used Processing for most of the examples (with a little bit of Perl) for sake of simplicity. (The book is already introducing a lot of new material, why hurt people and introduce multiple languages while I’m at it?) However that’s one place where the book diverges from my own process a bit, since I tend to use a lot of Perl when dealing with large volumes of text data. Python is also a good choice (or Ruby if that’s your thing), but I’m tainted since I learned Perl first, while a wee intern at Sun.

Switzerland makes an interesting example because it has far fewer postal codes than the U.S., though the dots are quite elegant on their own. With fewer data points, I’d be inclined to 1) change the size of the individual points to make them larger without making them overwhelming, 2) or work with the colors to make the contrast more striking, since changing the point size is likely to be too much), and 3) get the text into mixed case (in this example, Gossau SG instead of GOSSAU SG). Something as minor as avoiding ALL CAPS helps get us away from the representation looking too much like COMPUTERS and DATABASES, and instead into something meant for regular humans. Finally, 4) with the smaller (and far more regular) data set, it’s not clear if the zoom even helps—could even be better off without it.

Thanks to Dominic for passing this along; it’s great to see!