My colleague James Hay has been experimenting with Processing, working with some public data made available on the UK government’s statistics site:
http://neighbourhood.statistics.gov.uk/dissemination/Download1.do
The end result has been captured below, showing cylinders whose height relates to the population and whose position on the map relate to the GPS coordinates for the post-codes in the data.
On a similar theme I’ve been reading Programming Collective Intelligence, O’reilly publishing. This book contains all manner of interesting discussions and examples (in Python) related to the analysis and use of large data sets. Some uses include a “PageRank” algorithm, a simplification of what Google might use, and a “recommendations engine” as you might find when browsing Amazon.
The book is fairly dry, but if you are a geek that likes to deal with practical algorithms I’d definitely recommend it. There are a couple of definitions given to the term Collective Intelligence, both relating to the power of crowds but another definition relates specifically to the superhuman intelligence exhibited by large groups of people when solving puzzles, the “hive mind” if you like, and this usually manifests itself in a viral manner. This effect was superbly illustrated by the I Love Bees and the Iris alternate reality games (the latter of which was a product of our AKQA San Francisco office). A paper was recently published by Jane McGonigal, PhD, going into incredible detail on the production and execution of I Love Bees and again I thoroughly recommend the read (PDF). AKQA also created an interactive walkthrough of the Iris campaign, featuring videos, commentary and a timeline of events.