Determined to teach others how much data telecom providers not only harness but retain, German Green Party politician, Malte Spitz, sued his cell phone service provider, Deutsche Telecom, for his location data. What he received was raw data of his location, signal strength, and when he was on the phone.  Working with the German newspaper Zeit, he then published this data spanning the six months between August 2009 and February 2010 and put it up on Zeit Online into an interactive map.

Publishing your movements has obvious privacy implications and that is precisely the point that Mr. Spitz is trying to make. Over 35,000 location coordinates were taken but even the New York Times article reporting on this project revealed that these were only a subset of all the data that could be taken. Each of the Spitz datapoints were taken when he (or his email client) was checking email via the Deutsche Telecom servers. Yet, even with this low-fidelity view, it’s easy to draw lines between the points and the sample set is large enough that it’s fairly easy to work out where Mr. Spitz lives, works, and other aspects of his weekly routine. The calendar bar on the site also lets you zoom in on where he spent Christmas.

Put another way though, having access to a private dashboard where I could review where I’ve been would certainly be helpful for my expense reports and letting me selectively publish my data would certainly save having to manually check -in and create visualizations as I did with FourSquare or the raumzeitgeist report provided by  dopplr showing your past trips (above).

TED just published a fascinating talk (Birth of a Word) by MIT researcher Deb Roy who used location traces at a micro-level (below) to study how his son (who was filmed and tracked for the 24 hours a day for the first two years of his life) picked up language. While the visualization was used to assist in research, it also wowed the audience at TED as a breath-taking visualization as well.

Does location data represent a new form of art?

Did you know that dopplr assigns a unique color to each city? Neither did I. It’s not just random either – there’s an algorithm at play here. From the dopplr blog:

We wanted a deterministic RGB colour value for each city. At first, we tried mapping the latitude and longitude of a city to a point in colour space, but we found that this made neighbouring cities too similar in colour. This means that people who travel frequently between Glasgow and Edinburgh wouldn’t clearly see the difference in colour between the two. Also, since so much of the Earth’s surface is covered in water rather than cities, it leads to a sparse use of the potential colour space. In the end, we went with a much simpler approach: we take the MD5 digest of the city’s name, convert it to hex and take the first 6 characters as a CSS RGB value.

So now you know why San Francisco is pink, Helsinki is brown, and Berlin is red.