Reading Stamen's work about cab spots with Eddie Elliott. They actually used the Cabspotting API to produce high-res long-term point maps of San Francisco with cab GPS lcoations. Part of the result description attracted my attention:

"downtown buildings are so high and close together that GPS signals can't make it down to the ground with very much accuracy, bounce around off the glass and steel, and give "bad" results. Fair enough; downtown's not so accurate. But what it means in terms of urban area chartings, where cabs tend to stay in very narrow street slots, is that you can use a visualization like this to tell immediately where the high buildings are by the degree of fuzziness in the map, and if you mapped the height of the buildings over this image, they'd probably overlap prety much one-to-one. (...) you and I live in a world where normal people can look at complex data visualizations of urban environments, notice anomalies in the display, go to the web to find information about where that place is, and then make pretty good guesses as to why the data is showing up the way it is. It needs smart people with some non-trivial technical knowhow to make these particular views on it possible, sure. But once that's done, there's a very quick path available to free information that can be used to reinforce, disprove, or generally poke at the way that the world is, and why it is that way, and it's fluid and easy and you can start asking real questions very quickly.

I think this is a new thing."

Why do I blog this? documenting new processes about the implications of urban visualizations when discussing in a bar with Fabien.