Stamen is a hip online design outfit based in San Francisco. They’re well known for working on data visualizations for Trulia and Digg, and their own high profile websites like Oakland Crime Map and Cabspotting. Last week we were lucky enough to get founder Eric Rodenbeck to come in to give us a talk, which I will now liveblog eight days after the fact…
Welcome Eric! Eric is the founder of Stamen. Stamen is a 7 person studio that mostly does mapping and visualization work, mostly on live data. They’re based in San Francisco.
They try not to take stuff that’s ‘complete’ and draw conclusions from it, rather they prefer to take flowing data and build structures for it to flow into.
Eric talks about EJ Marey and his work visualizing movement. It’s pretty cool stuff. Single photographic plate showing multiple frames of movement.
This was the 1840s. He invented the first device to take the pulse non-invasively. Studied the flights of insects and birds, then moved onto air and water, smoke studies. Some of the time he was obviously just having fun – Eric shows a motion study photograph of two fencers.
There’s a correlation with the kind of thing Stamen does. A more modern twist on this kind of thing. Eric shows some images from a Koren artist showing planes taking off from an airport – as each plane takes off it has been overlaid on the same photograph. Another example is somebody who has played a console racing game hundreds of times, recording each path through the level then overlaid them all on top of each other, as if there are hundreds of cars racing against each other.
Moving on to what Stamen does. After Tufte nobody has an excuse to produce a bad chart. But there’s lots of data out there, flowing, live, bumping up against the limitations of existing viz tools.
Eric shows us the work Stamen has been doing at Digg Labs and on Cab Spotting (I would blog descriptions of these but it’s far faster and cooler to click through the links and play around with the visualizations themselves). For Cab Spotting they’ve got access to GPS data from 400 yellow cabs in San Francisco, updated once a minute.
They approached the data in lots of different ways. First, most obvious thing: cabs are animated dots on a black screen, when a cab picks somebody up that dot flashes, yellow dots are passengered cabs, gray dots are empty cabs.
Second: leave traces according to the speed that cabs travelling at: red roads are 35+ mph on average, white roads are slower on average.
They could do a ‘where’s the nearest cab now’ type site but somehow seems less interesting than this sort of pulsing, flowing data, showing cities as living organisms.
GPS stops working sometimes, like when cabs travel on the lower deck of the Golden Gate bridge. The data gets messy. Stamen like this, it’ll average out with enough data, don’t try and fuzzy it out.
Eric brings up the Trulia Hindsight housing maps. Trulia wanted to establish themselves as an expert in the real estate data field and show off the data they’d collected. They’ve got the location, price and build dates of properties across the US.
Stamen’s first pass: they displayed dots on a map as houses were built. You can see the city growing like a mossy organism. Then they tried incorporating price data, but this didn’t really work not least because the value of money over time changes.
Those were both paid projects. They do spare time stuff too. Michal Migurski sat down over the xmas holidays one year and created the first iteration of Oakland Crimespotting, which plots crime reports from the Oakland police department on a map with different colours and icons for different types of crime. Oakland city council already had a site that did this, but the interface sucked.
The Stamen version allows you to see where quality of life crimes (bums on street corners, drinking), violence, prostitution and theft correlate. Which streets have which kind of crime. Eric mentioned how as a side effect you could pick out patterns of how the Oakland PD operate – from the dates of the arrests you can see how they move up a particular highway arresting prostitutes, for example.
The site was up for a fortnight before the the city found out and shut off access. Because it made Oakland look bad, perhaps. After discussions and a local paper getting involved the city opened up the data again.
Eric suggests that Crimespotting works well because you can see everything at once and then filter unwanted stuff out, rather than having to select exactly what you need a priori.
(for some more Crimespotting goodness check out Tom Carden’s blog)
Finally he brings up the London property visualizations (scroll down to see the actual applet), made in conjunction with MySociety. These mashup data on house prices and travel times in London. Sadly they’re fixed in that you can only get travel times to either the olymic site at Stratford or London Transport HQ (from where they scrape the data).
Again, rather than specifying exactly what you want – to live within 45 minutes of x in a house costing no more than y – you have all of the relevant data plotted on a map of London straight away. By moving two sliders (one for maximum house price and one for travel time) you can visualize multiple scenarios quickly and easily.
Eric wraps up. He shows a venn diagram of “useful” and “cool”. He reckons Stamen works in the overlap, between analysis and spectacle.
Thanks Eric! We move on to some Q&A.