I’ve been really excited about the power of data to help us understand our cities and make better decisions. There have been some neat visualizations and infographics recently that demonstrate the power of visualizing big data. One uses data collected from Twitter and another through GPS tracks of cabs in Manhattan. Both are visually striking and both lead to deep conversations and insights.
Eric Fischer has done a number of visualizations around cities using geotagged content. In this recent project, he traces paths through cities using geotagged tweets. Pictured above is New York, but you can see many more on his flickr feed. Also, you can read more about this project on the Fast Company Design blog and dig through the comments for some great discussion. While there is much to say (pro and con) about the utility of using this data for making actual decisions (like where a new transit line should go), it still points toward the possibilities of sensor networks. In many ways, Twitter operates as a sort of high level, rudimentary opt-in sensor network. As more people volunteer location data on social networks like Instagram, Facebook, Twitter, and Google+, cities will have a growing compendium of data that could be sliced and diced in many ways. Now, of course there are equity and access issues that need to be solved before we start backing up our decision making with Twitter feeds and the like, but in the interim, I can see this layer of data providing another view of our world that can deepen and enliven conversations, not to mention this makes for some really cool art. Also in the world of information visualization are those of taxi cabs in New York. Coming out of the Spatial Information Design Lab in the Graduate School of Architecture, Planning and Preservation at Columbia University, Juan Francisco Saldarriaga programmed an origin – destination visualization (video below) for a randomly sampled group of NYC cabs from 2010. This is part of larger research by Professor David King. Eric Jaffe, on the Atlantic Cities blog, notes “the origins and destinations have a geographical asymmetry that suggests people are only using cabs for one leg of their daily round trip.” Why is this important? Well, to King this means people are using cabs to supplement their journeys. One leg in the morning to work may be by cab, with a ride home on the subway. This points to the role that cabs may have in a multi-modal transit system. In King’s own words:
This matters because it means that individual’s travel journeys are multi-modal. If we want to have transit oriented cities we have to plan for high quality, door-to-door services that allow spontaneous one-way travel. Yet for all of the billions of dollars we have spent of fixed-route transit and the built environment we haven’t spent any time thinking about how taxis (and related services) can help us reach our goals.
3 months prior to this post was another visualization using the TAXI! analytical model coming out of the same lab. In this one, we see the interaction of cabs over a 24 hour period.
Hopefully we’ll have more to you about big data and cities and what it means for community decision making. We’ll be at APA on 2 panels (Community Engagement in Intelligent Cities | Smarter Cities through Data Literacy) about the topic, so look out for us there. Also, tell us about other exciting uses of big data in cities in the comments below or on Twitter.