When he lived in Chicago, during college and for several years afterward, Raymon Sutedjo-The was often conscious of stories about the inequality of the city's transit system. People who lived on the North Side seemed to have an easier time getting around, while residents in remote lower-income neighborhoods on the South and West sides found themselves far from jobs with long and plodding bus commutes to get to them.
"Those questions of equity were always in the back of my mind," says Sutedjo-The, now a master's student in the University of California at Berkeley's School of Information. He had these questions in mind when presented with a set of transit data from the cities of San Francisco, Zurich and Geneva this spring as part of the Urban Data Challenge. Designers and developers were given one week's worth of data from the three cities, covering bus schedules, actual arrival times and passenger capacity for each line and stop, with the challenge to visualize it all in some new and meaningful way.
Sutedjo-The's project, along with collaborator Sandra Lee, took second prize for posing some complicated questions beyond the quality of bus service itself (we recently wrote about another project, the transit Frustration Index, that shared second place).
Transit data is regularly the subject of some of our favorite visualizations, but this one is a bit different. "Can we explore equity issues using data as well? What would that look like?" Sutedjo-The asks. "Sometimes when we think about equity issues, we depend on qualitative data, personal stories and narratives. That’s powerful. But I wanted to start from the other side: Can we do that with numbers, with quantitative data?"
With their Transit Quality + Equity web application, Sutedjo-The and Lee mapped the frequency of service on every bus route in San Francisco from the week of Oct. 1-7 last year and accounted for the average delay at each stop between the scheduled and actual arrival times. That map – effectively reflecting the quality of transit service – is then shaded according to the poverty levels by census tract across the city (comparable poverty data wasn't available for the two Swiss cities).
The resulting picture shows that some high-poverty parts of town aren't well served by public transit, despite the fact that these neighborhoods may be most in need of it.
This pattern doesn't hold across the city, with poor communities universally having sub-par transit service (or wealthier communities getting the best of it). The full map is mixed. But this is the type of analysis that should be welcome in any city as a first start to broader conversations about the shape of urban mobility.
"Using visualization is great because you can sum up this crazy amount of data in something that’s manageable and more digestible," Sutedjo-The says. "But also you can’t really look at it as a final product."