Taxis and a City's Vital Signs

A New York Times article recently highlighted a mobile application called CabSense, designed to help New Yorkers find the best intersections to hail cabs in the city. The app is based on a substantial body of GPS data collected by the Taxi and Limousine Commission, crunched and analyzed by Sense Networks, an analytics firm. Cited as painting "a grand urban portrait", "a record of a city's vital signs", and perhaps an M.R.I of the city, such data allows us to observe the waking up and dying down of a city's daily cycle: rush hour, night life trends, seasonal tourist routes and everything else in between. Public transportation data is thus viewed as symptomatic of the content of our actual urban lives - it represents the economic, social, and political life of a city. (In fact, on a separate but related note, a few researchers from the State University of New York at Albany had, in 2002, examined the use of transportation data as a measure of the economy. Some may argue, for instance, that a surfeit of cab drivers would indicate the onset of a recession; more individuals who would otherwise find jobs more suited to them aren't and are driving cabs instead).


But at some point in urban planning and development, what if public transportation data were considered not simply as a purveyor of symptoms that sketch out the intricacies of urban life, but as the genome that determines the makeup of a city? The city's economic, social and political activity would therefore be expressions of this genetic code - if the genome is broken, so will its expressions and ultimately, the health of a city, forcing us to be more thoughtful about our roads, subway lines, and train routes. If our well-being is substantively correlated with commute times, using public transportation -- instead of the activities it facilitates -- as a starting point for planning cities isn't a far-fetched consideration.

In the meantime, I'd be curious to see similar data approaches applied to other cities with prolific cab cultures and without. Friends in the city state of Singapore often complain of cabbies who are nowhere to be found, hiding out until rush hour hits so that they can charge a premium. Friends in Manila talk about taking 45 minutes in a cab on a circuitous route, simply to be able to merely cross a street. While in cities like Hong Kong, cabs are hailed almost immediately from every conceivable nook and corner (an interesting uniform dataset of cab-hails perhaps). And what of heavy car-culture cities such as Los Angeles (or some may argue, even San Francisco)? What insights would the data reveal, and what decisions would local city authorities make differently as a result?


Credits: Visualization of cab data from NYTimes.com. Photo of taxis in Hong Kong from the author.

3 comments:

  1. Interesting ideas. Maybe some of these technologies can be combined with research similar to San Francisco's parking meter study. It sounds very useful to track the functioning of cities and adjust infrastructure and services as needed.

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  2. Nitpick: public transportation data would be analogous to phenotype, not genotype: as the (easily) observable result of the underlying system of interest.

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