Archive for February, 2011

Transportation visualizations have a huge advantage over their drier, more numerical cousins in that there’s an intrinsic visual already built into their nature.  After all, the more steps required for a viewer’s brain to switch between an obvious metaphor and a completely unrelated one, the less likely it is for the visualization to have any impact.  What  size is to astronomical visualizations, a good solid X-axis is to time visualizations, and color is to wavelengths or hex codes, all these things length is to distance.   When it comes to transportation visualizations, it would be a fool who passed up length in favor of something with no visual correlation to distance, like, say, color, or size of circles.

I am that fool!

The following transportation visualization was completed in two parts: The first as part of Great Urban Hack in Manhattan this last December, and the second a month later once the sleep deprivation had worn off. Perhaps that is why one is successful, and one is such a complete and embarrassing failure.

The first analyzes taxi rides throughout Manhattan during a 24 hour period of March, 2009. Each visual aspect is correlated to a particular variable for the rides occurring during the 5 minute period there represented, but no accounting is taken for the type of variable occurring. So for example, both Time and Length of a cab ride and distances traveled are minimized by their placement in the interior of the 24 hour cycle, while number of passengers is represented by circle size instead of the obvious indicator: quantity.

What this entire visualization suffers from is the ancient and evil curse of “Data Inspired Environment”. In the same way that the producer of a dramatic movie may, in the face of their terrible product, suddenly decide that they meant to produce a dark comedy all along, so do many visualizations hide behind the label of “data-inspired environment”. They are easy to spot: the purpose is an impressive visual and the data relegated to just another way to get some random variation into the visuals. With any numerical narrative buried under mis-chosen visuals, these poor excuses for visualizations would have been better seeded with some Perlin noise and good riddance.

As a thorough apology, I offer this second visualization. It attempts to solve these problems in a couple of ways – first and formost by chosing a more intuitive metaphore for a measurement of distance (a chart that requires the eye “travel” to follow the narrative) and a much more intuitive metaphore for volume: volume. It also cuts down the amount of data displayed to a single hour. Less impressive yes, but with the singular advantage of actually having some kind of meaning.

This isn’t art, it’s data visualization. Like an attractive taxi driver, pretty only counts if it also gets you where you’re going.

Analysis & Data Visualizations done by Zoe Fraade-Blanar, Kevin Webb, Aaron Glazer, John Keefe, Lev Steshenko. Data is based on a record of GPS-tagged taxi rides in March 2009 provided by the New York City Taxi and Limousine Commission.


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