Riding Through Mountains of Data: Visualizations of Cycling
The project has two principle components: data collection using personal mobile devices and analysis / visualization.
What Iâ€™m really interested in exploring is a sense of connection between us by sharing our experiences. I ride a bike daily through NYC, and encounter many other cyclists, walkers and drivers. We pass each other in a moment, or perhaps share a lane for a bit and then continue on our separate ways. How does my 5 mile, 25 minute ride from Greenpoint to the East Village compare to someone riding from Queens? What does a ride around Prospect Park share with one in Central Park? Whatâ€™s the loudest part of the city for a cyclist? Where are the most frequently ridden routes?
What do these experiences look like? How could they be recorded? What could we learn about ourselves and our world if there was a ubiquitous network of sensors collecting data about the environment as we experience it? Would analysis and visualization reveal trends and patterns in the aggregate behavior of participants in the network?
Mobile sensors reflect a personal experience in a way that fixed sensors can only infer. Focusing on personal mobile devices as nodes in this network provides a priority on the experience of individuals rather than a specific location.
I'm an avid cyclist. I ride as a commuter, enthusiast and occasional racer.
I can get just about anywhere in the city, whenever I want and under my own power. It also provides fitness and for many people, employment. Its faster than walking and more maneuverable than driving. In dense city congestion it can be faster than mass transit. Itâ€™s a cheap way to get around. But above allâ€¦itâ€™s just FUN!
Cycling in the cityâ€”and what I really mean by that is riding among several ton moving vehicles, in all sorts of weather, often on roads not designed to accommodate bikesâ€”is by some estimation, insane. I could tell you about close calls, spin tales of getting from Midtown to the East Village in less than 10 minutes at 6pm on a weekday or talk about an epic ride past Nyack where I bonked on the return tripâ€”but thereâ€™s something really interesting about quantifying our experiences; somehow they become more tangible.
I first felt the excitement of self-quantification when using a heart rate monitor for training, and later when using a PowerTap cyclecomputer which could download ride logs to my computer. Having numbers for heart rate, speed and power provided nearly endless bragging rights (and sometimes shame) among my cycling teammates. It was a way for us to connect our individual experiences in a manner that we could understand and compare.
Iâ€™m certainly not the only cyclist on the roads. I see scores of other commuters as well as couriers, delivery riders and pedicabs â€” from folks leisurely riding around the Park to kitted racers in a pace line. However, apart from a friendly head nod, or occasional exchange of choice words I generally feel isolated as I head from here to there; in the margins of the roads (or sometimes splitting lanes), over the bridges with their scenic views of the city skyline, in the frenetic bustle of 5th Ave Midtown at rush hour and the desolation and darkness of post-midnight industrial Greenpoint and Queens.
I wondered, what do these other people experience while riding through the city? I have a clear understanding of what it feels like to me to be a cyclist in New York City. Is that experience a common one among other riders? How could I foster a sense of connection by relating through this shared experience?
There are several ways of describing an experience. We can say that weâ€™ve ridden 50 miles in a day, or show the raw data numbers. Visualizations are a manifestation of real events with which I'm hoping to make an emotional connection between the riders and the viewer.