The Geek in me like this: Social Running Geekiness

It’s one thing to look at your own personal-fitness data and identify trends and tendencies. But what about crunching the numbers of 1,000 New Yorkers over a nearly four-month period? That’s the kind of project that requires some serious know-how.

Graphic designer Nicholas Felton enlisted 14 of his students at Manhattan’s School of Visual Arts to analyze the metadata aggregated by 1,000 Nike+ runs conducted from Sept. 7 to Dec. 21 of last year. The result is an incredibly detailed representation of New Yorkers’ running habits, where the most popular routes are, what time of day Nike+ runners are more likely to be outside, and more.

The above graphic (done by Cooper Smith) shows where the most popular running paths are in Central Park. The red lines indicate the highest trafficked areas, and as Smith notes on his blog, the lighter green and blue entrails extending from the east side of the park show that more people tend to enter the park from the Upper East Side. The same lines don’t show up nearly as often along the Upper West Side entrance points.

Felton’s team did more than just static graphical overlays. The video below (also done by Smith) puts the Nike+ into motion, illustrating where people are running during what time of day. (The actual date of the run is irrelevant in this analysis.)

Teammate Erin Moore opted for a more traditional day-by-day analysis of New Yorkers’ running habits.

In all, there were more than 500,000 data points to wade through, and you can see the rest of Felton’s students’ work at their SVA page. And although the visualizations end up highlighting shortcomings in the data collection, this effort and new fitness-tracking features being developed by the likes of Boston-based startup RunKeeper prove that the future of personal data tracking has never been more rife with potential.

 

—– even more info

check out his site here 

where you can find his London Nike+ stuff … was apparently in Wired UK

London pretty

Author: richdirector

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