Nike+ is a revolutionary product in personal fitness. In its five years, its motivated millions of people to get off the couch and go for a run. At the completion of their run, the user is given a handful of useful metrics (time, pace, and route) in an effort to improve their next run.

What it doesn’t offer, however, is a holistic representation of the data. What does it look like when an entire city goes running? And how can we use that data to improve the experience of runners in different cities?

With 1,000 runs of Nike+ data, I set out to do an audit of running in New York City.

Location

After countless hours cleaning the data in Google Refine, and even more hours in Processing, I arrived at one of the simplest visualizations, location.

Though this rendering offers very little information about individual runs and the data within them, I love the story that it does tell about location. The GPS data draws its own map of New York City, from the shape of the Manhattan landmass down to its individual streets.

Popularity of Routes

Next, I wanted to explore how popular these different routes were. I achieved this by creating a simple heat map of all the runs, examining how many runners shared the same route over the course of this data set.

Not surprisingly, Central Park and the trails along the edge of Manhattan emerge as the most popular, as well as the bridges between Manhattan & Brooklyn. Downtown Brooklyn, especially along the promenade, also sees a great deal of traffic. Interestingly, it appears that more runners in Central Park tend to come from the Upper East Side, with far less entering the park from the Upper West Side.

-Via Cooper Smith