I’ve recently started incorporating an application called Moves into my self-tracking arsenal. As an app, Moves only does just one thing, but does it well – it runs in the background on your smartphone and passively tracks your location 24/7. It has no social sharing functionality, gamification, or bloated features. You can look at a timeline of your day and see how long you were actually at work versus commuting (it tracks steps as well if you are into that sort of stuff).

The location tracking app recently released an API to allow 3rd-party developers and apps to integrate their data. Notable design/data visualization guru Nicholas Felton whipped together some code using Processing that provides a cool visualization layer to my Moves location data. Here is what a typical New York City day looks like for me:

Moves app data visualization

The yellow lines indicate times when I was walking (to subway, out for lunch, walking the dog), gray lines show when I was in transit (subway, cab, driving), and blue lines represent times I was riding my bike (in this case, to a soccer game). Pretty neat, but nothing too exciting. However, if we look at several weeks worth of data, we can reveal some interesting trends!

Note that I left out two days of data where I went on out of state trips, as this skews the visualization (in its current incarnation). Check out Moves, and and if you aren’t afraid to get too technical you can get the MovesMapper code here. And this is a great set of location data that can be easily exported and integrated into other self-tracking experiments.