A Data-driven Study on Preferred Situations for Running.

We analyzed a large data set from a mobile exercise application to find the preferred running situations of a large number of users. We categorized the users according to their running behaviors (i.e. regularly active, or rarely active over the year), then studied the influence of 15 features, including temporal, geographical and weather-based features for different user groups. We found that geographical features influence the behavior of less active runners.