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 ac-tive 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