Lightweight Extraction of Frequent Spatio-Temporal Activities from GPS Traces

In this paper we present a classification of human movement in physical space into spatio-temporal activities (STAs) and classes thereof. Drawing from our experiences with real human data from GPS traces we define a clustering approach for STA extraction based on the amount of motion of the user in space and time. Our solution captures these properties in a lightweight online algorithm that can run inside mobile devices. We then cluster the discovered STAs into classes based on a similarity metric that aims to identify which activities (STAs) are consistent in time. In contrast to previous approaches of discovering important places, this work also utilizes the temporal properties of the data to extract more realistic STAs and STA classes. Our work is evaluated through simulations and real GPS traces.