Extracting human mobility patterns from GPS-based traces

In this paper we analyze few GPS-based traces to infer human mobility patterns. We propose a clustering method to extract the main points of interest, called geo-locations, from GPS data. Starting from geo-locations we propose a definition of community, the geo-community, which captures the relation between a spatial description of human movements and the social context where users live. A statistical analysis of the principal characteristics of human walks provide the fitting distributions of distances covered by people inside a geo-location and among geo-locations and pause time. Finally we analyze factors influencing people when choosing successive location in their movement.