Unsupervised inference of significant locations from WiFi data for understanding human dynamics

Motion and location activities are essential to understanding human dynamics. This paper presents a method for discovering significant locations and individuals' daily routines from WiFi data, a data source considered more suitable for analyzing human dynamics than GPS data. Our method determines significant locations by clustering access points in close proximity using the Affinity Propagation algorithm. We demonstrate the method on the MDC dataset that includes more than 30 million WiFi scans. The experimental results show a high clustering performance for most of the users. The discovered location trajectories revealed interesting mobility patterns of mobile phone users. The human dynamics of participants is reflected through the entropy of the location distributions which shows interesting correlation with the age and occupations of users. Quantitative results are presented to support our proposed approach