Discovering daily routines from Google Latitude with topic models

Discovering users´ whereabouts patterns is important for many emerging ubiquitous computing applications. Life-log systems, advertisement and smart environments are only some of the applications that can be supported by information regarding user patterns and routine behaviors. Latent Dirichlet Allocation (LDA) is a powerful mechanism to extract recurrent behaviors and high-level patterns (called topics) from mobility data in an unsupervised manner. In this paper we test the effectiveness of LDA in identifying users´ routine behaviors from mobility data collected with Google Latitude. Results show that the proposed technique provides good results in discovering patterns and routine behaviors.