PRED

Abstract

The availability of massive geo-annotated social media data sheds light on studying human mobility patterns. Among them, periodic pattern, \ie an individual visiting a geographical region with some specific time interval, has been recognized as one of the most important. Mining periodic patterns has a variety of applications, such as location prediction, anomaly detection, and location- and time-aware recommendation. However, it is a challenging task: the regions of a person and the periods of each region are both unknown. The interdependency between them makes the task even harder. Hence, existing methods are far from satisfactory for detecting periodic patterns from the low-sampling and noisy social media data. We propose a Bayesian non-parametric model, named \textbf{P}eriodic \textbf{RE}gion \textbf{D}etection (PRED), to discover periodic mobility patterns by jointly modeling the geographical and temporal information. Our method differs from previous studies in that it is non-parametric and thus does not require priori knowledge about an individual's mobility (\eg number of regions, period length, region size). Meanwhile, it models the time gap between two consecutive records rather than the exact visit time, making it less sensitive to data noise. Extensive experimental results on both synthetic and real-world datasets show that PRED outperforms the state-of-the-art methods significantly in four tasks: periodic region discovery, outlier movement finding, period detection, and location prediction.