Mining spatio-temporal chaining patterns in non-identity event databases

Spatio-temporal pattern mining attempts to discover unknown, potentially interesting and useful event sequences in which events occur within a specific time interval and spatial region. In the literature, mining of spatio-temporal sequential patterns generally relies on the existence of identity information for the accumulation of pattern appearances. For the recent trend of open data, which are mostly released without the specific identity information due to privacy concern, previous work will encounter the challenging difficulty to properly transform such non-identity data into the mining process. In this paper, we propose a practical approach, called Top K Spatio-Temporal Chaining Patterns Discovery (abbreviated as TKSTP), to discover frequent spatio-temporal chaining patterns. The TKSTP framework is applied on two real criminal datasets which are released without the identity information. As shown in our experimental studies, the proposed framework effectively discovers high-quality spatio-temporal patterns. In addition, case studies of crime pattern analysis also demonstrate their applicability and reveal several interestingly hidden phenomenons.