Mining spatio-temporal co-location patterns with weighted sliding window

Abstract

Spatial co-location patterns represent the subsets of features (co-location) whose events are frequently located together in geographic space. Spatio-temporal co-location (co-occurrence) pattern mining extends the mining task to the scope of both space and time. However, embedding the time factor into spatial co-location pattern mining process is a subtle problem. Previous researches either treat the time factor as an alternative dimension or simply carry out the mining process on each time segment. In this paper, we propose a weighted sliding window model (WSW-model) which introduces the impact of time interval between the spatio-temporal events into the interest measure of the spatio-temporal co-location patterns. We figure out that the aforementioned two approaches fit into the two special cases in our proposed model. We also propose an algorithm (STCP-Miner) to mine spatio-temporal co-location patterns. The experimental evaluation with both the synthetic data sets and a real world data set shows that our algorithm is relatively effective with different parameters.