Mining Spatio-Temporal Co-location Patterns with Weighted Sliding Window

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