Mining Evolving Spatial Co-location Patterns from Spatio-temporal Databases

Spatial co-location patterns (SCPs) represent the subsets of spatial features which are frequently located together in a geographic space. SCP mining has been a research hot in recent years. However, many applications include location-based services, rare animals and plants protection, transportation and environmental monitoring collect their data periodically or continuously. With the change of databases, the SCPs will evolve. Evolving spatial co-location patterns (ESCs) widely exist in spatio-temporal databases and discovering the ESCs can help us better understand the relationships between SCPs over time, find the variation trends of SCPs and dataset, and track the diversity of SCPs. This paper defines ESCs and proposes a two-step framework to discover ESCs. An extend-and-evaluate scheme is proposed to form ESCs by selecting appropriate evolvers from top-k spatial prevalent co-location patterns at each time slot. Two kinds of pattern division and storage are proposed to speed up the mining course. The experiments evaluate the effectiveness and efficiency of the proposed algorithms with “real + synthetic” datasets. Our important findings include identification of all the ESCs of mixed forests in Shilin nature preservation zone of Yunnan Province over ten years.