Mining Local Tight Spatial Sub-Prevalent Co-Location Patterns

Spatial sub-frequent co-location patterns reveal the rich spatial relation-ship of spatial features and instances, which are widely used in real applications such as environmental protection, urban computing, public transportation, and so on. Existing sub-frequent pattern mining methods cannot distinguish patterns whose row instance spatial distributions are significantly different. Additionally, patterns whose row instances are tightly located in a local area can further reveal the particularity of the local area such as special environments and functions. Therefore, this paper proposes mining Local Tight Spatial Sub-frequent Co-location Patterns (LTSCPs). First, a relevancy index is presented to measure the local tightness between sub-frequent pattern row instances by analyzing mutual participation instances between row instances. The concept of LTSCPs is then proposed followed by an algorithm for mining these LTSCPs. Finally, a large number of experiments are carried out on synthetic and real datasets. The results show that the algorithm for mining LTSCPs is efficient and LTSCPs are practical.