Density based co-location pattern discovery

Co-location pattern discovery is to find classes of spatial objects that are frequently located together. For example, if two categories of businesses often locate together, they might be identified as a co-location pattern; if several biologic species frequently live in nearby places, they might be a co-location pattern. Most existing co-location pattern discovery methods are generate-and-test methods, that is, generate candidates, and test each candidate to determine whether it is a co-location pattern. In the test step, we identify instances of a candidate to obtain its prevalence. In general, instance identification is very costly. In order to reduce the computational cost of identifying instances, we propose a density based approach. We divide objects into partitions and identifying instances in dense partitions first. A dynamic upper bound of the prevalence for a candidate is maintained. If the current upper bound becomes less than a threshold, we stop identifying its instances ...

• Type: Conference
• Year: 2008
• Where: GIS
• Authors: Xiangye Xiao, Xing Xie, Qiong Luo, Wei-Ying Ma
• Keywords: Co-location Pattern Discovery, Database, GIS 2008, Keywords Co-location Patterns, Most Existing Co-location