Density based co-location pattern discovery

Xiangye Xiao, Xing Xie, Qiong Luo, Wei-Ying Ma
2008 Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems - GIS '08  
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
more » ... n 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 in the remaining partitions. We prove that our approach is complete and correct in finding co-location patterns. Experimental results on real data sets show that our method outperforms a traditional approach.
doi:10.1145/1463434.1463471 dblp:conf/gis/XiaoXLM08 fatcat:7rx2dhhekzadniydnv7m3tt7yq