Efficient Discovery of Spatial Co-Location Patterns Using the iCPI-tree

Abstract

With the rapid growth and extensive applications of the spatial dataset, it's getting more important to solve how to find spatial knowledge automatically from spatial datasets. Spatial co-location patterns represent the subsets of features whose instances are frequently located together in geographic space. It's difficult to discovery co-location patterns be- cause of the huge amount of data brought by the instances of spatial features. A large fraction of the computation time is devoted to identifying the table instances of co-location patterns. The essence of co-location patterns discovery and four co-location patterns mining algorithms proposed in recent years are analyzed, and a new join-less approach for co-location patterns mining, which based on a data structure----iCPI-tree (Improved Co-location Pattern Instance Tree), is proposed. The iCPI-tree is an improved version of the CPI-tree which materializes spatial neighbor relationships in order to acceler- ate the process of identifying co-location instances. This paper proves the correctness and completeness of the new ap- proach. Finally, an experimental evaluations using synthetic and real world datasets show that the algorithm is computa- tionally more efficient.