Mining Spatial co-location Patterns with Star High Influence

Abstract

The spatial co-location pattern is a group of spatial features whose instances are frequently collocated in the spatial neighborhood.Traditional spatial co-location pattern mining methods usually assume that the spatial instances are independent each other,and use participation index (PI) to measure the patterns.They don't consider the influence of different features or different instances of the same feature so that the mining results are often lack of relevance and interpretability.This paper proposes the spatial co-location pattern with star high influence which has influence in the neighborhood,and its mining method.Firstly,this paper defines two indicators to measure the influence of the pattern:influence participation index (IPI) and influence occupancy index (IOI).Secondly,a basic algorithm and pruning strategies for mining co-location patterns with star high influence are proposed.Finally,the experimental results on real and synthetic data sets show that the proposed method can discover the strong relevant co-location patterns.

Keywords

• spatial data mining|spatial co-location pattern|star influence|high influence pattern