A Spatial Co-location Pattern Mining Algorithm Without Distance Thresholds

Abstract

Spatial co-location pattern mining is a process of finding a group of distinct spatial features whose instances frequently appear in close proximity to each other. The proximity of instances is often defined by the distance between them, if the distance is smaller than a distance threshold specified by users, they have a neighbor relationship. However, in this definition, the proximity of instances deeply depends on the distance threshold, the heterogeneity of the distribution density of spatial datasets is neglected, and it is hard for users to give a suitable threshold value. In this paper, we propose a statistical method that eliminates the distance threshold parameters from users to determine the neighbor relationships of instances in space. First, the proximity of instances is roughly materialized by employing Delaunay triangulation. Then, according to the statistical information of the vertices and edges in the Delaunay triangulation, we design three strategies to constrain the Delaunay triangulation. The neighbor relationships of instances are extracted automatically and accurately from the constrained Delaunay triangulation without requiring users to specify distance thresholds. After that, we propose a k-order neighbor notion to get neighborhoods of instances for mining co-location patterns. Finally, we develop a constrained Delaunay triangulation-based k-order neighborhood co-location pattern mining algorithm called CDT-kN-CP. The results of testing our algorithm on both synthetic datasets and the real point-of-interest datasets of Beijing and Guangzhou, China indicate that our method improves both accuracy and scalability compared with previous methods.