Discovery of regional co-location patterns with k-nearest neighbor graph

Abstract

The spatial co-location pattern mining discovers the subsets of features of which the events are frequently located together in a geographic space. The current research on this topic adopts a distance threshold that has limitations in spatial data sets with various magnitudes of neighborhood distances, especially for mining of regional co-location patterns. In this paper, we propose a hierarchical co-location mining framework by considering both varieties of neighborhood distances and spatial heterogeneity. By adopting k-nearest neighbor graph (kNNG) instead of distance threshold, we propose "distance variation coefficient" as a new measure to drive the mining process and determine an individual neighborhood relationship graph for each region. The experimental results on a real world data set verify the effectiveness of our framework. © Springer-Verlag Berlin Heidelberg 2013.