A detection of multi-level co-location patterns based on column calculation and HDBSCAN clustering

Abstract

A spatial co-location pattern is a subset of a spatial feature set whose instances prevalently appear in nearby locations in space. The objective of spatial co-location pattern mining is to detect co-location patterns that are non-obvious, informative, or predictive. Due to the heterogeneity in the distribution of spatial instances, spatial co-location patterns are classified into global co-location patterns (GCPs) and local co-location patterns (LCPs). The technique that discovers both simultaneously is termed multi-level co-location pattern mining (MLCPM). However, existing MLCPM methods have room for improvement in efficiently identifying GCPs and perform poorly in discovering prevalent sub-areas of LCPs. To address these issues, we propose a novel MLCPM framework called ML-CCHDB. This framework enhances GCP mining efficiency by optimizing the column calculation method tailored for MLCPM. Furthermore, it utilizes the HDBSCAN clustering method to identify potential prevalent sub-areas of LCPs and develops an adaptive approach for generating input parameters to enhance detection efficiency and quality. Experimental results on both synthetic and real datasets demonstrate that the column calculation optimizations in ML-CCHDB effectively enhance efficiency. Moreover, HDBSCAN strikes a balance between efficiency and quality in prevalent sub-area mining. These results fully validate the proposed framework's effectiveness and efficiency.