dGridED-SCPM: A grid-clique-based approach for efficiently mining spatial co-location patterns

Spatial co-location pattern mining (SCPM) aims to discover sets of spatial features whose instances are frequently located in close geographic proximity. Most existing SCPM methods judge the neighbor relationships between instances by computing their Euclidean distance and enumerate all instances (participating instances) that participate in co-location patterns based on the neighbor relationships, resulting in time-consumption. To tackle the low efficiency issue, we propose an efficient grid-clique-based SCPM approach (dGridED-SCPM) in the paper. Specifically, we present a novel concept of d^-grid clique and prove the instances in a d^-grid clique satisfy the neighbor relationship with each other, which allows the partial participating instances of the patterns are queried from d^-grid cliques. Then, we design an algorithm (DGCS) and a hash table (AQIHash) for rapidly searching d^-grid cliques and immediately querying the partial participating instances. Next, we develop an algorithm (CS-DGHBS) for efficiently identifying the remaining participating instances of the patterns. Moreover, we prove the correctness and completeness of the proposed dGridED-SCPM algorithm, and analyze the complexity of dGridED-SCPM. We conduct extensive experiments on both real-world and synthetic datasets to evaluate the efficiency and scalability of dGridED-SCPM. The experimental results demonstrate that the dGridED-SCPM algorithm outperforms the five state-of-the-art baselines by several times or even orders of magnitude in the majority of cases, exhibiting superior performance. © 2024 Elsevier Ltd