A Method for Mining Spatial Co-location Patterns Based on Contextual Similarity Among Categories

In the field of Geographic Information Science (GIS), the identification of spatial co-location patterns is of paramount importance for the acquisition of insights into complex geographical phenomena and the provision of decision support. However, the rapid growth of spatial datasets and the increasing complexity of data relationships present significant challenges to existing mining algorithms. The majority of these methods rely on co-occurrence frequency information to mine co-location patterns, simplifying the complex relationships between instances and the holistic information of local contexts, which leads to insufficient accuracy or information omission in mining results. To address this issue, we propose a spatial co-location pattern mining approach based on contextual similarity among categories. This method employs a novel embedding technique to obtain detailed local context information through multiple feature extractors and convert it into low-dimensional embeddings. On this basis, local context information under the same category is fused to obtain the features of the contexts where the categories are located, and then, regional feature similarity is calculated to achieve efficient spatial co-location pattern mining. The proposed method was validated using simulated data and field data from Xiamen Island, and it successfully identified multiple spatial co-location patterns, demonstrating its feasibility. Comparative experiments with other methods also showed that the proposed method is superior and more robust in mining spatial co-location patterns.