Mining Interpretable Regional Co-location Patterns Based on Urban Functional Region Division

Discovering the correlation relationships of spatial facilities in urban functional regions is of great significance in analyzing urban planning and promoting urban development. However, existing regional co-location pattern mining methods do not fully consider the attributes of urban functional regions. Consequently, they fail to effectively capture the relationships between spatial facilities and regions, resulting in patterns lacking practical application value and interpretability. To address this issue, this paper proposes a novel regional co-location pattern (RCP) mining method based on urban functional region division. First, an ontology-based method for urban functional region division is proposed. On the basis of dividing urban functional regions, the expected mean distance is generated to adaptively calculate the neighbor relationships between instances in each region, and a parallel algorithm is designed to quickly mine RCPs in urban functional regions. Then, the correlation coefficient ( \(CC_{RP}\) $C{C}_{\mathit{RP}}$ ) measures the association strength between RCP and function type to provide interpretability for RCPs. Finally, extensive experiments on real-world datasets are conducted to verify the effectiveness of urban functional region partition and to demonstrate the superiority of the proposed RCP mining method.