A Spatial Interaction Model for the Identification of Urban Functional Regions

Urban Functional regions represent dynamic formations molded by spatial interactions, illustrating the intricate connections between different geographic areas. Identifying functional regions is essential for supporting urban planning efforts and promoting sustainable development. This study presents a comprehensive framework to characterize critical road network locations, integrating structural, functional, and geographical dimensions of the built environment. Addressing limitations in one-dimensional networks and multivariate issues, the study utilizes spatial density-based hotspot detection approaches and develops a method to identify critical locations as multivariate hotspots. Emphasizing critical locations as foundational units for functional regions, it then addresses spatial interaction data limitations through flexible modeling approaches, enhancing local modeling with Artificial Neural Networks (ANNs) within Geographically Weighted Regression (GWR) models. Lastly, dynamic spatial interactions are considered using overlapping community detection methods, offering a structured framework for identifying overlapping functional regions in urban landscapes. This study enhances the identification and analysis of urban functional regions, providing deeper insights into spatial complexities and facilitating more effective decision-making for sustainability.