Modeling the Relationship Between Ride-Hailing Demand Density and the Urban Built Environment on the Basis of the GNNWR and Dynamic Spatial Partitioning

Understanding the complex spatiotemporal heterogeneity between ride-hailing demand and the urban built environment is crucial for optimizing urban space and allocating transportation resources. Previous analyses using fixed spatial scales (e.g., regular grids or administrative boundaries) often suffer from the modifiable areal unit problem (MAUP) and fail to capture the actual distribution of ride-hailing demand. Furthermore, traditional geographically weighted regression (GWR) models struggle with nonlinear spatial relationships. To address these challenges, this study uses Xiamen, China, as a case study. We employ the K-means++ clustering method on ride-hailing origin–destination (OD) data to generate dynamic spatial partitioning units, integrating them with points of interest (POIs), land use, and population data. A geographically neural network weighted regression (GNNWR) model is then introduced to quantify how built environment factors influence mobility, explicitly capturing nonlinear variations. Results show that dynamic spatial partitioning effectively mitigates the MAUP: the traditional GWR R2 improved from 0.414 (administrative units) and 0.577 (regular grids) to 0.649. Using these dynamic clustering units, the GNNWR model achieved an average R2 of 0.750, outperforming multiscale GWR (MGWR, 0.614) and traditional GWR (0.596) by approximately 22.1% and 25.8%, respectively. Empirically, the built environment’s impact exhibits pronounced spatiotemporal nonstationarity. Residential and office areas reveal distinct tidal commuting patterns driven by job-housing separation, while public transit density shows a dual substitution–complementarity relationship with ride-hailing. Ultimately, this research validates GNNWR’s applicability for microlevel geographic analysis, providing a robust scientific basis for differentiated traffic management and precise urban spatial planning.