The Influence Mechanism and Spatial Heterogeneity of Urban Spatial Structure on the Thermal Environment: A Case Study of the Central Urban Area of Jinan

Urban expansion and spatial restructuring significantly influence the urban thermal environment. This study investigates the central urban area of Jinan, developing a multi-dimensional spatial structure index system that integrates terrain, 2D/3D morphology, and layout based on multi-source data. Land surface temperature (LST) was derived from remote sensing imagery. Using road networks and triangulated irregular networks (TINs) generated from a digital elevation model (DEM), hybrid analysis units were created. Pearson correlation and bivariate global/local spatial autocorrelation analyses were applied to examine the mechanisms and spatial heterogeneity of how urban spatial structure affects LST. The results showed that (1) LST was strongly associated with urban spatial structure. Among the 12 significantly correlated indicators, building density showed the strongest positive correlation with LST (r = 0.5883), while DEM mean had the strongest negative correlation (r = −0.7444), indicating that compact built-up areas intensified heating, whereas terrain most strongly moderated surface temperature. (2) LST and indicator correlations varied with elevation. LST showed a negative correlation with the standard deviation of DEM, suggesting that greater terrain variability enhances cooling effects. This spatial variation in the dominant drivers of the thermal environment reflects a clear divergence of influencing factors across different elevational zones. The thermal environment exhibits a pronounced north–south split: cooling effects prevail in the south due to terrain, while warming effects dominate in the north due to building forms. (3) Bivariate spatial autocorrelation revealed clear spatial heterogeneity. High–high clustering of LST and spatial structure indicators in the northern plain denoted heat-aggregated zones. Low–low clustering in the topographically complex, sparsely built south formed cold-source zones, and transitional areas showed mixed high–low and low–high clustering. (4) Based on these findings, a zonal governance framework was advocated, prioritizing terrain assessment followed by spatial structure optimization. This promoted a shift from uniform to precise, zone-based thermal environment management, laying a scientific foundation for sustainable spatial planning.