City-scale UAV planning requires a clear distinction between areas screened out by operational constraints and areas retained for graded suitability assessment. To address this need, this study develops a grid-based framework for near-ground airspace up to 120 m above ground level in Yangzhou, China, and examines the resulting spatial patterns. Multi-source geospatial and regulatory data are integrated into regular hexagonal cells with a side length of 100 m. The framework first applies no-fly zone-related constraint screening and then evaluates the remaining cells using nine suitability indicators weighted by the Criteria Importance Through Intercriteria Correlation method. Global and local Moran’s I are used to identify spatial dependence, local clusters, and spatial outliers, while ordinary least squares and geographically weighted regression characterize global and spatially varying associations. The suitability index exhibits significant positive spatial autocorrelation (Moran’s I = 0.738, p = 0.005), with continuous high–high and low–low clusters and localized transition zones. Compared with OLS, GWR increases R² from 0.876 to 0.911 and reduces residual Moran’s I from 0.275 to 0.127, providing evidence of spatial non-stationarity across urban subregions. Overall, the framework integrates constraint screening, graded suitability assessment, and spatial heterogeneity analysis into a consistent geospatial decision-support structure for differentiated pre-operational UAV deployment and urban low-altitude airspace planning.