GIS spatial analysis of land use planning based onspatial database and geographical weightedregression model

Abstract In the era of geospatial big data and intelligent urban governance, the integration of advanced computational models withspatial analysis has become essential for effective and adaptive land use planning. Traditional approaches often struggle toaccommodate the complex, dynamic, and heterogeneous nature of urban environments, resulting in limited decision-makingcapabilities. To overcome these limitations, this study introduces an innovative framework that synergizes a hybrid spatialrepresentation model with a topology-guided inference strategy. The proposed framework formalizes geographic entitiesthrough a symbolic structure, enabling rigorous mathematical representation of spatial regions, boundaries, and topologies. Thehybrid model combines discrete topological graphs with continuous spatial fields, allowing for the encoding of non-Euclideanspatial relationships and supporting analyses across multiple spatial and temporal scales. This dual representation capturesboth granular spatial variations and abstract structural patterns. Complementing the model, the inference strategy utilizesdomain-specific knowledge and semantic constraints to perform context-aware reasoning, even in scenarios with incomplete,uncertain, or ambiguous data inputs. By embedding semantic understanding into spatial analytics, the system enhancesinterpretability and decision support capabilities. Empirical evaluations conducted across various urban datasets demonstratethe framework’s superiority over conventional models in terms of accuracy, adaptability, and explainability. The results indicatea marked improvement in spatial decision outcomes and predictive robustness, validating the framework’s potential as a criticaltool in the field of spatial informatics. Ultimately, this approach represents a significant advancement aligned with the goals ofintelligent urban governance, offering scalable and intelligent solutions to the evolving challenges of contemporary urbanmanagement.