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

GIS spatial analysis of land use planning based onspatial database and geographical weightedregression model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article GIS spatial analysis of land use planning based onspatial database and geographical weightedregression model Li Zhou, Yan Feng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7625081/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version 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. Earth and environmental sciences/Environmental social sciences Physical sciences/Mathematics and computing Spatial Informatics Hybrid Spatial Modeling Topology-guided Inference Geospatial Big Data Intelligent Urban Governance Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7625081","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":531614011,"identity":"b0133424-4fa2-41db-8637-45805923ce04","order_by":0,"name":"Li Zhou","email":"","orcid":"","institution":"Yangtze University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Zhou","suffix":""},{"id":531614012,"identity":"6d62c057-9ba2-4861-b601-f54fb63cb7f7","order_by":1,"name":"Yan 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