Classifying urban areas into residential, non-residential and mixed-use proportions using building footprints and geospatial models

Classifying urban areas into residential, non-residential and mixed-use proportions using building footprints and geospatial models | 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 Research Article Classifying urban areas into residential, non-residential and mixed-use proportions using building footprints and geospatial models Wole Ademola Adewole, Ortis Yankey, Edson Utazi, Chris Lloyd, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8773396/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 High-resolution building footprints have transformed urban mapping, yet functional use information remains scarce in rapidly urbanising, data-limited settings. This study presents a geospatial framework for estimating the proportions of residential, non-residential, and mixed-use areas, demonstrated in Lagos, Nigeria, with methodological components suitable for adaptation in other data-scarce cities. Using over 180,000 ground-truth building samples and 68 geospatial covariates, we apply Random Forest and Bayesian Hierarchical models to characterise urban function. Both models perform strongly (residential r = 0.85, 0.84; non-residential r = 0.72, 0.69), while the Bayesian model provides enhanced uncertainty quantification. The resulting 1-km² gridded functional surface captures Lagos’s urban structure, including dense residential districts, commercial corridors, and mixed-use transition zones. This study provides a method for producing a semantically enriched representation of urban function in an African megacity, offering a transferable framework for advancing urban analytics, population modelling, and sustainable development planning. Urban functional classification building footprint data proportional modelling settlement analysis building use Lagos Full Text Additional Declarations The authors declare no competing interests. Supplementary Files SupplementaryClassifyingurbanareasintosettlementclasses.docx Classifying urban areas into residential, non-residential and mixed-use proportions using building footprints and geospatial models 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. 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