Beyond Euclidean Zoning: Machine Learning Reveals Empirical Patterns of Land Use Compatibility

Beyond Euclidean Zoning: Machine Learning Reveals Empirical Patterns of Land Use Compatibility | 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 Beyond Euclidean Zoning: Machine Learning Reveals Empirical Patterns of Land Use Compatibility Omid Mansourihanis, Xuantong Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9043733/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 Despite over a century of zoning practice, land use compatibility — the degree to which adjacent land uses coexist without generating negative externalities — remains assessed through expert judgment and inherited assumptions rather than empirical evidence of urban outcomes. Given that compatibility standards underpin billions of dollars in annual zoning decisions, their empirical validity carries direct consequences for urban livability. This study presents a machine learning framework that shifts compatibility assessment from subjective prescription to data-driven discovery. Applying gradient boosting regression to 134,863 census blocks across New York, Los Angeles, Chicago, and Houston, the model predicts composite compatibility scores derived from six dimensions — functional diversity, activity complementarity, conflict minimization, accessibility synergy, socioeconomic integration, and infrastructure efficiency — using 32 features capturing land use composition, built environment morphology, transportation infrastructure, and socioeconomic context. The model achieves strong predictive performance (R² = 0.895, RMSE = 0.087), establishing that compatibility follows systematic, learnable patterns. Land use entropy dominates prediction (63.7% feature importance), providing robust evidence that functional mixing enhances neighborhood quality — directly challenging Euclidean use-separation zoning. Context-specific compatibility matrices reveal that 45 of 55 land use pairings are empirically estimable in transit-oriented contexts versus only one in auto-dependent suburbs, demonstrating that compatibility is locally conditioned rather than universally fixed. Most pairings fall within a moderate range, with mixed-use designations consistently scoring highest. The study identifies empirically derived attribute thresholds for high-compatibility neighborhoods, offering planners measurable benchmarks — including optimal entropy ranges, building height and coverage parameters, and land use composition targets — to replace arbitrary development standards. Land use compatibility machine learning gradient boosting Euclidean zoning land use entropy mixed-use development urban big data evidence-based planning 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-9043733","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":611155973,"identity":"2694202b-cd5e-4de2-8790-68919cc51865","order_by":0,"name":"Omid Mansourihanis","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArUlEQVRIiWNgGAWjYDADfgjFTIIWyTZmUrUYHCNWC7/Y4WcPf1Qczje+339MgqHCOrGBoHtmp5kb85w5bLntGDObBMOZdMJaDG4nmEkzth02MANpATKI0ZL+TfInUItxG0jLP6K05JhJ8AK1GLCBtDQQoUVydk6ZNM+ZdAOJY8nGFgnH0o0JauGXTt8m+aPC2oC/+eDDGx9qrGUJakEFCaQpHwWjYBSMglGACwAAA8k2zUGKfHwAAAAASUVORK5CYII=","orcid":"","institution":"Texas Tech University","correspondingAuthor":true,"prefix":"","firstName":"Omid","middleName":"","lastName":"Mansourihanis","suffix":""},{"id":611155974,"identity":"507676f2-220b-46cf-a8a5-3df8baed5966","order_by":1,"name":"Xuantong Wang","email":"","orcid":"","institution":"Texas Tech University","correspondingAuthor":false,"prefix":"","firstName":"Xuantong","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2026-03-05 19:38:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9043733/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9043733/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108492021,"identity":"6eb0941b-01b7-4226-943b-189dd9dfc1be","added_by":"auto","created_at":"2026-05-05 09:56:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2473830,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscriptwithoutauthordetails.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9043733/v1_covered_30a7c5ce-755f-44c8-b946-528a4abc90a5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Beyond Euclidean Zoning: Machine Learning Reveals Empirical Patterns of Land Use Compatibility","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Land use compatibility, machine learning, gradient boosting, Euclidean zoning, land use entropy, mixed-use development, urban big data, evidence-based planning","lastPublishedDoi":"10.21203/rs.3.rs-9043733/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9043733/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDespite over a century of zoning practice, land use compatibility \u0026mdash; the degree to which adjacent land uses coexist without generating negative externalities \u0026mdash; remains assessed through expert judgment and inherited assumptions rather than empirical evidence of urban outcomes. Given that compatibility standards underpin billions of dollars in annual zoning decisions, their empirical validity carries direct consequences for urban livability. This study presents a machine learning framework that shifts compatibility assessment from subjective prescription to data-driven discovery. Applying gradient boosting regression to 134,863 census blocks across New York, Los Angeles, Chicago, and Houston, the model predicts composite compatibility scores derived from six dimensions \u0026mdash; functional diversity, activity complementarity, conflict minimization, accessibility synergy, socioeconomic integration, and infrastructure efficiency \u0026mdash; using 32 features capturing land use composition, built environment morphology, transportation infrastructure, and socioeconomic context. The model achieves strong predictive performance (R\u0026sup2; = 0.895, RMSE\u0026thinsp;=\u0026thinsp;0.087), establishing that compatibility follows systematic, learnable patterns. Land use entropy dominates prediction (63.7% feature importance), providing robust evidence that functional mixing enhances neighborhood quality \u0026mdash; directly challenging Euclidean use-separation zoning. Context-specific compatibility matrices reveal that 45 of 55 land use pairings are empirically estimable in transit-oriented contexts versus only one in auto-dependent suburbs, demonstrating that compatibility is locally conditioned rather than universally fixed. Most pairings fall within a moderate range, with mixed-use designations consistently scoring highest. The study identifies empirically derived attribute thresholds for high-compatibility neighborhoods, offering planners measurable benchmarks \u0026mdash; including optimal entropy ranges, building height and coverage parameters, and land use composition targets \u0026mdash; to replace arbitrary development standards.\u003c/p\u003e","manuscriptTitle":"Beyond Euclidean Zoning: Machine Learning Reveals Empirical Patterns of Land Use Compatibility","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-25 15:18:02","doi":"10.21203/rs.3.rs-9043733/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"be2756c4-fbcf-4150-baad-efb48ab79a34","owner":[],"postedDate":"March 25th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-03T04:23:51+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-03T04:39:59+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-25 15:18:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9043733","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9043733","identity":"rs-9043733","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}