A GeoAI-Enabled Spatial Capacitated Allocation Network for Waste Collection Facility Siting with Exclusion Zones

A GeoAI-Enabled Spatial Capacitated Allocation Network for Waste Collection Facility Siting with Exclusion Zones | 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 A GeoAI-Enabled Spatial Capacitated Allocation Network for Waste Collection Facility Siting with Exclusion Zones Pinglu Gong, Wei He, Jun Ma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8434796/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 Determining locations for waste collection points that are practicable and efficient is a core challenge for municipal solid waste management. Planners should simultaneously balance coverage, walking distance, capacity limits, and environmental siting constraints such as buffers around sensitive areas. Existing approaches tend to address only parts of this problem: Geographic Information System–based multi-criteria decision analysis (GIS–MCDA) studies focus on suitability mapping without explicit capacity or assignment; classical facility location models assume idealized settings and rigid hard constraints with limited exclusion handling; and clustering methods emphasize geometric compactness but rarely incorporate exclusion zones or operational capacity ranges. In rural and small-town settings, these gaps are often bridged by manual siting rather than through reproducible, data-driven tools. This paper proposes the Spatial Capacitated Allocation Network (SCAN) model, a capacity-constrained spatial clustering framework for siting waste collection points in both dense urban neighborhoods and dispersed rural townships. SCAN combines a mixed-integer assignment step with continuous updates of facility locations in an Alternating Assignment–Relocation (AAR) algorithm, a coordinate-descent procedure inspired by k-means, extended to handle capacity bands, soft service-radius penalties, and exclusion zones within a unified workflow. Experiments on a compact high-rise neighborhood in Shenzhen and a mountainous rural county in Sichuan, China, show that SCAN attains high coverage, short access distances, and good capacity utilization while respecting GIS-derived exclusion constraints where planning often relies on manual site selection. Together, these results demonstrate that SCAN provides a flexible decision-support tool for geospatially aware planning of waste collection infrastructure across settlement patterns. GeoAI Spatial analysis Capacitated spatial clustering Facility location Exclusion zones 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-8434796","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":572878084,"identity":"cdc0d344-8db3-47cf-82b2-f6994010a017","order_by":0,"name":"Pinglu Gong","email":"","orcid":"","institution":"University of Hong Kong","correspondingAuthor":false,"prefix":"","firstName":"Pinglu","middleName":"","lastName":"Gong","suffix":""},{"id":572878086,"identity":"fdee6d7a-ff47-4b08-9ff6-836cc79d0ecb","order_by":1,"name":"Wei He","email":"","orcid":"","institution":"Wuhan University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"He","suffix":""},{"id":572878091,"identity":"a749d2e6-be54-4819-af33-9277f5b5522e","order_by":2,"name":"Jun Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAlklEQVRIiWNgGAWjYBACxgYQWXHAgIGBh5kULWdI0QLR10aKFuYZuUc3/Jx3x9jgAO9hA+IsmJGXdrN32zMzgwN8yQlEaskxu8G77bCNwQEe4wNEa7n5dw6pWm7zNhw2A2kh0mE9b8xuyxw7bCx5mMeYOO8btgMd9qbmsGHf8R5jCeK0NMBYREekPLEKR8EoGAWjYAQDAEiuMgl0trd8AAAAAElFTkSuQmCC","orcid":"","institution":"University of Hong Kong","correspondingAuthor":true,"prefix":"","firstName":"Jun","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2025-12-23 14:23:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8434796/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8434796/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101206755,"identity":"80949847-f6cf-49cf-ad64-2a7e0f08eb34","added_by":"auto","created_at":"2026-01-27 09:56:41","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1216150,"visible":true,"origin":"","legend":"","description":"","filename":"submittoJGSAmain.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8434796/v1_covered_4d84d571-87d5-4b8b-b02a-b5ae724e7c34.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A GeoAI-Enabled Spatial Capacitated Allocation Network for Waste Collection Facility Siting with Exclusion Zones","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":"GeoAI, Spatial analysis, Capacitated spatial clustering, Facility location, Exclusion zones","lastPublishedDoi":"10.21203/rs.3.rs-8434796/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8434796/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDetermining locations for waste collection points that are practicable and efficient is a core challenge for municipal solid waste management. Planners should simultaneously balance coverage, walking distance, capacity limits, and environmental siting constraints such as buffers around sensitive areas. Existing approaches tend to address only parts of this problem: Geographic Information System\u0026ndash;based multi-criteria decision analysis (GIS\u0026ndash;MCDA) studies focus on suitability mapping without explicit capacity or assignment; classical facility location models assume idealized settings and rigid hard constraints with limited exclusion handling; and clustering methods emphasize geometric compactness but rarely incorporate exclusion zones or operational capacity ranges. In rural and small-town settings, these gaps are often bridged by manual siting rather than through reproducible, data-driven tools. 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