Location Optimization of Cold Chain Logistics Parks Based on Bayesian Probability Theory and K-means Clustering Analysis in China

Location Optimization of Cold Chain Logistics Parks Based on Bayesian Probability Theory and K-means Clustering Analysis in China | 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 Location Optimization of Cold Chain Logistics Parks Based on Bayesian Probability Theory and K-means Clustering Analysis in China Lu Wang, Xuannuo Liu, Xuhui Wang, Yijun Jiang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7453335/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract The site selection of cold-chain logistics parks is an indispensable part of their planning and construction. This study aims to establish site selection model provide a scientific and sustainable for selecting and determining optimal cold chain logistics parks sites. Traditional site selection methods lacking quantitative standards for assessing the reliability of results. In response, this study introduces Bayesian probability theory to construct a Bayesian network model. This model selects and quantifies influencing factors for site selection, establishing a scientifically evaluation indicator system. Subsequently, utilizing K-means clustering analysis to develop a site selection model. The reliability of clustering results is verified using Bayesian discriminant analysis. Furthermore, a city within the first-class cluster is selected to construct a comprehensive suitability evaluation indicator system for cold-chain logistics park location using Geographic Information System (GIS) technology. Jiangsu Province is chosen as the study area to validate the model, and the analysis demonstrates that Suzhou is the most suitable location for establishing a cold-chain logistics park. The comprehensive suitability evaluation further divides Suzhou into five distinct zones, from which the optimal site is identified and confirmed. Overall, the established site selection model provides a scientific and reliable approach for selecting and determining optimal sites. Physical sciences/Engineering Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences Physical sciences/Mathematics and computing cold chain logistics parks site selection Bayesian Probability Theory K-means clustering suitability evaluation GIS technology Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 14 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 08 Sep, 2025 Reviews received at journal 06 Sep, 2025 Reviews received at journal 02 Sep, 2025 Reviewers agreed at journal 02 Sep, 2025 Reviewers agreed at journal 02 Sep, 2025 Reviewers invited by journal 02 Sep, 2025 Editor assigned by journal 02 Sep, 2025 Editor invited by journal 29 Aug, 2025 Submission checks completed at journal 28 Aug, 2025 First submitted to journal 28 Aug, 2025 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-7453335","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":511639421,"identity":"be476f73-0243-4980-b706-027c6ce1ddb4","order_by":0,"name":"Lu Wang","email":"data:image/png;base64,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","orcid":"","institution":"North West Agriculture and Forestry University","correspondingAuthor":true,"prefix":"","firstName":"Lu","middleName":"","lastName":"Wang","suffix":""},{"id":511639422,"identity":"1f669808-b8d6-4759-b629-16a35518f1a0","order_by":1,"name":"Xuannuo Liu","email":"","orcid":"","institution":"North West Agriculture and Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Xuannuo","middleName":"","lastName":"Liu","suffix":""},{"id":511639423,"identity":"c751708c-6ef2-48d4-a845-1adb46d9b71b","order_by":2,"name":"Xuhui Wang","email":"","orcid":"","institution":"North West Agriculture and Forestry University","correspondingAuthor":false,"prefix":"","firstName":"Xuhui","middleName":"","lastName":"Wang","suffix":""},{"id":511639424,"identity":"d42e05a8-1375-4f32-8373-7b9705ad1344","order_by":3,"name":"Yijun Jiang","email":"","orcid":"","institution":"The University of Kitakyushu","correspondingAuthor":false,"prefix":"","firstName":"Yijun","middleName":"","lastName":"Jiang","suffix":""}],"badges":[],"createdAt":"2025-08-25 11:38:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7453335/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7453335/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-23591-x","type":"published","date":"2025-11-14T15:57:12+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":96105336,"identity":"e5bb6ba6-7197-4259-bd97-b38b829a602d","added_by":"auto","created_at":"2025-11-17 16:11:14","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1697556,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7453335/v1_covered_c0952c79-1ebc-4e81-aa49-a9bc78c0f3ed.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Location Optimization of Cold Chain Logistics Parks Based on Bayesian Probability Theory and K-means Clustering Analysis in China","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"cold chain logistics parks, site selection, Bayesian Probability Theory, K-means clustering, suitability evaluation, GIS technology","lastPublishedDoi":"10.21203/rs.3.rs-7453335/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7453335/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe site selection of cold-chain logistics parks is an indispensable part of their planning and construction. This study aims to establish site selection model provide a scientific and sustainable for selecting and determining optimal cold chain logistics parks sites. Traditional site selection methods lacking quantitative standards for assessing the reliability of results. In response, this study introduces Bayesian probability theory to construct a Bayesian network model. This model selects and quantifies influencing factors for site selection, establishing a scientifically evaluation indicator system. Subsequently, utilizing K-means clustering analysis to develop a site selection model. The reliability of clustering results is verified using Bayesian discriminant analysis. Furthermore, a city within the first-class cluster is selected to construct a comprehensive suitability evaluation indicator system for cold-chain logistics park location using Geographic Information System (GIS) technology. Jiangsu Province is chosen as the study area to validate the model, and the analysis demonstrates that Suzhou is the most suitable location for establishing a cold-chain logistics park. The comprehensive suitability evaluation further divides Suzhou into five distinct zones, from which the optimal site is identified and confirmed. Overall, the established site selection model provides a scientific and reliable approach for selecting and determining optimal sites.\u003c/p\u003e","manuscriptTitle":"Location Optimization of Cold Chain Logistics Parks Based on Bayesian Probability Theory and K-means Clustering Analysis in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-14 10:38:07","doi":"10.21203/rs.3.rs-7453335/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-08T05:46:37+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-06T09:13:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-02T16:53:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"125311008015949342847859052009962980512","date":"2025-09-02T15:31:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"178073268657983711662092857218442483816","date":"2025-09-02T14:48:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-02T12:26:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-02T12:15:36+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-29T10:59:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-28T12:52:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-08-28T12:44:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7258772a-f958-4834-8a7a-b5e15ff6c5ce","owner":[],"postedDate":"September 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":54334470,"name":"Physical sciences/Engineering"},{"id":54334471,"name":"Earth and environmental sciences/Environmental sciences"},{"id":54334472,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":54334473,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2025-11-17T16:07:38+00:00","versionOfRecord":{"articleIdentity":"rs-7453335","link":"https://doi.org/10.1038/s41598-025-23591-x","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-11-14 15:57:12","publishedOnDateReadable":"November 14th, 2025"},"versionCreatedAt":"2025-09-14 10:38:07","video":"","vorDoi":"10.1038/s41598-025-23591-x","vorDoiUrl":"https://doi.org/10.1038/s41598-025-23591-x","workflowStages":[]},"version":"v1","identity":"rs-7453335","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7453335","identity":"rs-7453335","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}