Unraveling nonlinear impacts of seasonal climate and built environments on exercise walking in high- density cities via a modified machine learning approach

Unraveling nonlinear impacts of seasonal climate and built environments on exercise walking in high- density cities via a modified machine learning approach | 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 Unraveling nonlinear impacts of seasonal climate and built environments on exercise walking in high- density cities via a modified machine learning approach Bo Lu, Tianxiang Long, Bo Li, Yu Chen, Lin Zhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7636974/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Feb, 2026 Read the published version in International Journal of Health Geographics → Version 1 posted 10 You are reading this latest preprint version Abstract Background: Physical inactivity is a major health risk worldwide, while walking is one of the most accessible forms of exercise that improves public health and supports sustainable urban mobility. Yet the combined and nonlinear effects of the built environment and seasonal climate on exercise walking in high-density cities remain insufficiently explored. This study aims to uncover these relationships and provide insights for health-oriented and climate-adaptive urban planning. Methods: Crowdsourced walking trajectory data were analyzed for three representative high-density Chinese cities，Beijing, Wuhan, and Guangzhou，covering both summer and winter. A comprehensive variable system was established, incorporating built environment, seasonal climate, and socioeconomic factors. A geographically weighted extreme gradient boosting model was developed with Bayesian optimization and cross-validation to improve robustness. Interpretability was achieved through Shapley Additive Explanations, partial dependence plots, and clustering analysis to identify global and local drivers of walking activity. Results: The geographically weighted extreme gradient boosting model outperformed traditional regression and other machine learning models in prediction accuracy. Walking trajectories showed clear spatial clustering, with central urban cores as hotspots, and seasonal differences most pronounced in Beijing. Walk Score was consistently the most stable and influential factor across cities and seasons. Among climatic variables, air quality and temperature had the strongest impacts, particularly in winter. Variables exhibited three types of nonlinear responses: sustained growth (such as Walk Score and pedestrian street length), threshold-sensitive (such as intersection density and population density), and fluctuating patterns (such as air quality and housing prices). Local cluster analysis revealed three context-specific patterns: environment-driven areas such as parks and campuses, function-driven commercial centers, and structurally imbalanced or transitional zones. Conclusions: Exercise walking in high-density cities is shaped by both seasonal climate variability and spatial heterogeneity of the built environment. Improving pedestrian infrastructure, managing density thresholds, and implementing climate sensitive design can mitigate adverse weather impacts and foster year-round walking. Tailored strategies, including enhancing microclimate resilience in ecological zones, optimizing density and functional mix in commercial districts, and restructuring fragmented large blocks, are essential to create pedestrian friendly, health oriented, and climate adaptive cities. Built environment Seasonal climatic factors High density cities Exercise walking GW-XGBoost Health-oriented Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.docx Cite Share Download PDF Status: Published Journal Publication published 06 Feb, 2026 Read the published version in International Journal of Health Geographics → Version 1 posted Editorial decision: Revision requested 05 Nov, 2025 Reviews received at journal 28 Oct, 2025 Reviews received at journal 26 Oct, 2025 Reviewers agreed at journal 01 Oct, 2025 Reviewers agreed at journal 01 Oct, 2025 Reviewers agreed at journal 30 Sep, 2025 Reviewers invited by journal 30 Sep, 2025 Editor assigned by journal 22 Sep, 2025 Submission checks completed at journal 22 Sep, 2025 First submitted to journal 17 Sep, 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. 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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-7636974","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":527720412,"identity":"c3cca8d7-6e03-489d-a172-a1ce613f3e0b","order_by":0,"name":"Bo Lu","email":"","orcid":"","institution":"Central South University","correspondingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"Lu","suffix":""},{"id":527720413,"identity":"05eae0af-3b9a-43a5-8a78-9abc9a82b80e","order_by":1,"name":"Tianxiang Long","email":"","orcid":"","institution":"Hunan City 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environments on exercise walking in high- density cities via a modified machine learning approach","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":"international-journal-of-health-geographics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijhg","sideBox":"Learn more about [International Journal of Health Geographics](http://ij-healthgeographics.biomedcentral.com/)","snPcode":"12942","submissionUrl":"https://submission.nature.com/new-submission/12942/3","title":"International Journal of Health Geographics","twitterHandle":"@IJHGeo","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Built environment, Seasonal climatic factors, High density cities, Exercise walking, GW-XGBoost, Health-oriented","lastPublishedDoi":"10.21203/rs.3.rs-7636974/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7636974/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003cbr\u003e\nPhysical inactivity is a major health risk worldwide, while walking is one of the most accessible forms of exercise that improves public health and supports sustainable urban mobility. Yet the combined and nonlinear effects of the built environment and seasonal climate on exercise walking in high-density cities remain insufficiently explored. This study aims to uncover these relationships and provide insights for health-oriented and climate-adaptive urban planning.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003cbr\u003e\nCrowdsourced walking trajectory data were analyzed for three representative high-density Chinese cities，Beijing, Wuhan, and Guangzhou，covering both summer and winter. A comprehensive variable system was established, incorporating built environment, seasonal climate, and socioeconomic factors. A geographically weighted extreme gradient boosting model was developed with Bayesian optimization and cross-validation to improve robustness. Interpretability was achieved through Shapley Additive Explanations, partial dependence plots, and clustering analysis to identify global and local drivers of walking activity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003cbr\u003e\nThe geographically weighted extreme gradient boosting model outperformed traditional regression and other machine learning models in prediction accuracy. Walking trajectories showed clear spatial clustering, with central urban cores as hotspots, and seasonal differences most pronounced in Beijing. Walk Score was consistently the most stable and influential factor across cities and seasons. Among climatic variables, air quality and temperature had the strongest impacts, particularly in winter. Variables exhibited three types of nonlinear responses: sustained growth (such as Walk Score and pedestrian street length), threshold-sensitive (such as intersection density and population density), and fluctuating patterns (such as air quality and housing prices). Local cluster analysis revealed three context-specific patterns: environment-driven areas such as parks and campuses, function-driven commercial centers, and structurally imbalanced or transitional zones.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e\u003cbr\u003e\nExercise walking in high-density cities is shaped by both seasonal climate variability and spatial heterogeneity of the built environment. Improving pedestrian infrastructure, managing density thresholds, and implementing climate sensitive design can mitigate adverse weather impacts and foster year-round walking. Tailored strategies, including enhancing microclimate resilience in ecological zones, optimizing density and functional mix in commercial districts, and restructuring fragmented large blocks, are essential to create pedestrian friendly, health oriented, and climate adaptive cities.\u003c/p\u003e","manuscriptTitle":"Unraveling nonlinear impacts of seasonal climate and built environments on exercise walking in high- density cities via a modified machine learning approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-13 11:09:37","doi":"10.21203/rs.3.rs-7636974/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-05T15:54:36+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-28T14:45:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-26T16:36:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"169615629104058126282111437501585333046","date":"2025-10-01T08:24:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"38800136508648605402496414303308339919","date":"2025-10-01T04:49:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"15034091453966591489429340573465610503","date":"2025-09-30T16:04:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-30T13:50:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-22T11:01:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-22T11:01:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Health Geographics","date":"2025-09-17T07:18:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-health-geographics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijhg","sideBox":"Learn more about [International Journal of Health Geographics](http://ij-healthgeographics.biomedcentral.com/)","snPcode":"12942","submissionUrl":"https://submission.nature.com/new-submission/12942/3","title":"International Journal of Health Geographics","twitterHandle":"@IJHGeo","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c76fe0ed-350a-4c57-b158-317d4e32accc","owner":[],"postedDate":"October 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T16:05:48+00:00","versionOfRecord":{"articleIdentity":"rs-7636974","link":"https://doi.org/10.1186/s12942-026-00453-x","journal":{"identity":"international-journal-of-health-geographics","isVorOnly":false,"title":"International Journal of Health Geographics"},"publishedOn":"2026-02-06 15:58:31","publishedOnDateReadable":"February 6th, 2026"},"versionCreatedAt":"2025-10-13 11:09:37","video":"","vorDoi":"10.1186/s12942-026-00453-x","vorDoiUrl":"https://doi.org/10.1186/s12942-026-00453-x","workflowStages":[]},"version":"v1","identity":"rs-7636974","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7636974","identity":"rs-7636974","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}