From Crowdsourced Data to Policy Design: Monitoring and Forecasting Homeless Tents

From Crowdsourced Data to Policy Design: Monitoring and Forecasting Homeless Tents | 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 From Crowdsourced Data to Policy Design: Monitoring and Forecasting Homeless Tents Wooyong Jung, Sola Kim, Dongwook Kim, Andre Sihombing, Maryam Tabar, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7754821/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Mar, 2026 Read the published version in EPJ Data Science → Version 1 posted 10 You are reading this latest preprint version Abstract Homelessness in the United States remains a persistent and growing crisis, further complicated by a shortage of high-quality, high-resolution data. The existing Point-in-Time (PIT) count data has several limitations, as it relies on single-night counts with inconsistent coverage and cannot capture the dynamic and seasonal nature of unsheltered homelessness. To address this gap, we integrate multiple crowdsourced data sources---including 311 Service Call records, Mapillary street-view images, and OpenStreetMap (OSM) amenities---into a spatiotemporal variational Gaussian Process (ST-VGP) model for monitoring and predicting daily homeless tent trends. Using San Francisco as a case study, we leverage quarterly tent counts from the city’s Department of Emergency Management to calibrate and validate the model over the period from January 2016 to May 2024. Our results demonstrate that crowdsourced data provide fine-grained insights into the spatial and temporal dynamics of homelessness, thereby complementing the PIT counts. We find that tents dispersed from downtown to other parts of the city following aggressive crackdown policies in 2018, highlighting the unintended consequences of short-term enforcement measures. Moreover, urban amenities and structures such as banks, restaurants, bridges, and highway ramps are significantly associated with tent locations. These findings underscore the transformative potential of crowdsourced data for designing sustainable, data-driven homelessness interventions. By offering a cost-efficient and adaptive framework, our approach enables policymakers and service providers to allocate resources proactively, evaluate policy effectiveness, and move toward systemic solutions for ending homelessness. Homelessness Crowdsourced data Spatiotemporal modeling Variational Gaussian Processes Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 05 Mar, 2026 Read the published version in EPJ Data Science → Version 1 posted Editorial decision: Revision requested 03 Jan, 2026 Reviews received at journal 02 Jan, 2026 Reviewers agreed at journal 21 Nov, 2025 Reviews received at journal 05 Nov, 2025 Reviewers agreed at journal 03 Nov, 2025 Reviewers agreed at journal 12 Oct, 2025 Reviewers invited by journal 01 Oct, 2025 Editor assigned by journal 01 Oct, 2025 Submission checks completed at journal 30 Sep, 2025 First submitted to journal 30 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. 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-7754821","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":528642566,"identity":"44432944-a06f-4942-9b89-5f44f02bd936","order_by":0,"name":"Wooyong Jung","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArUlEQVRIiWNgGAWjYFCCA0BcAWFKkKDlDGlagICxjRQtBgfPmD38Ou+OvcEB5oO3eYjScuCMubHstmeJGw6wJVsTpcXswBkzaclthxMMDvCYSZOgZc5hoMP4vxGvRfJjw2HGDQd42IjTYn/gWJk0w7FniTMPsxlbziFGi+SMw9skf9Tcsec73vzwxhtitDBIHGBg5gHFJzNRykGAv4GB8Qc4CYyCUTAKRsEowAEARrI20qZjAZUAAAAASUVORK5CYII=","orcid":"","institution":"Pennsylvania State University","correspondingAuthor":true,"prefix":"","firstName":"Wooyong","middleName":"","lastName":"Jung","suffix":""},{"id":528642568,"identity":"61480872-d62c-4b72-8787-b9e3f27a2c78","order_by":1,"name":"Sola Kim","email":"","orcid":"","institution":"Arizona State University","correspondingAuthor":false,"prefix":"","firstName":"Sola","middleName":"","lastName":"Kim","suffix":""},{"id":528642569,"identity":"32fa15d9-6b99-4d13-a32c-6bd71eb047c8","order_by":2,"name":"Dongwook Kim","email":"","orcid":"","institution":"Arizona State University","correspondingAuthor":false,"prefix":"","firstName":"Dongwook","middleName":"","lastName":"Kim","suffix":""},{"id":528642570,"identity":"a37688e9-ba48-40ed-983a-76bf6a9d98cd","order_by":3,"name":"Andre Sihombing","email":"","orcid":"","institution":"Pennsylvania State University","correspondingAuthor":false,"prefix":"","firstName":"Andre","middleName":"","lastName":"Sihombing","suffix":""},{"id":528642571,"identity":"b2c978c5-d32b-4312-bbb1-329089a48d83","order_by":4,"name":"Maryam Tabar","email":"","orcid":"","institution":"The University of Texas at San Antonio","correspondingAuthor":false,"prefix":"","firstName":"Maryam","middleName":"","lastName":"Tabar","suffix":""},{"id":528642572,"identity":"dd452996-884b-4020-adb7-930dae65fcf7","order_by":5,"name":"Dongwon Lee","email":"","orcid":"","institution":"Pennsylvania State University","correspondingAuthor":false,"prefix":"","firstName":"Dongwon","middleName":"","lastName":"Lee","suffix":""}],"badges":[],"createdAt":"2025-09-30 20:53:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7754821/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7754821/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1140/epjds/s13688-026-00631-8","type":"published","date":"2026-03-05T15:57:26+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":93445206,"identity":"ede9e996-cdc2-4043-b351-f4f89ec7f09f","added_by":"auto","created_at":"2025-10-14 01:23:47","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7665,"visible":true,"origin":"","legend":"","description":"","filename":"3f3204f784794ba3b2e96796d7726d78.json","url":"https://assets-eu.researchsquare.com/files/rs-7754821/v1/c5e6b73d413f66c7ffb8e2d3.json"},{"id":104250656,"identity":"92b371aa-f24c-46c7-af62-3a582250eaca","added_by":"auto","created_at":"2026-03-09 16:04:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3514733,"visible":true,"origin":"","legend":"","description":"","filename":"EPJDataScienceHomelessnessStudy.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7754821/v1_covered_c255617e-e004-4a50-a771-55b0731dfcc3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"From Crowdsourced Data to Policy Design: Monitoring and Forecasting Homeless Tents","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":"epj-data-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"epds","sideBox":"Learn more about [EPJ Data Science](https://epjdatascience.springeropen.com/)","snPcode":"13688","submissionUrl":"https://submission.springernature.com/new-submission/13688/3","title":"EPJ Data Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Homelessness, Crowdsourced data, Spatiotemporal modeling, Variational Gaussian Processes","lastPublishedDoi":"10.21203/rs.3.rs-7754821/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7754821/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Homelessness in the United States remains a persistent and growing crisis, further complicated by a shortage of high-quality, high-resolution data. The existing Point-in-Time (PIT) count data has several limitations, as it relies on single-night counts with inconsistent coverage and cannot capture the dynamic and seasonal nature of unsheltered homelessness. To address this gap, we integrate multiple crowdsourced data sources---including 311 Service Call records, Mapillary street-view images, and OpenStreetMap (OSM) amenities---into a spatiotemporal variational Gaussian Process (ST-VGP) model for monitoring and predicting daily homeless tent trends. Using San Francisco as a case study, we leverage quarterly tent counts from the city’s Department of Emergency Management to calibrate and validate the model over the period from January 2016 to May 2024. Our results demonstrate that crowdsourced data provide fine-grained insights into the spatial and temporal dynamics of homelessness, thereby complementing the PIT counts. We find that tents dispersed from downtown to other parts of the city following aggressive crackdown policies in 2018, highlighting the unintended consequences of short-term enforcement measures. Moreover, urban amenities and structures such as banks, restaurants, bridges, and highway ramps are significantly associated with tent locations. These findings underscore the transformative potential of crowdsourced data for designing sustainable, data-driven homelessness interventions. By offering a cost-efficient and adaptive framework, our approach enables policymakers and service providers to allocate resources proactively, evaluate policy effectiveness, and move toward systemic solutions for ending homelessness.","manuscriptTitle":"From Crowdsourced Data to Policy Design: Monitoring and Forecasting Homeless Tents","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-14 01:23:34","doi":"10.21203/rs.3.rs-7754821/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-03T14:43:01+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-03T04:02:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"243178825001696828617821644760758916211","date":"2025-11-21T14:56:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-05T16:44:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"289535592971644227062582415301217041249","date":"2025-11-03T16:06:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"166101329652516255001276183025183113561","date":"2025-10-12T14:40:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-01T10:12:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-01T08:59:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-01T03:44:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"EPJ Data Science","date":"2025-09-30T20:40:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"epj-data-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"epds","sideBox":"Learn more about [EPJ Data Science](https://epjdatascience.springeropen.com/)","snPcode":"13688","submissionUrl":"https://submission.springernature.com/new-submission/13688/3","title":"EPJ Data Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"04634bfa-e9ab-44b3-abe5-f4a268464879","owner":[],"postedDate":"October 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-09T16:01:33+00:00","versionOfRecord":{"articleIdentity":"rs-7754821","link":"https://doi.org/10.1140/epjds/s13688-026-00631-8","journal":{"identity":"epj-data-science","isVorOnly":false,"title":"EPJ Data Science"},"publishedOn":"2026-03-05 15:57:26","publishedOnDateReadable":"March 5th, 2026"},"versionCreatedAt":"2025-10-14 01:23:34","video":"","vorDoi":"10.1140/epjds/s13688-026-00631-8","vorDoiUrl":"https://doi.org/10.1140/epjds/s13688-026-00631-8","workflowStages":[]},"version":"v1","identity":"rs-7754821","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7754821","identity":"rs-7754821","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}