Complex Network Analysis of Mobility Dynamics in Seoul During the COVID-19 Pandemic (2020–2022)

Complex Network Analysis of Mobility Dynamics in Seoul During the COVID-19 Pandemic (2020–2022) | 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 Complex Network Analysis of Mobility Dynamics in Seoul During the COVID-19 Pandemic (2020–2022) Zeyu Hu, Manjun Yu, Youngook Jang, Sourya Shrestha, Jaehun Jung, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6792793/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Background : Recent advances in geolocated data enable quantitative analysis of human mobility, critical for urban planning and epidemic control. This study examines mobility dynamics in Seoul during the COVID-19 pandemic, where minimal lockdown measures allowed relatively natural movement patterns, offering insights for managing viral spread with reduced socioeconomic disruption. Methods : We analyzed the Seoul "Living Mobility" dataset (January 2020–December 2022), which contains monthly origin-to-destination flows among 424 sub-districts (“dong”) in 25 districts (“gu”). We constructed a directed and weighted mobility network based on monthly trips between every pair of dongs. We assessed (i) local node intensity via weighted density within and across districts, (ii) global connectivity through distribution of node strengths, (iii) node clusters via community detection with the Infomap community-detection algorithm, and (iv) the top and bottom 10 hub nodes using weighted PageRank centrality. Results : Within-dong mobility was 24 times greater than cross-dong mobility, growing faster (28% vs. 14%) over the study period. Node strength distribution showed 138 high-mobility dongs (33%) driving 56% of total flows. Community detection identified 11 clusters transcending district boundaries, with within-community mobility 2–5 times higher than cross-community flows. High-mobility hubs, such as Yeoksam-dong (Gangnam-gu) and Yeoui-dong (Yeongdeungpo-gu), exhibited up to 70 times more movement than peripheral areas. Conclusion : Seoul’s mobility network remained fully connected yet highly heterogeneous during the COVID-19 pandemic years, with central hubs and clusters dominating movement. The rising saturation of within-dong flows reflects intensified local interactions, highlighting the growing importance of clustering and localized hotspots in driving future transmission risks. Targeting interventions at the 138 high-mobility dongs—and at bridges between their communities—can optimize disease control while minimizing disruption. Low-mobility dongs highlight potential service access disparities, guiding tailored interventions for equitable urban planning and public health strategies. Health sciences/Diseases Health sciences/Health care Full Text Additional Declarations No competing interests reported. Supplementary Files appendix0608.docx Cite Share Download PDF Status: Published Journal Publication published 30 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 03 Sep, 2025 Reviews received at journal 18 Aug, 2025 Reviewers agreed at journal 30 Jul, 2025 Reviews received at journal 29 Jun, 2025 Reviewers agreed at journal 12 Jun, 2025 Reviewers agreed at journal 10 Jun, 2025 Reviewers invited by journal 10 Jun, 2025 Editor assigned by journal 10 Jun, 2025 Editor invited by journal 09 Jun, 2025 Submission checks completed at journal 06 Jun, 2025 First submitted to journal 31 May, 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-6792793","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":469681283,"identity":"762bc3bb-33c8-41f3-8f44-2d7e4675e358","order_by":0,"name":"Zeyu Hu","email":"","orcid":"","institution":"University of Connecticut","correspondingAuthor":false,"prefix":"","firstName":"Zeyu","middleName":"","lastName":"Hu","suffix":""},{"id":469681284,"identity":"c8e791a9-0c06-468f-820c-92cb4bd63037","order_by":1,"name":"Manjun Yu","email":"","orcid":"","institution":"University of 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Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6792793/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6792793/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Recent advances in geolocated data enable quantitative analysis of human mobility, critical for urban planning and epidemic control. This study examines mobility dynamics in Seoul during the COVID-19 pandemic, where minimal lockdown measures allowed relatively natural movement patterns, offering insights for managing viral spread with reduced socioeconomic disruption.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: We analyzed the Seoul \"Living Mobility\" dataset (January 2020–December 2022), which contains monthly origin-to-destination flows among 424 sub-districts (“dong”) in 25 districts (“gu”). We constructed a directed and weighted mobility network based on monthly trips between every pair of dongs. We assessed (i) local node intensity via weighted density within and across districts, (ii) global connectivity through distribution of node strengths, (iii) node clusters via community detection with the Infomap community-detection algorithm, and (iv) the top and bottom 10 hub nodes using weighted PageRank centrality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Within-dong mobility was 24 times greater than cross-dong mobility, growing faster (28% vs. 14%) over the study period. Node strength distribution showed 138 high-mobility dongs (33%) driving 56% of total flows. Community detection identified 11 clusters transcending district boundaries, with within-community mobility 2–5 times higher than cross-community flows. High-mobility hubs, such as Yeoksam-dong (Gangnam-gu) and Yeoui-dong (Yeongdeungpo-gu), exhibited up to 70 times more movement than peripheral areas.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: Seoul’s mobility network remained fully connected yet highly heterogeneous during the COVID-19 pandemic years, with central hubs and clusters dominating movement. The rising saturation of within-dong flows reflects intensified local interactions, highlighting the growing importance of clustering and localized hotspots in driving future transmission risks. Targeting interventions at the 138 high-mobility dongs—and at bridges between their communities—can optimize disease control while minimizing disruption. Low-mobility dongs highlight potential service access disparities, guiding tailored interventions for equitable urban planning and public health strategies.\u003c/p\u003e","manuscriptTitle":"Complex Network Analysis of Mobility Dynamics in Seoul During the COVID-19 Pandemic (2020–2022)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-12 14:41:52","doi":"10.21203/rs.3.rs-6792793/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-03T04:50:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-18T17:30:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"332137691257871199658961989548224540902","date":"2025-07-30T13:30:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-29T06:16:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"66581292719773489698590101361878365115","date":"2025-06-12T23:15:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"242567957324997994869340765304748773844","date":"2025-06-11T01:11:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-10T20:24:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-10T19:40:01+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-09T14:49:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-07T03:44:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-05-31T20:02:48+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":"a9fcfb31-71e9-4e79-8732-883e739b5cc5","owner":[],"postedDate":"June 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":49870357,"name":"Health sciences/Diseases"},{"id":49870358,"name":"Health sciences/Health care"}],"tags":[],"updatedAt":"2026-02-02T16:04:25+00:00","versionOfRecord":{"articleIdentity":"rs-6792793","link":"https://doi.org/10.1038/s41598-025-33655-7","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-01-30 15:58:53","publishedOnDateReadable":"January 30th, 2026"},"versionCreatedAt":"2025-06-12 14:41:52","video":"","vorDoi":"10.1038/s41598-025-33655-7","vorDoiUrl":"https://doi.org/10.1038/s41598-025-33655-7","workflowStages":[]},"version":"v1","identity":"rs-6792793","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6792793","identity":"rs-6792793","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}