Abstract
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. 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. From 2020 to 2022, within-gu weighted density increased by 21.7%, and across-gu density by 13.0%. 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. 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.
Data availability
All the processed data and code can be found at (https://github.com/UConn-Health-Disease-Modeling/SeoulMobilityNetwork).
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Funding
Grant of the project for Infectious Disease Medical Safety, funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HG22C0094) and Korea Health Technology R&D project through the Korea Health Industry Development Institute (KHIDI), funded by the National Institute of Infectious Diseases, National Institute of Health Republic of Korea (grant number: HD22C2045).
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conceptualization: Y.J., Z.H.data curation: Z.H., Y.J., S.S., Y.O.Jformal analysis: Z.H.funding acquisition: J.J.investigation: J.J., Y.J.methodology: Z.H., Y.J.project administration: Z.H., M.Y.resources, software: Z.H.supervision: Y.J., J.J.validation: Y.J.visualization: Z.H.writing – original draft: Z.H., M.Y., Y.J.writing – review & editing: Y.J., S.S., Y.O.J, J.J.
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Hu, Z., Yu, M., Jang, Y. et al. Complex network analysis of mobility dynamics in Seoul during the COVID-19 pandemic 2020–2022. Sci Rep (2026). https://doi.org/10.1038/s41598-025-33655-7
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DOI: https://doi.org/10.1038/s41598-025-33655-7
