Abstract
The COVID-19 pandemic has profoundly transformed daily life and mobility behaviors, thereby creating an urgent need to understand these shifts. This study examines the spatial and temporal patterns of human mobility during the pandemic, with a focus on how these patterns vary across five East Asian countries and regions: Mongolia, Japan, Republic of Korea, Hong Kong, and Taiwan (China). By analysing Community Mobility Report and employing advanced analytical methods such as Gradient Boosting Machines and changepoints detection, the research identifies distinct adaptive behaviors in response to the pandemic. The findings reveal variations in the speed and nature of mobility adaptation across categories, such as retail, residential, and transit. While Mongolia exhibited relatively stable mobility patterns, Taiwan (China), Hong Kong, Republic of Korea, and Japan demonstrated notable adaptive responses. Furthermore, the study highlights the socioeconomic implications of mobility changes, providing insights into economic resilience and behavioral adaptation during health crises. These findings offer valuable evidence to inform public health strategies and economic recovery plans.
Similar content being viewed by others
Data availability
The data are available from the corresponding author upon reasonable request.
References
Aktay A, Bavadekar S, Cossoul G, Davis J, Desfontaines D, Fabrikant A, … & Wilson RJ (2020) Google COVID-19 community mobility reports: anonymization process description (version 1.1). https://doi.org/10.48550/arXiv.2004.04145
Bavel JJV, Baicker K, Boggio PS, Capraro V, Cichocka A, Cikara M, Willer R (2020) Using social and behavioural science to support COVID-19 pandemic response. Nat Hum Behav 4(5):460–471. https://doi.org/10.1038/s41562-020-0884-z
Bamney A, Jashami H, Sonduru Pantangi S, Ambabo J, Megat-Johari MU, Cai Q, Savolainen PT (2023) Examining impacts of COVID-19-related stay-at-home orders through a two-way random effects model. Transport Res Rec 2677(4):255–266
Bonaccorsi G, Pierri F, Cinelli M, Flori A, Galeazzi A, Porcelli F, Pammolli F (2020) Economic and social consequences of human mobility restrictions under COVID-19. Proc Natl Acad Sci 117(27):15530–15535. https://doi.org/10.1073/pnas.2007658117
Bryant P, Elofsson A (2020) Estimating the impact of mobility patterns on COVID-19 infection rates in 11 European countries. PeerJ 8: e9879. https://doi.org/10.7717/peerj.9879
Cao L, Liu Q (2024) COVID-19 modeling: a review. ACM Comput Surv 57(1):1–42. https://doi.org/10.1145/3686150
Champlin C, Sirenko M, Comes T (2023) Measuring social resilience in cities: An exploratory spatio-temporal analysis of activity routines in urban spaces during Covid-19. Cities 135: 104220. https://doi.org/10.1016/j.cities.2023.104220
Chapin C, Roy SS (2021) A spatial web application to explore the interactions between human mobility, government policies, and COVID-19 cases. J Geovisualization Spat Anal 5:1–8. https://doi.org/10.1007/s41651-021-00081-y
Chen S, Ding F, Buil-Gil D, Hao M, Maystadt JF, Fu J, Jiang D (2024) The impact of COVID-19 lockdown on fraud in the UK. Humanit Soc Sci Commun 11(1):1–11. https://doi.org/10.1057/s41599-024-04201-z
Chen Y, Chen M, Huang B, Wu C, Shi W (2021) Modeling the spatiotemporal association between COVID-19 transmission and population mobility using geographically and temporally weighted regression. GeoHealth 5(5):e2021GH000402. https://doi.org/10.1029/2021GH000402
Dehning J, Zierenberg J, Spitzner FP, Wibral M, Neto JP, Wilczek M, Priesemann V (2020) Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions. Science 369(6500):eabb9789. https://doi.org/10.1126/science.abb9789
Doornik JA, Castle JL, Hendry DF (2021) Modeling and forecasting the COVID-19 pandemic time-series data. Soc Sci Q 102(5):2070–2087. https://doi.org/10.1111/ssqu.13008
Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Annals of Statistics, 1189-1232
Fumagalli LAW, Rezende DA, Guimarães TA (2021) Challenges for public transportation: Consequences and possible alternatives for the Covid-19 pandemic through strategic digital city application. J Urban Manag 10(2):97–109. https://doi.org/10.1016/j.jum.2021.04.002
Golledge, RG (1997) Spatial behavior: A geographic perspective. Guilford Press
Gu H, Shen J, Chu J (2023) Understanding intercity mobility patterns in rapidly urbanizing China, 2015–2019: evidence from longitudinal poisson gravity modeling. Ann Am Assoc Geographers 113(1):307–330. https://doi.org/10.1080/24694452.2022.2097050
Gu H, Lin Y, Hu H, Yu H (2025) COVID-19 pandemic and road infrastructure exerted stage-dependent spatiotemporal influences on inter-city road travel in China. Humanit Soc Sci Commun 12(1):1–13. https://doi.org/10.1057/s41599-025-05018-0
Hajlasz M, Pei S (2024) Predictability of human mobility during the COVID-19 pandemic in the United States. PNAS nexus 3(8):308. https://doi.org/10.1093/pnasnexus/pgae308
Haldane V, De Foo C, Abdalla SM, Jung AS, Tan M, Wu S, Legido-Quigley H (2021) Health systems resilience in managing the COVID-19 pandemic: lessons from 28 countries. Nat Med 27(6):964–980. https://doi.org/10.1038/s41591-021-01381-y
Hamilton M, Fischer AP, Guikema SD, Keppel-Aleks G (2018) Behavioral adaptation to climate change in wildfire-prone forests. Wiley Interdiscip Rev: Clim Change 9(6):e553. https://doi.org/10.1002/wcc.553
Hasselwander M, Tamagusko T, Bigotte JF, Ferreira A, Mejia A, Ferranti EJ (2021) Building back better: The COVID-19 pandemic and transport policy implications for a developing megacity. Sustain Cities Soc 69: 102864. https://doi.org/10.1016/j.scs.2021.102864
Hayakawa K, Mukunoki H, Urata S (2023) Can e-commerce mitigate the negative impact of COVID-19 on international trade? Jpn Econ Rev 74(2):215–232. https://doi.org/10.1007/s42973-021-00099-3
Hinkley, DV (1970). Inference about the change-point in a sequence of random variables, 1-17. https://doi.org/10.1093/biomet/57.1.1
Huang Z, Ling X, Wang P, Zhang F, Mao Y, Lin T, Wang FY (2018) Modeling real-time human mobility based on mobile phone and transportation data fusion. Transportation Res part C: Emerg Technol 96:251–269. https://doi.org/10.1016/j.trc.2018.09.016
Huang X, Li Z, Jiang Y, Ye X, Deng C, Zhang J, Li X (2021) The characteristics of multi-source mobility datasets and how they reveal the luxury nature of social distancing in the US during the COVID-19 pandemic. Int J Digital Earth 14(4):424–442. https://doi.org/10.1080/17538947.2021.1886358
Hu S, Xiong C, Yang M, Younes H, Luo W, Zhang L (2021) A big-data driven approach to analyzing and modeling human mobility trend under non-pharmaceutical interventions during COVID-19 pandemic. Transportation Res Part C: Emerg Technol 124: 102955. https://doi.org/10.1016/j.trc.2020.102955
Hu S, Xiong C, Younes H, Yang M, Darzi A, Jin ZC (2022) Examining spatiotemporal evolution of racial/ethnic disparities in human mobility and COVID-19 health outcomes: Evidence from the contiguous United States. Sustain Cities Soc 76: 103506. https://doi.org/10.1016/j.scs.2021.103506
Jin H, Li X, Huang Y, Yang C, Armoogum S, Xiong N, Wu W (2024) The interplay of time and space in human behavior: a sociological perspective on the TSCH model. Humanit Soc Sci Commun 11(1):1–17. https://doi.org/10.1057/s41599-024-04274-w
Kan Z, Kwan MP, Huang J, Wong MS, Liu D (2021) Comparing the space-time patterns of high-risk areas in different waves of COVID-19 in Hong Kong. Trans GIS 25(6):2982–3001. https://doi.org/10.1111/tgis.12800
Killick R, Eckley I (2014) Changepoint: An R package for changepoint analysis. J Stat Softw 58(3):1–19. https://doi.org/10.18637/jss.v058.i03
Kraemer MU, Yang CH, Gutierrez B, Wu CH, Klein B, Pigott DM, Scarpino SV (2020) The effect of human mobility and control measures on the COVID-19 epidemic in China. Science 368(6490):493–497. https://doi.org/10.1126/science.abb4218
Long Y, Han H, Tu Y, Shu X (2015) Evaluating the effectiveness of urban growth boundaries using human mobility and activity records. Cities 46:76–84. https://doi.org/10.1016/j.cities.2015.05.001
Nanda RO, Nursetyo AA, Ramadona AL, Imron MA, Fuad A, Setyawan A, Ahmad RA (2022) Community mobility and COVID-19 dynamics in Jakarta, Indonesia. Int J Environ Res Public Health 19(11):6671. https://doi.org/10.3390/ijerph19116671
Okamoto S (2022) State of emergency and human mobility during the COVID-19 pandemic in Japan. J Transp Health 26: 101405. https://doi.org/10.1016/j.jth.2022.101405
Osman M (2010) Controlling uncertainty: a review of human behavior in complex dynamic environments. Psychol Bull 136(1):65. https://doi.org/10.1037/a0017815
Panik RT, Watkins K, Ederer D (2023) Metrics of mobility: assessing the impact of COVID-19 on travel behavior. Transportation Res Rec 2677(4):583–596. https://doi.org/10.1177/03611981221131812
Pavlović T, Azevedo F, De K, Riaño-Moreno JC, Maglić M, Gkinopoulos T, Keudel O (2022) Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning. PNAS nexus 1(3):pgac093. https://doi.org/10.1093/pnasnexus/pgac093
Pullano G, Valdano E, Scarpa N, Rubrichi S, Colizza V (2020) Evaluating the effect of demographic factors, socioeconomic factors, and risk aversion on mobility during the COVID-19 epidemic in France under lockdown: a population-based study. Lancet Digital Health 2(12):e638–e649. https://doi.org/10.1016/S2589-7500(20)30243-0
Rachman I (2025) Cognitive and behavioral adaptation strategies in learning financial derivatives: a qualitative study on undergraduate students. Daengku: J Humanities Soc Sci Innov 5(2):310–319. https://doi.org/10.35877/454RI.daengku3892
Rafiq R, McNally MG, Uddin YS, Ahmed T (2022) Impact of working from home on activity-travel behavior during the COVID-19 Pandemic: an aggregate structural analysis. Transportation Res Part A: Policy Pract 159:35–54. https://doi.org/10.1016/j.tra.2022.03.003
Rowe F, Calafiore A, Arribas-Bel D, Samardzhiev K, Fleischmann M (2023) Urban exodus? Understanding human mobility in Britain during the COVID-19 pandemic using Meta-Facebook data. Popul, Space Place 29(1):e2637. https://doi.org/10.1002/psp.2637
Sih A, Ferrari MC, Harris DJ (2011) Evolution and behavioural responses to human-induced rapid environmental change. Evolut Appl 4(2):367–387. https://doi.org/10.1111/j.1752-4571.2010.00166.x
Sun X, Wei Y, & Song W (2024) Tracking global changes of human mobility behavior and resilience during the COVID-19 pandemic. Papers in Applied Geography, 1-17. https://doi.org/10.1080/23754931.2024.2361236
Tirachini A, Cats O (2020) COVID-19 and public transportation: Current assessment, prospects, and research needs. J Public Transportation 22(1):1–21. https://doi.org/10.5038/2375-0901.22.1.1
Vanhove MP, Thys S, Decaestecker E, Antoine-Moussiaux N, De Man J, Hugé J, Janssens de Bisthoven L (2021) Global change increases zoonotic risk, COVID-19 changes risk perceptions: a plea for urban nature connectedness. Cities health 5(sup1):S131–S139. https://doi.org/10.1080/23748834.2020.1805282
Verschuur J, Koks EE, Hall JW (2021) Observed impacts of the COVID-19 pandemic on global trade. Nat Hum Behav 5(3):305–307. https://doi.org/10.1038/s41562-020-0896-8
Wang A, Zhang A, Chan EH, Shi W, Zhou X, Liu Z (2020) A review of human mobility research based on big data and its implication for smart city development. ISPRS Int J Geo-Inf 10(1):13. https://doi.org/10.3390/ijgi10010013
Weitz JS, Park SW, Eksin C, Dushoff J (2020) Awareness-driven behavior changes can shift the shape of epidemics away from peaks and toward plateaus, shoulders, and oscillations. Proc Natl Acad Sci USA 117(51):32764–32771. https://doi.org/10.1073/pnas.2009911117
Xu F, Wang Q, Moro E, Chen L, Salazar Miranda A, González MC, … & Evans J (2025). Using human mobility data to quantify experienced urban inequalities. Nature Human Behaviour, 1-11. https://doi.org/10.1038/s41562-024-02079-0
Yang M, Han C, Cui Y, Zhao Y (2021) COVID-19 and mobility in tourism cities: A statistical change-point detection approach. J Hospitality Tour Manag 47:256–261. https://doi.org/10.1016/j.jhtm.2021.03.014
Yang Z (2024) How east Asian culture influences the personality traits of its residents: an exploration of cultural characteristics and the emphasis on interdependence. Commun HumanitRes 31:44–50. https://doi.org/10.54254/2753-7064/31/20231831
Yeung HWC (2022) Explaining geographic shifts of chip making toward East Asia and market dynamics in semiconductor global production networks. Econ Geogr 98(3):272–298. https://doi.org/10.1080/00130095.2021.2019010
Yu Z, Liu X (2023) Spatial variations of the third and fourth COVID-19 waves in Hong Kong: a comparative study using built environment and socio-demographic characteristics. Environ Plan B Urban Analytics City Sci 50(5):1144–1160. https://doi.org/10.1177/23998083221107019
Yu S, Mückschel M, Hoffmann S, Bluschke A, Pscherer C, Beste C (2023) The neural stability of perception–motor representations affects action outcomes and behavioral adaptation. Psychophysiology 60(1):e14146. https://doi.org/10.1111/psyp.14146
Zabaniotou A (2020) A systemic approach to resilience and ecological sustainability during the COVID-19 pandemic: Human, societal, and ecological health as a system-wide emergent property in the Anthropocene. Glob Transit 2:116–126. https://doi.org/10.1016/j.glt.2020.06.002
Zbezhkhovska U, Chumachenko D (2025) Smoothing techniques for improving COVID-19 time series forecasting across countries. Computation 13(6):136. https://doi.org/10.3390/computation13060136
Zhao P, Liu Q, Ma T, Kang T, Zhou Z, Liu Z, Wan J (2023) Policy instruments facilitate China’s COVID-19 work resumption. Proc Natl Acad Sci 120(41):e2305692120. https://doi.org/10.1073/pnas.2305692120
Zheng Z, Xie Z, Qin Y, Wang K, Yu Y, Fu P (2021) Exploring the influence of human mobility factors and spread prediction on early COVID-19 in the USA. BMC Public Health 21:1–13. https://doi.org/10.1186/s12889-021-10682-3
Zheng Y, Yue K, Wong EW, Yuan HY (2025) Impact of human mobility and weather conditions on Dengue mosquito abundance during the COVID-19 pandemic in Hong Kong. Infect Dis Model 10(3):840–849. https://doi.org/10.1016/j.idm.2025.04.002
Acknowledgements
This study was funded by National Natural Science Foundation of China [grant number 42571232, 41971202] and the Education Department of Jilin Province [grant number JJKH20250320BS].
Author information
Authors and Affiliations
Contributions
X.S. conceived the study, developed the methodology, performed the formal analysis and investigation, and wrote the original draft. W.S. contributed to conceptualization, provided resources, supervised the research, and reviewed and edited the manuscript. Y.W. acquired funding and supervised the project. All authors reviewed and approved the final manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Ethical approval
This article does not contain any studies with human participants performed by any of the authors.
Informed consent
This article does not contain any studies with human participants performed by any of the authors.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Sun, X., Song, W. & Wei, Y. Adapting mobility: insights from COVID-19 impact on east asian regions. Humanit Soc Sci Commun (2026). https://doi.org/10.1057/s41599-026-06662-w
Received:
Accepted:
Published:
DOI: https://doi.org/10.1057/s41599-026-06662-w
