Original Articles
23 June 2025
Vol. 20 No. 1 (2025)
Spatio-temporal analysis of foot traffic dynamics in Charleston County, South Carolina: before, during, and after COVID-19
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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While the COVID-19 pandemic significantly disrupted urban mobility in general, its effects on spatio-temporal foot traffic patterns remain insufficiently explored. This study addresses this issue by analysing foot traffic dynamics across various regions of Charleston County, South Carolina, before, during and after the pandemic. We examined changes across nine distinct stages of the pandemic from 2018 to 2022 at the sub-county level, utilizing point of interest data and public health records. Various machine learning models, including Random Forest, were employed to predict foot traffic trends, achieving high predictive accuracy with an R2 value of 0.88. Our findings reveal varying foot traffic patterns across the county. Prior to the pandemic, foot traffic was generally consistent across county subdivisions, maintaining steady levels in each area. The onset of the pandemic led to significant decreases in foot traffic across most subdivisions, followed by gradual recovery, with some areas surpassing pre-pandemic levels. These results underscore the need for tailored crisis management and urban planning, particularly in midsized counties with similar structures to inform more effective resource allocation and improve risk management in public safety during public health crises.
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Allard A, Moore C, Scarpino SV, Althouse BM, Hébert-Dufresne L, 2023. The role of directionality, heterogeneity, and correlations in epidemic risk and spread. SIAM Rev 65:471-92. DOI: https://doi.org/10.1137/20M1383811
Angel A, Cohen A, Dalyot S, Plaut P, 2023. Impact of COVID-19 policies on pedestrian traffic and walking patterns. Environ Plan B: Urban Anal City Sci 50:1178-93. DOI: https://doi.org/10.1177/23998083221113332
Arambepola R, Schaber KL, Schluth C, Huang AT, Labrique AB, Mehta SH, Solomon SS, Cummings DA, Wesolowski A, 2023. Fine scale human mobility changes within 26 US cities in 2020 in response to the COVID-19 pandemic were associated with distance and income. PLOS Glob Public Health 3:e0002151. DOI: https://doi.org/10.1371/journal.pgph.0002151
Bartsch SM, Ferguson MC, McKinnell JA, O'Shea KJ, Wedlock PT, Siegmund SS, Lee BY, 2020. The potential health care costs and resource use associated with COVID-19 in the United States. Health Aff 39:927-935. DOI: https://doi.org/10.1377/hlthaff.2020.00426
Bavel JJV, Baicker K, Boggio PS, Capraro V, Cichocka A, Cikara M, Crockett MJ, Crum AJ, Douglas KM, Druckman JN, 2020. Using social and behavioural science to support COVID-19 pandemic response. Nat Hum Behav 4:460-71. DOI: https://doi.org/10.1038/s41562-020-0884-z
Bonaccorsi G, Pierri F, Cinelli M, Flori A, Galeazzi A, Porcelli F, Schmidt AL, Valensise CM, Scala A, Quattrociocchi W, 2020. Economic and social consequences of human mobility restrictions under COVID-19. Proc Natl Acad Sci USA 117:15530-35. DOI: https://doi.org/10.1073/pnas.2007658117
Centers for Disease Control and Prevention, 2021. COVID-19 pandemic planning scenarios. U.S. Department of Health and Human Services. https://stacks.cdc.gov/view/cdc/106864.
Chang S, Pierson E, Koh PW, Gerardin J, Redbird B, Grusky D, Leskovec J, 2021. Mobility network models of COVID-19 explain inequities and inform reopening. Nature 589:82-7. DOI: https://doi.org/10.1038/s41586-020-2923-3
Charleston County Development, 2023. Charleston County development. Available from: https://www.charlestoncountydevelopment.org/
City of Charleston, 2023. Coronavirus updates. Available from: https://www.charleston-sc.gov/2408/city-of-charleston-coronavirus-updates Accessed in July 2023
Coston A, Kearns M, Morgenstern J, Dwork C, 2021. Leveraging administrative data for bias audits: Assessing disparate coverage in the SafeGraph dataset. FAccT'21:32-44.
DeFilippis E, Impink SM, Singell M, Polzer JT, Sadun R, 2020. Collaborating during coronavirus: The impact of COVID-19 on the nature of work. Natl Bur Econ Res Work Pap No. w27612. DOI: https://doi.org/10.3386/w27612
Deng H, Aldrich DP, Danziger MM, Donges JF, 2021. High-resolution human mobility data reveal race and wealth disparities in disaster evacuation patterns. Humanit Soc Sci Commun 8:144. DOI: https://doi.org/10.1057/s41599-021-00824-8
Elarde J, Kim JS, Kavak H, Züfle A, Anderson T, 2021. Change of human mobility during COVID-19: A United States case study. PLOS One 16:e0259031. DOI: https://doi.org/10.1371/journal.pone.0259031
Gao S, Rao J, Kang Y, Liang Y, Kruse J, 2020. Mapping county-level mobility pattern changes in the United States in response to COVID-19. SIGSPATIAL Spec 12:16-26. DOI: https://doi.org/10.1145/3404820.3404824
Google LLC, 2020. Google COVID-19 community mobility reports. Available from: https://www.google.com/covid19/mobility/
Guo J, Deng C, Gu F, 2022. Vaccinations, mobility and COVID-19 transmission. Int J Envirol Res Public Health 19:97. DOI: https://doi.org/10.3390/ijerph19010097
Gursoy D, Chi CG, 2020. Effects of COVID-19 pandemic on hospitality industry: Review of the current situations and a research agenda. J Hosp Mark Manag 29:527-29. DOI: https://doi.org/10.1080/19368623.2020.1788231
Hsiang S, Allen D, Annan-Phan S, Bell K, Bolliger I, Chong T, Druckenmiller H, Huang LY, Hultgren A, Krasovich E, 2020. The effect of large-scale anti-contagion policies on the COVID-19 pandemic. Nature 584:262-7. DOI: https://doi.org/10.1038/s41586-020-2404-8
Huang Z, 2021. Spatiotemporal evolution patterns of the COVID-19 pandemic using space-time aggregation and spatial statistics: A global perspective. ISPRS Int J Geo-Inf 10:519. DOI: https://doi.org/10.3390/ijgi10080519
Li Z, Xu W, Zhang C, Yang Y, Li Q, 2023. A multi-year assessment of the sampling bias in SafeGraph mobility data. PLoS One 18:e0294430. DOI: https://doi.org/10.1371/journal.pone.0294430
Liang LL, Le HM, Wu CY, Sher CY, McGuire A, 2023. Human mobility increased with vaccine coverage and attenuated the protection of COVID-19 vaccination: A longitudinal study of 107 countries. J Glob Health 13:06009. DOI: https://doi.org/10.7189/jogh.13.06009
Longley PA, Goodchild MF, Maguire DJ, Rhind DW, 2015. Geographic information systems and science. 4th ed. Wiley.
Noland RB, Iacobucci E, Zhang W, 2023. Public views on the reallocation of street space due to COVID-19. J Am Plann Assoc 89:93-106. DOI: https://doi.org/10.1080/01944363.2022.2058595
Pan A, Liu L, Wang C, Guo H, Hao X, Wang Q, Huang J, He N, Yu H, Lin X, 2020. Association of public health interventions with the epidemiology of the COVID-19 outbreak in Wuhan, China. JAMA 323:1915-23. DOI: https://doi.org/10.1001/jama.2020.6130
Pan Y, Darzi A, Kabiri A, Zhao G, Luo W, Xiong C, Zhang L, 2020. Quantifying human mobility behaviour changes during the COVID-19 outbreak in the United States. Sci Rep 10:20742. DOI: https://doi.org/10.1038/s41598-020-77751-2
Pepe E, Bajardi P, Gauvin L, Privitera F, Lake B, Cattuto C, Tizzoni M, 2020. COVID-19 outbreak response, a dataset to assess mobility changes in Italy following national lockdown. Sci Data 7:230. DOI: https://doi.org/10.1038/s41597-020-00575-2
Roy A, Sattiraju S, Bhowmick A, Das A, 2021. Characterizing the spread of COVID-19 from human mobility patterns and socio-demographic indicators. Proc 27th ACM SIGKDD Conf Knowl Discov Data Min 2679:3084–92.
SafeGraph, n.d. SafeGraph: Foot traffic and location data insights for physical places. Available from: https://www.safegraph.com/
South Carolina Department of Health and Environmental Control, 2023a. COVID-19 vaccination plan for South Carolina. Available from https://aging.sc.gov/vaccine
South Carolina Department of Health and Environmental Control, 2023b. DHEC news releases for COVID-19. Available from: https://scdhec.gov/covid19/dhec-news-releases-information-videos-covid-19 Accessed in June 2023
Stasi C, Fallani S, Voller F, Silvestri C, 2020. Treatment for COVID-19: An overview. Eur J Pharmacol 889:173644. DOI: https://doi.org/10.1016/j.ejphar.2020.173644
Tegally H, Khan K, Huber C, De Oliveira T, Kraemer MU, 2022. Shifts in global mobility dictate the synchrony of SARS-CoV-2 epidemic waves. J Travel Med 29:taac134. DOI: https://doi.org/10.1093/jtm/taac134
Tonekaboni NH, Ramaswamy L, Sachdev S, 2019. A mobile and web-based approach for targeted and proactive participatory sensing. 15th EAI International Conference, CollaborateCom 2019, London, UK, Proceedings 15. Springer: 215-230. DOI: https://doi.org/10.1007/978-3-030-30146-0_15
U.S. Census Bureau, 2022. U.S. Census Bureau QuickFacts: Charleston County, South Carolina. Available from: https://www.census.gov/quickfacts/fact/table/charlestoncountysouthcarolina/POP010220
Warren MS, Skillman SW, 2020. Mobility changes in response to COVID-19. arXiv:2003.14228.
World Health Organization, 2022. COVID-19 weekly epidemiological update. Available from: https://www.who.int
World Health Organization, 2023. A checklist for respiratory pathogen pandemic preparedness planning. Available from: https://www.who.int/publications/i/item/9789240084513
How to Cite
Spatio-temporal analysis of foot traffic dynamics in Charleston County, South Carolina: before, during, and after COVID-19. (2025). Geospatial Health, 20(1). https://doi.org/10.4081/gh.2025.1363
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