Abstract
In this study, we investigate the potential driving factors that lead to the disparity in the time-series of home dwell time in a data-driven manner, aiming to provide fundamental knowledge that benefits policy-making for better mitigation strategies of future pandemics. Taking Metro Atlanta as a study case, we perform a trend-driven analysis by conducting Kmeans time-series clustering using fine-grained home dwell time records from SafeGraph. Furthermore, we apply ANOVA (Analysis of Variance) coupled with post-hoc Tukey’s test to assess the statistical difference in sixteen recoded demographic/socioeconomic variables (from ACS 2014–2018 estimates) among the identified time-series clusters. We find that demographic/socioeconomic variables can explain the disparity in home dwell time in response to the stay-at-home order, which potentially leads to disparate exposures to the risk from the COVID-19. The results further suggest that socially disadvantaged groups are less likely to follow the order to stay at home, pointing out the extensive gaps in the effectiveness of social distancing measures that exist between socially disadvantaged groups and others. Our study reveals that the long-standing inequity issue in the U.S. stands in the way of the effective implementation of social distancing measures.
Highlights
The coronavirus disease 2019 (COVID-19) is a global threat that raises worldwide concerns with escalating economic, social, and health challenges
Geo-Inf. 2020, 9, 675 and using the Atlanta-Sandy Springs-Roswell metropolitan statistical area (MSA) as a study case, this study investigates the potential driving factors that lead to the disparity in the time-series of home dwell times during the COVID-19 pandemic, providing fundamental knowledge that benefits policy-making for better mitigation measures in future pandemics
This study categorizes the time-series of home dwell time records during the COVID-19 pandemic, and further explores what demographic/socioeconomic variables differ among the categories with statistical significance
Summary
The coronavirus disease 2019 (COVID-19) is a global threat that raises worldwide concerns with escalating economic, social, and health challenges. In response to the threat of COVID-19, social distancing measures are one of the primary tools to reduce the transmission of the SARS-CoV-2 virus. National and local governments have promoted stay-at-home orders while requiring non-essential business closures to reduce the risk of transmission by further enhancing social distancing measures [4]. In the U.S, many states, counties, and cities began issuing stay-at-home or similar mitigation measures that require residents to reduce movement and stay home as early as March 2020, which has lead to a considerable increase in home dwell time. Despite widely adopted stay-at-home orders, there was mounting evidence of the disparate responses that potentially leave vulnerable populations unequally exposed to the COVID-19 pandemic [10,11]. Identifying demographic and socioeconomic variables that potentially drive the disparity in the implementation of stay-at-home orders deserves much attention
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