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Article

Time-Series Clustering for Home Dwell Time during COVID-19: What Can We Learn from It?

1
Department of Geosciences, University of Arkansas, Fayetteville, AR 72701, USA
2
Department of Geography, University of South Carolina, Columbia, SC 29208, USA
3
School of Community Resources and Development, Arizona State University, Phoenix, AZ 85004, USA
4
Edward J. Bloustein School of Planning and Public Policy, Rutgers, The State University of New Jersey, New Brunswick, NJ 08901, USA
5
Department of City and Regional Planning, Cornell University, Ithaca, NY 14850, USA
6
Department of Computer Science and Engineering, University of Michigan, Ann Arbor, MI 48109, USA
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(11), 675; https://doi.org/10.3390/ijgi9110675
Received: 27 September 2020 / Revised: 3 November 2020 / Accepted: 11 November 2020 / Published: 13 November 2020
(This article belongs to the Special Issue GIScience for Risk Management in Big Data Era)
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. View Full-Text
Keywords: COVID-19; home dwell time; time-series clustering; stay-at-home orders COVID-19; home dwell time; time-series clustering; stay-at-home orders
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MDPI and ACS Style

Huang, X.; Li, Z.; Lu, J.; Wang, S.; Wei, H.; Chen, B. Time-Series Clustering for Home Dwell Time during COVID-19: What Can We Learn from It? ISPRS Int. J. Geo-Inf. 2020, 9, 675. https://doi.org/10.3390/ijgi9110675

AMA Style

Huang X, Li Z, Lu J, Wang S, Wei H, Chen B. Time-Series Clustering for Home Dwell Time during COVID-19: What Can We Learn from It? ISPRS International Journal of Geo-Information. 2020; 9(11):675. https://doi.org/10.3390/ijgi9110675

Chicago/Turabian Style

Huang, Xiao, Zhenlong Li, Junyu Lu, Sicheng Wang, Hanxue Wei, and Baixu Chen. 2020. "Time-Series Clustering for Home Dwell Time during COVID-19: What Can We Learn from It?" ISPRS International Journal of Geo-Information 9, no. 11: 675. https://doi.org/10.3390/ijgi9110675

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