Time-Series Clustering for Home Dwell Time during COVID-19: What Can We Learn from It?
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 st...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/9/11/675 |
| _version_ | 1797548014750924800 |
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| author | Xiao Huang Zhenlong Li Junyu Lu Sicheng Wang Hanxue Wei Baixu Chen |
| author_facet | Xiao Huang Zhenlong Li Junyu Lu Sicheng Wang Hanxue Wei Baixu Chen |
| author_sort | Xiao Huang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T14:53:20Z |
| format | Article |
| id | doaj.art-e6647f5058294c248eb3ea06f4c053a4 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T14:53:20Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-e6647f5058294c248eb3ea06f4c053a42023-11-20T20:51:56ZengMDPI AGISPRS International Journal of Geo-Information2220-99642020-11-0191167510.3390/ijgi9110675Time-Series Clustering for Home Dwell Time during COVID-19: What Can We Learn from It?Xiao Huang0Zhenlong Li1Junyu Lu2Sicheng Wang3Hanxue Wei4Baixu Chen5Department of Geosciences, University of Arkansas, Fayetteville, AR 72701, USADepartment of Geography, University of South Carolina, Columbia, SC 29208, USASchool of Community Resources and Development, Arizona State University, Phoenix, AZ 85004, USADepartment of Geography, University of South Carolina, Columbia, SC 29208, USADepartment of City and Regional Planning, Cornell University, Ithaca, NY 14850, USADepartment of Computer Science and Engineering, University of Michigan, Ann Arbor, MI 48109, USAIn 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.https://www.mdpi.com/2220-9964/9/11/675COVID-19home dwell timetime-series clusteringstay-at-home orders |
| spellingShingle | Xiao Huang Zhenlong Li Junyu Lu Sicheng Wang Hanxue Wei Baixu Chen Time-Series Clustering for Home Dwell Time during COVID-19: What Can We Learn from It? ISPRS International Journal of Geo-Information COVID-19 home dwell time time-series clustering stay-at-home orders |
| title | Time-Series Clustering for Home Dwell Time during COVID-19: What Can We Learn from It? |
| title_full | Time-Series Clustering for Home Dwell Time during COVID-19: What Can We Learn from It? |
| title_fullStr | Time-Series Clustering for Home Dwell Time during COVID-19: What Can We Learn from It? |
| title_full_unstemmed | Time-Series Clustering for Home Dwell Time during COVID-19: What Can We Learn from It? |
| title_short | Time-Series Clustering for Home Dwell Time during COVID-19: What Can We Learn from It? |
| title_sort | time series clustering for home dwell time during covid 19 what can we learn from it |
| topic | COVID-19 home dwell time time-series clustering stay-at-home orders |
| url | https://www.mdpi.com/2220-9964/9/11/675 |
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