Change of human mobility during COVID-19: A United States case study.

With the onset of COVID-19 and the resulting shelter in place guidelines combined with remote working practices, human mobility in 2020 has been dramatically impacted. Existing studies typically examine whether mobility in specific localities increases or decreases at specific points in time and rel...

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Autores principales: Justin Elarde, Joon-Seok Kim, Hamdi Kavak, Andreas Züfle, Taylor Anderson
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Publicado: Public Library of Science (PLoS) 2021
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Acceso en línea:https://doaj.org/article/11cbdc515fe3456aa194058478d36793
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spelling oai:doaj.org-article:11cbdc515fe3456aa194058478d367932021-12-02T20:07:45ZChange of human mobility during COVID-19: A United States case study.1932-620310.1371/journal.pone.0259031https://doaj.org/article/11cbdc515fe3456aa194058478d367932021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0259031https://doaj.org/toc/1932-6203With the onset of COVID-19 and the resulting shelter in place guidelines combined with remote working practices, human mobility in 2020 has been dramatically impacted. Existing studies typically examine whether mobility in specific localities increases or decreases at specific points in time and relate these changes to certain pandemic and policy events. However, a more comprehensive analysis of mobility change over time is needed. In this paper, we study mobility change in the US through a five-step process using mobility footprint data. (Step 1) Propose the Delta Time Spent in Public Places (ΔTSPP) as a measure to quantify daily changes in mobility for each US county from 2019-2020. (Step 2) Conduct Principal Component Analysis (PCA) to reduce the ΔTSPP time series of each county to lower-dimensional latent components of change in mobility. (Step 3) Conduct clustering analysis to find counties that exhibit similar latent components. (Step 4) Investigate local and global spatial autocorrelation for each component. (Step 5) Conduct correlation analysis to investigate how various population characteristics and behavior correlate with mobility patterns. Results show that by describing each county as a linear combination of the three latent components, we can explain 59% of the variation in mobility trends across all US counties. Specifically, change in mobility in 2020 for US counties can be explained as a combination of three latent components: 1) long-term reduction in mobility, 2) no change in mobility, and 3) short-term reduction in mobility. Furthermore, we find that US counties that are geographically close are more likely to exhibit a similar change in mobility. Finally, we observe significant correlations between the three latent components of mobility change and various population characteristics, including political leaning, population, COVID-19 cases and deaths, and unemployment. We find that our analysis provides a comprehensive understanding of mobility change in response to the COVID-19 pandemic.Justin ElardeJoon-Seok KimHamdi KavakAndreas ZüfleTaylor AndersonPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11, p e0259031 (2021)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Justin Elarde
Joon-Seok Kim
Hamdi Kavak
Andreas Züfle
Taylor Anderson
Change of human mobility during COVID-19: A United States case study.
description With the onset of COVID-19 and the resulting shelter in place guidelines combined with remote working practices, human mobility in 2020 has been dramatically impacted. Existing studies typically examine whether mobility in specific localities increases or decreases at specific points in time and relate these changes to certain pandemic and policy events. However, a more comprehensive analysis of mobility change over time is needed. In this paper, we study mobility change in the US through a five-step process using mobility footprint data. (Step 1) Propose the Delta Time Spent in Public Places (ΔTSPP) as a measure to quantify daily changes in mobility for each US county from 2019-2020. (Step 2) Conduct Principal Component Analysis (PCA) to reduce the ΔTSPP time series of each county to lower-dimensional latent components of change in mobility. (Step 3) Conduct clustering analysis to find counties that exhibit similar latent components. (Step 4) Investigate local and global spatial autocorrelation for each component. (Step 5) Conduct correlation analysis to investigate how various population characteristics and behavior correlate with mobility patterns. Results show that by describing each county as a linear combination of the three latent components, we can explain 59% of the variation in mobility trends across all US counties. Specifically, change in mobility in 2020 for US counties can be explained as a combination of three latent components: 1) long-term reduction in mobility, 2) no change in mobility, and 3) short-term reduction in mobility. Furthermore, we find that US counties that are geographically close are more likely to exhibit a similar change in mobility. Finally, we observe significant correlations between the three latent components of mobility change and various population characteristics, including political leaning, population, COVID-19 cases and deaths, and unemployment. We find that our analysis provides a comprehensive understanding of mobility change in response to the COVID-19 pandemic.
format article
author Justin Elarde
Joon-Seok Kim
Hamdi Kavak
Andreas Züfle
Taylor Anderson
author_facet Justin Elarde
Joon-Seok Kim
Hamdi Kavak
Andreas Züfle
Taylor Anderson
author_sort Justin Elarde
title Change of human mobility during COVID-19: A United States case study.
title_short Change of human mobility during COVID-19: A United States case study.
title_full Change of human mobility during COVID-19: A United States case study.
title_fullStr Change of human mobility during COVID-19: A United States case study.
title_full_unstemmed Change of human mobility during COVID-19: A United States case study.
title_sort change of human mobility during covid-19: a united states case study.
publisher Public Library of Science (PLoS)
publishDate 2021
url https://doaj.org/article/11cbdc515fe3456aa194058478d36793
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