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
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/fc8fdd7e7af148479fd5234a1fe7cf60
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spelling	oai:doaj.org-article:fc8fdd7e7af148479fd5234a1fe7cf602021-11-11T06:44:20ZChange of human mobility during COVID-19: A United States case study1932-6203https://doaj.org/article/fc8fdd7e7af148479fd5234a1fe7cf602021-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8562789/?tool=EBIhttps://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 (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/fc8fdd7e7af148479fd5234a1fe7cf60
work_keys_str_mv	AT justinelarde changeofhumanmobilityduringcovid19aunitedstatescasestudy AT joonseokkim changeofhumanmobilityduringcovid19aunitedstatescasestudy AT hamdikavak changeofhumanmobilityduringcovid19aunitedstatescasestudy AT andreaszufle changeofhumanmobilityduringcovid19aunitedstatescasestudy AT tayloranderson changeofhumanmobilityduringcovid19aunitedstatescasestudy
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Change of human mobility during COVID-19: A United States case study

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