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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2021-01-01
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| Series: | PLoS ONE |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8562789/?tool=EBI |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-19T03:05:48Z |
| format | Article |
| id | doaj.art-fc8fdd7e7af148479fd5234a1fe7cf60 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-19T03:05:48Z |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-fc8fdd7e7af148479fd5234a1fe7cf602022-12-21T20:38:06ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-011611Change of human mobility during COVID-19: A United States case studyJustin ElardeJoon-Seok KimHamdi KavakAndreas ZüfleTaylor AndersonWith 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.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8562789/?tool=EBI |
| spellingShingle | Justin Elarde Joon-Seok Kim Hamdi Kavak Andreas Züfle Taylor Anderson Change of human mobility during COVID-19: A United States case study PLoS ONE |
| title | 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_short | 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 |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8562789/?tool=EBI |
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