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

PLoS One

Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA, United States of America.

Published: November 2021

AI Article Synopsis

  • The COVID-19 pandemic significantly affected human mobility in the US during 2020, leading to a need for a comprehensive analysis beyond existing studies that focus on specific points in time.
  • The research employs a five-step process using mobility footprint data, introducing the Delta Time Spent in Public Places (ΔTSPP) to quantify daily mobility changes and analyze variations across counties.
  • The study finds that mobility trends can be explained by three latent components: long-term reduction, no change, and short-term reduction in mobility, with notable correlations to factors like population characteristics, political leaning, and COVID-19 impacts.

Article Abstract

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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8562789PMC
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0259031PLOS

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