Change of human mobility during COVID-19: A United States case study
This research provides a comprehensive analysis of mobility changes during the pandemic for public health and policy researchers, though it is incremental as it builds on existing methods with new data.
The study tackled the problem of quantifying and analyzing human mobility changes in the US during COVID-19 by proposing a new measure (Delta-TSPP) and using PCA, clustering, and correlation analyses, resulting in the identification of three latent components explaining 59% of mobility variation and significant correlations with population characteristics.
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. 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 (Delta-TSPP) 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 Delta-TSPP 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. 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.