Examining post-pandemic behaviors influencing human mobility trends

Abstract

COVID-19 unleashed a global pandemic that has resulted in human, economic, and social crises of unprecedented scale. While the efficacy of mobility restrictions in curbing contagion has been scientifically and empirically acknowledged, a deeper understanding of the human behavioral trends driving the mixed adoption of mobility restrictions will aid future policymaking. In this paper, we employ associative rule-mining and regression to pinpoint socioeconomic and demographic factors influencing the evolving mobility trends. We compare and contrast short-distance and long-distance trips by analyzing Chicago county-level and US state-level mobility. Our study yields rules that explain the changing propensity in trip length and the collective effect of population density, economic standing, COVID testing, and the number of infected cases on mobility decisions. Through regression and correlation analysis, we show the influence of ethnic and demographic factors and perception of infection on short and long-distance trips. We find that the new mobility rules correspond to reduced long- and short-distance trip frequencies. We graphically demonstrate a marked decline in the proportion of long county-level trips but a minor change in the distribution of state-level trips. Our correlation study highlights it is hard to characterize the effect of perception of infection spread on mobility decisions. We conclude the paper with a discussion on the overlap between the analysis in the existing literature on both during- and post-lockdown mobility trends and our findings.