The coronavirus disease 2019 (COVID-19) has exposed and, to some degree, exacerbated social inequity in the United States. This study reveals the correlation between demographic and socioeconomic variables and home-dwelling time records derived from large-scale mobile phone location tracking data at the U.S. census block group (CBG) level in the twelve most populated Metropolitan Statistical Areas (MSAs) and further investigates the contribution of these variables to the disparity in home-dwelling time that reflects the compliance with stay-at-home orders via machine learning approaches. We find statistically significant correlations between the increase in home-dwelling time ( ${\nabla }_{\mathit{HDT}}$) and variables that describe economic status in all MSAs, which is further confirmed by the optimized random forest models, because median household income and percentage of high income are the two most important variables in predicting ${\nabla }_{\mathit{HDT}}.$ The partial dependence between median household income and ${\nabla }_{\mathit{HDT}}$ reveals that the contribution of income to ${\nabla }_{\mathit{HDT}}$ is place dependent, nonlinear, and different given varying income intervals. Our study reveals the luxury nature of stay-at-home orders with which lower income groups cannot afford to comply. Such disparity in responses under stay-at-home orders reflects the long-standing social inequity issues in the United States, potentially causing unequal exposure to COVID-19 that disproportionately affects vulnerable populations. We must confront systemic social inequity issues and call for a high-priority assessment of the long-term impact of COVID-19 on geographically and socially disadvantaged groups.

In this study, we investigate the potential driving factors that lead to the disparity in the time-series of home dwell time in a data-driven manner, aiming to provide fundamental knowledge that benefits policy-making for better mitigation strategies of future pandemics. Taking Metro Atlanta as a study case, we perform a trend-driven analysis by conducting Kmeans time-series clustering using fine-grained home dwell time records from SafeGraph. Furthermore, we apply ANOVA (Analysis of Variance) coupled with post-hoc Tukey’s test to assess the statistical difference in sixteen recoded demographic/socioeconomic variables (from ACS 2014–2018 estimates) among the identified time-series clusters. We find that demographic/socioeconomic variables can explain the disparity in home dwell time in response to the stay-at-home order, which potentially leads to disparate exposures to the risk from the COVID-19. The results further suggest that socially disadvantaged groups are less likely to follow the order to stay at home, pointing out the extensive gaps in the effectiveness of social distancing measures that exist between socially disadvantaged groups and others. Our study reveals that the long-standing inequity issue in the U.S. stands in the way of the effective implementation of social distancing measures.

Background: The fast-changing labor market highlights the need for an in-depth understanding of occupational mobility impacted by technological change. However, we lack a multidimensional classification scheme that considers similarities of occupations comprehensively, which prevents us from predicting employment trends and mobility across occupations. This study fills the gap by examining employment trends based on similarities between occupations. Method: We first demonstrated a new method that clusters 756 occupation titles based on knowledge, skills, abilities, education, experience, training, activities, values, and interests. We used the Principal Component Analysis to categorize occupations in the Standard Occupational Classification, which is grouped into a four-level hierarchy. Then, we paired the occupation clusters with the occupational employment projections provided by the U.S. Bureau of Labor Statistics. We analyzed how employment would change and what factors affect the employment changes within occupation groups. Particularly, we specified factors related to technological changes. Results: The results reveal that technological change accounts for significant job losses in some clusters. This poses occupational mobility challenges for workers in these jobs at present. Job losses for nearly 60% of current employment will occur in low-skill, low-wage occupational groups. Meanwhile, many mid-skilled and highly skilled jobs are projected to grow in the next ten years. Conclusion: Our results demonstrate the utility of our occupational classification scheme. Furthermore, it suggests a critical need for skills upgrading and workforce development for workers in declining jobs. Special attention should be paid to vulnerable workers, such as older individuals and minorities.