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.

The overturning of Roe v Wade reinvigorated the national debate on abortion. We used Twitter data to examine temporal, geographical and sentiment patterns in the public’s reaction. Using the Twitter API for Academic Research, a random sample of publicly available tweets was collected from 1 May–15 July in 2021 and 2022. Tweets were filtered based on keywords relating to Roe v Wade and abortion (227,161 tweets in 2021 and 504,803 tweets in 2022). These tweets were tagged for sentiment, tracked by state, and indexed over time. Time plots reveal low levels of conversations on these topics until the leaked Supreme Court opinion in early May 2022. Unlike pro-choice tweets which declined, pro-life conversations continued with renewed interest throughout May and increased again following the official overturning of Roe v Wade. Conversations were less prevalent in some these states had abortion trigger laws (Wyoming, North Dakota, South Dakota, Texas, Louisiana, and Mississippi). Collapsing across topic categories, 2022 tweets were more negative and less neutral and positive compared to 2021 tweets. In network analysis, tweets mentioning woman/women, supreme court, and abortion spread faster and reached to more Twitter users than those mentioning Roe Wade and Scotus. Twitter data can provide real-time insights into the experiences and perceptions of people across the United States, which can be used to inform healthcare policies and decision-making.

It is crucial to understand the current pattern of urban park visitation to achieve environmental justice. Current discussions of environmental equity of parks mainly focus on the inequality provision measured by park accessibility, park area, park quality, and park congestion, ignoring the inequity of social benefits through interactions among mixed-income groups. Based on fine-grained mobile phone location data at the census block group level in 2018 and 2019, we explored visitation patterns and the mixed-income levels of visitors in urban parks in New York City. The visitors were divided into five income groups, with the “income entropy” used as a measure of the mixed level of the visitors in terms of income groups. We answered an important question: what factors affect the visitation intensity and the mixed level of income groups in urban park visitors? Our results revealed that the time of year and season, the parks’ characteristics, the built environment, and the socioeconomic characteristics of the park’s surrounding neighborhoods have strong associations with park use patterns. We also offered implications for urban planning and urban design to promote park visitation and income-diverse park use and improve social inclusion and environmental equity.

Historic redlining grades that were assigned to US neighborhoods based largely on minority race or ethnicity by the Home Owners Loan Corporation (HOLC) during the 1930s have been linked with adverse health outcomes among neighborhood residents. This review aimed to summarize the quantitative evidence, so we could determine if any findings are replicated and otherwise identify research gaps. Systematic review. We conducted a systematic review by searching the PubMed® MEDLINE database for observational studies that reported on health outcomes among people who resided in neighborhoods that were assigned HOLC grades. We assessed quality by allocating points based on whether studies reported the sample size or count of people affected by outcomes (yes = 1 point, no = 2 points), and whether unadjusted magnitudes of association were reported alongside adjusted estimates (yes = 1 point, no = 2 points). The sum score was used to classify each study as high (2 points), average (3 points) or low quality (4 points). Among the 89 articles identified, 32 met inclusion criteria; 15 were deemed high-quality. The most frequently studied health conditions in order of descending frequency were: i) injury or violence (n = 8), ii) cancer (n = 7), iii) cardiometabolic (n = 6), iv) perinatal (n = 5), v) asthma (n = 2). People who lived in areas with less desirable HOLC grades tended to suffer higher than expected rates of injury or violence, asthma, adverse pregnancy outcomes, and some cardiometabolic disorders; associations with cancer were mostly null. Methodological differences limited opportunities for direct comparison across studies, and there was significant heterogeneity among the few estimates that were generally comparable. While robust data are lacking, the limited existing evidence supports a possible association between historically redlined areas and heightened risk of adverse health outcomes. Why this association may exist remains unknown.

In this study, we investigate the potential driving factors that lead to the disparity in the time-series of home dwell time, 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, and further assess the statistical significance of sixteen demographic/socioeconomic variables from five major categories. 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 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. Policymakers need to carefully evaluate the inevitable trade-off among different groups, making sure the outcomes of their policies reflect interests of the socially disadvantaged groups. Highlights: We perform a trend-driven analysis by conducting Kmeans time-series clustering using fine- grained home dwell time records from SafeGraph. We find that demographic/socioeconomic variables can explain the disparity in home dwell time in response to the stay-at-home order. The results suggest that socially disadvantaged groups are less likely to follow the order to stay at home, potentially leading to more exposures to the COVID-19. Policymakers need to make sure the outcomes of their policies reflect the interests of the disadvantaged groups.