Race and class disparities in COVID-19 cases are well documented, but pathways of possible transmission by neighborhood inequality are not. This study uses administrative data on COVID-19 cases for roughly 2000 census tracts in Wisconsin, Seattle/King County, and San Francisco to analyze how neighborhood socioeconomic (dis)advantage predicts cumulative caseloads through February 2021. Unlike past research, we measure a neighborhood’s disadvantage level using both its residents’ demographics and the demographics of neighborhoods its residents visit and are visited by, leveraging daily mobility data from 45 million mobile devices. In all three jurisdictions, we find sizable disparities in COVID-19 caseloads. Disadvantage in a neighborhood’s mobility network has greater impact than its residents’ socioeconomic characteristics. We also find disparities by neighborhood racial/ethnic composition, which can be explained, in part, by residential and mobility-based disadvantage. Neighborhood conditions measured before a pandemic offer substantial predictive power for subsequent incidence, with mobility-based disadvantage playing an important role.
Nascent research documents that U.S. racial segregation is not merely a residential phenomenon but is present in everyday mobility patterns. Better understanding the causes of mobility-based segregation requires disentangling the spatial macrosegregation, which constitutes an obvious confounding factor. In this work, the author analyzes big data on everyday visits between 270 million neighborhood dyads to estimate the effect of neighborhood racial composition on mobility patterns, net of driving, walking, and public transportation travel time. Matching on these travel times, the author finds that residents of Black and Hispanic neighborhoods visit White neighborhoods only slightly less than they visit other Black and Hispanic neighborhoods. Distinctly, residents of White neighborhoods are far less likely to visit non-White neighborhoods than other White neighborhoods, even net of travel time. The author finds that this travel time–adjusted visit homophily among White neighborhoods is greater in commuting zones where White neighborhoods are situated closer to non-White neighborhoods.
Previous studies on distributive environmental justice issues related to flooding exposure have been limited in spatial scale, afflicted by measurement shortcomings, and inconsistent in findings. We provide the first U.S. national and state-by-state descriptive portrait of annual average exposure to floods across all Census-defined racial/ethnic groups. Specifically, we investigate whether there are statistically significant interracial differences in average annual probabilities of experiencing a flood in the U.S. and how these differences vary across states and coastal vs. inland areas. We use predictions from the recent First Street Foundation flooding exposure model and demographic data from the US Census Bureau American Community Survey in our analysis. We observe no states in which non-Hispanic Blacks have a (statistically) significantly higher average exposure to floods than non-Hispanic Whites, but 21 states where the reverse is true. Hispanics have a significantly higher average exposure in three states, and a significantly lower average exposure in 18 states, compared to Whites. Notably, however, the aggregate Hispanic population of the three states where Hispanics face greater flood exposure (Illinois, Massachusetts, and Texas) exceeds the aggregate Hispanic population of the 18 where their average exposures are lower than Whites’. There are eight states in which Native Americans have significantly higher exposure, and none where have lower exposure than Whites’, implying that additional research and appropriate, community-informed policy responses should focus on this group. Further studies are also needed at smaller spatial scales to identify communities of color facing disproportionate flood exposures.
Sociological research has investigated neighborhood inequality across various consequential events. Crime and violence continue to be dominant phenomena examined. Less sociological attention has been given to other types of adverse incidents involving emergency services responses. In this article, the author draws on a unique data set on medical emergencies, fires, traffic collisions, gas leaks, carbon monoxide leaks, and hazardous incidents from more than 600 local first-responder agencies across the United States to examine neighborhood inequalities in prevalence. The author finds that across nearly all outcomes, neighborhood proportion Black is a dominant predictor of incidence that persists net of a battery of controls. The author additionally finds socioeconomic disparities across a few of these outcomes, including medical emergencies, fires, and traffic collisions. The author concludes by broadly encouraging more sociological research on these understudied events.
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