Neighborhood Racial and Economic Segregation and Disparities in Violence During the COVID-19 Pandemic

Objectives. To describe associations between neighborhood racial and economic segregation and violence during the COVID-19 pandemic. Methods. For 13 US cities, we obtained zip code–level data on 5 violence outcomes from March through July 2018 through 2020. Using negative binomial regressions and marginal contrasts, we estimated differences between quintiles of racial, economic, and racialized economic segregation using the Index of Concentration at the Extremes as a measure of neighborhood privilege (1) in 2020 and (2) relative to 2018 through 2019 (difference-in-differences). Results. In 2020, violence was higher in less-privileged neighborhoods than in the most privileged. For example, if all zip codes were in the least privileged versus most privileged quintile of racialized economic segregation, we estimated 146.2 additional aggravated assaults (95% confidence interval = 112.4, 205.8) per zip code on average across cities. Differences over time in less-privileged zip codes were greater than differences over time in the most privileged for firearm violence, aggravated assault, and homicide. Conclusions. Marginalized communities endure endemically high levels of violence. The events of 2020 exacerbated disparities in several forms of violence. Public Health Implications. To reduce violence and related disparities, immediate and long-term investments in low-income neighborhoods of color are warranted. (Am J Public Health. Published online ahead of print December 9, 2021:e1–e10. https://doi.org/10.2105/AJPH.2021.306540 )

Urban Shrinkage and Socio-Economic Segregation in Medium-Sized Cities: The Case of Schwerin (Germany)

Although past studies have found that processes of urban shrinkage may act as a catalyst for socio-economic segregation, these relationships remain underexplored outside the context of large cities and capitals. Moreover, cities at lower-tiers of the urban hierarchy in post-socialist Europe have been doubly excluded from the critical discourse on the socio-spatial effects of shrinkage. Hence, this article examines how shrinkage affects socio-economic segregation in the medium-sized post-socialist city of Schwerin, employing segregation indices to assess levels of spatial unevenness and location quotients to map intra-urban patterns of vulnerable population groups over time. Results indicate processes of shrinkage may exacerbate socio-economic segregation in medium-sized cities and that the spatial heterogeneity of shrinkage intersects with uneven distributions of affluence and poverty. However, suggesting that legacies of state socialism shape contemporary socio-spatial change, segregation in Schwerin is strongly conditioned by its socialist-era housing estates, which are generally characterised by the highest rates of population decline, vacancy, and vulnerable groups.

Socioeconomic differences and persistent segregation of Italian territories during COVID-19 pandemic

Lockdowns implemented to address the COVID-19 pandemic have disrupted human mobility flows around the globe to an unprecedented extent and with economic consequences which are unevenly distributed across territories, firms and individuals. Here we study socioeconomic determinants of mobility disruption during both the lockdown and the recovery phases in Italy. For this purpose, we analyze a massive data set on Italian mobility from February to October 2020 and we combine it with detailed data on pre-existing local socioeconomic features of Italian administrative units. Using a set of unsupervised and supervised learning techniques, we reliably show that the least and the most affected areas persistently belong to two different clusters. Notably, the former cluster features significantly higher income per capita and lower income inequality than the latter. This distinction persists once the lockdown is lifted. The least affected areas display a swift (V-shaped) recovery in mobility patterns, while poorer, most affected areas experience a much slower (U-shaped) recovery: as of October 2020, their mobility was still significantly lower than pre-lockdown levels. These results are then detailed and confirmed with a quantile regression analysis. Our findings show that economic segregation has, thus, strengthened during the pandemic.

Embodying (In)justice and Embodied Truths

Chapter 2 discusses application of ecosocial theory to analyze the health impacts of Jim Crow and its legal abolition, racialized and economic breast cancer inequities involving the breast cancer estrogen receptor, and the joint health impacts of physical and social hazards at work (including racism, sexism, and heterosexism) and relationship hazards (involving unsafe sex and violence). It also uses ecosocial theory to develop and apply measures of structural injustice, including historical redlining (1930s US government policies imposing racial residential segregation) and contemporary racialized economic segregation. The chapter additionally explains the construct of “emergent embodied phenotypes” and the different types of histories involved in disease processes: societal, individual (lifecourse), pathological/cellular, and evolutionary. It concludes by providing selected examples of how others, in diverse disciplines and settings worldwide, have employed the ecosocial theory of disease distribution to conceptualize and analyze embodiment of (in)justice across a wide range of exposures and outcomes.

You Can’t Get There from Here: Mobility Networks and the Housing Choice Voucher Program

The Housing Choice Voucher (HCV) program represents the largest subsidized housing program in the United States. While families with vouchers can, in theory, lease any housing of reasonable quality renting below a rent ceiling, the empirical evidence suggests that they rarely use their vouchers to move to lower poverty neighborhoods. This paper examines the question of how spatial boundaries impact the residential possibilities of HCV subsidized families, both the visible boundaries of Public Housing Authority (PHA) catchment areas and the invisible boundaries of racial and economic segregation. I use administrative data supplied by the Department of Housing and Urban Development, which includes all moves by HCV families between 2005 and 2015 in the Baltimore, MD, Cleveland, OH, and Dallas, TX, metropolitan areas. Using a Louvain method of network cluster detection, I subdivide each metro into distinct mobility clusters—sets of census tracts within which voucher holders move but between which they rarely do. I find that the empirical mobility clusters at the metropolitan level are highly defined by PHA’s catchment areas. Even though families are technically allowed to “port” their voucher from one PHA catchment area to another, such behavior is rare. Within the PHA catchment areas, HCV mobility clusters are defined by patterns of race, income, and history. These findings suggest that patterns of racial and economic segregation seem to partially define the mobility clusters within PHA catchment areas, but not across them.

Segregation, Caste and Cash: Segrenomics in Detroit

Though in other countries caste is generally understood to name social stratification based on ethnic and/or religious affiliation, in the United States, racial and economic segregation in housing and education are the factors that trap one to the lower rungs of the social system in that nation. Significantly, these caste making levels of segregation are “cash making” for wealthy business concerns. In my earlier work, I have referred to this profit from segregation as, “segrenomics.” In this piece, I offer an example of the mechanics of these relationships relative to segregated schools, caste, and cash making in the city of Detroit, Michigan.

Self-Driving Vehicles—an Ethical Overview

The introduction of self-driving vehicles gives rise to a large number of ethical issues that go beyond the common, extremely narrow, focus on improbable dilemma-like scenarios. This article provides a broad overview of realistic ethical issues related to self-driving vehicles. Some of the major topics covered are as follows: Strong opinions for and against driverless cars may give rise to severe social and political conflicts. A low tolerance for accidents caused by driverless vehicles may delay the introduction of driverless systems that would substantially reduce the risks. Trade-offs will arise between safety and other requirement on the road traffic system. Over-reliance on the swift collision-avoiding reactions of self-driving vehicles can induce people to take dangerous actions, such as stepping out in front of a car, relying on its fast braking. Children travelling alone can violate safety instructions such as the use of seatbelts. Digital information about routes and destinations can be used to convey commercial and political messages to car users. If fast passage can be bought, then socio-economic segregation of road traffic may result. Terrorists and other criminals can hack into a vehicle and make it crash. They can also use self-driving vehicles for instance to carry bombs to their designed places of detonation or to wreak havoc on a country’s road system.

Spatial inequalities of retail food stores may determine availability of healthful food choices in a Brazilian metropolis

Objective: To examine the association between economic residential segregation and food environment. Design: Ecological. Food stores categorized according to the NOVA classification were geocoded, and absolute availability was calculated for each neighborhood. Segregation was measured using local Gi* statistic, a measure of the standard deviation (SD) between the economic composition of a neighborhood (the proportion of heads of households in neighborhoods earn monthly income of 0 to 3 minimum wages) and larger metropolitan area, weighted by the economic composition of surrounding neighborhoods. Segregation was categorized as high [most segregated], medium [integrated], and low [less segregated or integrated]. A proportional odds models were used to model the association between segregation and food environment. Setting: Belo Horizonte, Brazil. Participants: Food stores. Results: After adjustment for covariates, neighborhoods characterized by high economic segregation had fewer food stores overall compared to neighborhoods characterized by low segregation [OR=0.56;CI95%=0.45-0.69]. In addition, high segregated neighborhoods were 49% (OR=0.51;95%CI=0.42–0.61) and 45% (OR=0.55;95% CI=0.45–0.67) less likely to have a high number of food stores that predominantly marketed ultra-processed foods and mixed food stores, respectively, as compared to their counterparts. Conclusions: Economic segregation is associated with differences in the distribution of food stores. Both low and high segregation territories should be prioritized by public policies to ensure healthy and adequate nutrition as a right for all communities. The former must continue to be protected from access to unhealthy commercial food outlets while the latter must be the locus of actions that limit the availability of unhealthy commercial food store.