Historical redlining and food environments: A study of 102 urban areas in the United States.

OBJECTIVE: Unhealthy food environments are disproportionally concentrated in neighborhoods with clustering of racial/ethnic minorities and poverty. This disparity has been blamed, in part, on market self-regulation. This explanation risks overlooking past and current practices of racial segregation that have created and reinforced the obstacles blocking investments from food retailers in marginalized neighborhoods. We fill this gap by investigating how the long-term ramifications of redlining, discriminatory housing practices enacted by federal Home Owner Lending Corporation (HOLC) in the 1930s, has evolved generations later to disproportionally exposing neighborhoods to unhealthy food environments. METHODS: We overlaid historical redlining maps over 2010 food environment observations at the census tract level to identify areas with less healthy food environments and to assess the historical context of those areas. For 11,651 census tracts within 102 U.S. urban areas, we described the healthiness of food environments as measured by the modified retail food environment index (mRFEI). Using hurdle models with random effects, we further examined the association between redlining housing practice and food environments. RESULTS: The results indicate that historically redlined neighborhoods show a higher likelihood for unhealthy retail food environments even for census tracts with present-day economic and racial privilege. CONCLUSION: The current evidence shows how structural discrimination manifested by unjust housing practices and racial residential segregation fueled an uneven food environment where minority neighborhoods disproportionally bore the brunt of restrictive food access. It highlights an urgent need to ameliorate patterns of housing inequality as a fix to unequal food environments.

Historical Redlining and Resident Exposure to COVID-19: A Study of New York City.

The Coronavirus Disease 2019 (COVID-19) has been reported to disproportionately impact racial/ethnic minorities in the USA, both in terms of infections and deaths. This racial disparity in the COVID-19 outcomes may result from the segregation of minorities in neighborhoods with health-compromising conditions. We, thus, anticipate that neighborhoods would be especially vulnerable to COVID-19 if they are of present-day economic and racial disadvantage and were redlined historically. To test this expectation, we examined the change of both confirmed COVID-19 cases and deaths from April to July, 2020, in zip code tabulation areas (ZCTAs) in the New York City using multilevel regression analysis. The results indicate that ZCTAs with a higher proportion of black and Hispanic populations are associated with a higher percentage of COVID-19 infection. Historically low-graded neighborhoods show a higher risk for COVID-19 infection, even for ZCTAs with present-day economic and racial privilege. These associations change over time as the pandemic unfolds. Racial/ethnic minorities are bearing the brunt of the COVID-19 pandemic's health impact. The current evidence shows that the pre-existing social structure in the form of racial residential segregation could be partially responsible for the disparities observed, highlighting an urgent need to stress historical segregation and to build a less segregated and more equal society.

Characterizing equitable access to grocery stores during disasters using location-based data.

Natural hazards cause disruptions in access to critical facilities, such as grocery stores, impeding residents' ability to prepare for and cope with hardships during the disaster and recovery; however, disrupted access to critical facilities is not equal for all residents of a community. In this study, we examine disparate access to grocery stores in the context of the 2017 Hurricane Harvey in Harris County, Texas. We utilized high-resolution location-based datasets in implementing spatial network analysis and dynamic clustering techniques to uncover the overall disparate access to grocery stores for socially vulnerable populations during different phases of the disaster. Three access indicators are examined using network-centric measures: number of unique stores visited, average trip time to stores, and average distance to stores. These access indicators help us capture three dimensions of access: redundancy, rapidity, and proximity. The findings show the insufficiency of focusing merely on the distributional factors, such as location in a food desert and number of facilities, to capture the disparities in access, especially during the preparation and impact/short-term recovery periods. Furthermore, the characterization of access by considering combinations of access indicators reveals that flooding disproportionally affects socially vulnerable populations. High-income areas have better access during the preparation period as they are able to visit a greater number of stores and commute farther distances to obtain supplies. The conclusions of this study have important implications for urban development (facility distribution), emergency management, and resource allocation by identifying areas most vulnerable to disproportionate access impacts using more equity-focused and data-driven approaches.

Quantifying community resilience based on fluctuations in visits to points-of-interest derived from digital trace data.

This research establishes a methodological framework for quantifying community resilience based on fluctuations in a population's activity during a natural disaster. Visits to points-of-interests (POIs) over time serve as a proxy for activities to capture the combined effects of perturbations in lifestyles, the built environment and the status of business. This study used digital trace data related to unique visits to POIs in the Houston metropolitan area during Hurricane Harvey in 2017. Resilience metrics in the form of systemic impact, duration of impact, and general resilience (GR) values were examined for the region along with their spatial distributions. The results show that certain categories, such as religious organizations and building material and supplies dealers had better resilience metrics-low systemic impact, short duration of impact, and high GR. Other categories such as medical facilities and entertainment had worse resilience metrics-high systemic impact, long duration of impact and low GR. Spatial analyses revealed that areas in the community with lower levels of resilience metrics also experienced extensive flooding. This insight demonstrates the validity of the approach proposed in this study for quantifying and analysing data for community resilience patterns using digital trace/location-intelligence data related to population activities. While this study focused on the Houston metropolitan area and only analysed one natural hazard, the same approach could be applied to other communities and disaster contexts. Such resilience metrics bring valuable insight into prioritizing resource allocation in the recovery process.