No abstract

The early outbreak of coronavirus disease-2019 (COVID)-19 became associated with various ‘hot spots’ in the USA, particularly in large cities. However, despite the widespread nature of the outbreak, much of what is known about the virus’ impact and clusters is understood either for individuals, or at the state level. This paper assesses the predictors of outbreaks at the neighborhood level. Using data from the Louisiana Department of Health, we use spatial regression models to analyze the case count through 3 May 2020 and its relationship to individual and geographic neighborhood characteristics at the census tract level. We find a particularly strong and large correlation between race and COVID-19 cases, robust to model specification and spatial autocorrelation. In addition, neighborhoods with lower rates of poverty and those with fewer residents over 70 have fewer cases. Policy makers should adjust testing strategies to better service the hardest hit populations, particularly minorities and the elderly. In addition, the results are greater evidence of the impact of systemic issues on health, which require a long-term strategy for redress.

No abstract

Hurricane Katrina struck the city of New Orleans in August of 2005, devastating the built environment and displacing nearly one-third of the city’s residents. Despite the considerable literature that exists concerning Hurricane Katrina, the storm’s long-term impact on neighbourhood change in New Orleans has not been fully addressed. In this article we analyse the potential for Hurricane Katrina to have contributed to patterns of gentrification during the city’s recovery one decade after the storm. We study the association between Hurricane Katrina and neighbourhood change using data on the damage from the storm at the census tract level and Freeman’s (2005) gentrification framework. We find that damage is positively associated with the likelihood of a neighbourhood gentrifying in New Orleans after one decade, which drives our recommendations for policy makers to take greater concern for their communities during the process of rebuilding from storm damage.

Despite the growing number of natural disasters around the globe, limited research exists on post‐disaster patterns of neighborhood change. In this paper, we test two theories of neighborhood change, the “recovery machine” and “rent gap,” which predict opposing effects for low socioeconomic status (SES) neighborhoods following damage from hurricanes, tropical storms, and other natural hazard events. The recovery machine theory posits that after natural hazard events, local communities experience patterns of recovery based on their pre‐disaster SES and access to resources, suggesting that wealthier neighborhoods will recover robustly while lower status neighborhoods languish. In contrast, the rent gap theory suggests that developers will identify a profit opportunity in the depressed values created by damage from natural hazard events, and seek to redevelop low SES areas. We use fixed effects models with census data from 1970 to 2015 to test the impact of damage from natural hazards on neighborhood change. We find substantial recovery and change in low‐income neighborhoods, but not in the high‐income neighborhoods supporting the rent gap theory. We conclude that natural hazard events resulting in damage produce uneven recovery by socioeconomic status of neighborhoods, potentially leading to displacement of low SES groups.