How Much Were Minority Neighborhoods in the United States Affected During COVID-19 in the Aspect of Business Closure Rates? Evidence From Six Metropolitan Areas

Teaching and Learning Guide for: Racial Residential Segregation in Urban America

This study maps the geography of the recent housing crisis within and across American metropolitan areas, and evaluates how it is related to a series of spatial and socioeconomic variables at neighborhood and metropolitan levels. It finds that the spatial patterns of housing recessions vary widely by region. In general, fast-growing metropolitan areas in the Southwest and Florida experienced not only deeper but also longer housing recessions. In contrast, metropolitan areas in the South (except in Florida) saw shallower and shorter housing recessions. Metropolitan areas in the Midwest and Northeast had fewer price declines in the crisis, but their housing recessions tended to be longer. Housing recessions tend to be deeper and longer in larger metropolitan areas. Neighborhoods located closer to city centers experienced shallower and shorter recessions compared with those in fringe areas. Even after controlling for many other variables, automobile dependency is still a strong and positive predictor of housing recession depth and duration. The effects of other urban form variables, such as land-use density and mixed use, are mixed and vary by region. The significance of the effects of neighborhood demographic variables on recession depth is highly dependent on the inclusion of high-risk loan in the model, suggesting that predatory and high-risk lending is one major reason why lower income and minority neighborhoods were hit harder by the recent housing crisis. The effects of high-risk loan and neighborhood demographic variables on housing recession duration, however, are rather weak.

We are starting to understand the magnitude of economic damage from the COVID-19 pandemic. Current estimates cover the national or state level, but tell us little about how massive business closures may be affecting urban vitality at the intrametropolitan level. A particular concern is whether urban areas with high poor or minority populations are more deeply affected. This paper combines InfoGroup Historic Business Data and Google Map API to analyze business closures at the neighborhood level in Franklin County, Ohio, encompassing the Columbus Metropolitan Area. As expected, retail and restaurant sectors had the highest number of closures, but closure rates were higher in other sectors. Descriptive and multivariate analyzes reveal that downtown Columbus has been severely affected, but no disadvantaging effect for communities of color or neighborhoods with concentrations of poor residents is found to be statistically significant.

BackgroundThe COVID-19 pandemic has had profound and differential impacts on metropolitan areas across the United States and around the world. Within the United States, metropolitan areas that were hit earliest with the pandemic and reacted with scientifically based health policy were able to contain the virus by late spring. For other areas that kept businesses open, the first wave in the United States hit in mid-summer. As the weather turns colder, universities resume classes, and people tire of lockdowns, a second wave is ascending in both metropolitan and rural areas. It becomes more obvious that additional SARS-CoV-2 surveillance is needed at the local level to track recent shifts in the pandemic, rates of increase, and persistence.ObjectiveThe goal of this study is to provide advanced surveillance metrics for COVID-19 transmission that account for speed, acceleration, jerk and persistence, and weekly shifts, to better understand and manage risk in metropolitan areas. Existing surveillance measures coupled with our dynamic metrics of transmission will inform health policy to control the COVID-19 pandemic until, and after, an effective vaccine is developed. Here, we provide values for novel indicators to measure COVID-19 transmission at the metropolitan area level.MethodsUsing a longitudinal trend analysis study design, we extracted 260 days of COVID-19 data from public health registries. We used an empirical difference equation to measure the daily number of cases in the 25 largest US metropolitan areas as a function of the prior number of cases and weekly shift variables based on a dynamic panel data model that was estimated using the generalized method of moments approach by implementing the Arellano-Bond estimator in R.ResultsMinneapolis and Chicago have the greatest average number of daily new positive results per standardized 100,000 population (which we refer to as speed). Extreme behavior in Minneapolis showed an increase in speed from 17 to 30 (67%) in 1 week. The jerk and acceleration calculated for these areas also showed extreme behavior. The dynamic panel data model shows that Minneapolis, Chicago, and Detroit have the largest persistence effects, meaning that new cases pertaining to a specific week are statistically attributable to new cases from the prior week.ConclusionsThree of the metropolitan areas with historically early and harsh winters have the highest persistence effects out of the top 25 most populous metropolitan areas in the United States at the beginning of their cold weather season. With these persistence effects, and with indoor activities becoming more popular as the weather gets colder, stringent COVID-19 regulations will be more important than ever to flatten the second wave of the pandemic. As colder weather grips more of the nation, southern metropolitan areas may also see large spikes in the number of cases.

This paper presents measures of segregation in public schools for metropolitan areas. It shows that, not only are metropolitan areas very segregated, most of that segregation is due to racial disparities between districts rather than segregative patterns within districts. Metropolitan areas in the South and West tend to have larger districts, and thus feature less fragmentation by school district. Segregation at the metropolitan level appears to vary systematically with size, racial mix, and region. Because larger metropolitan areas tend to have more jurisdictions and exhibit greater differences in racial composition among jurisdictions, measured segregation rises with size, as measured by school enrollment

This paper presents measures of segregation in public schools for metropolitan areas. It shows that, not only are metropolitan areas very segregated, most of that segregtion is due to racial disparities between districts rather than segregative patterns within districts. Metropolitan areas in the South and West tend to have larger districts, and thus feature less fragmentation by school district. Segregation at the metropolitan level appears to vary systematically with size, racial mix, and region. Because larger metropolitan areas tend to have more jurisdictions and exhibit greater differences in racial composition among jurisdictions, measured segregation rises with size, as measured by school enrollment.

For much of the 20th century, America's central cities were viewed as synonymous with economic and social hardship, often used as proxy for low-income communities of color. Since the 1990s, however, many metropolitan areas have seen a resurgence of interest in central city neighborhoods. Theoretical models of income sorting lead to ambiguous predictions about where households of different income levels will live within metropolitan areas. In this paper, we explore intra-city spatial patterns of income and race for U.S. metropolitan areas, focusing particularly on the locations of low-income and minority neighborhoods. Results indicate that, on average, income and white population shares increase with distance to city centers. However, many centrally located neighborhoods are neither low-income nor majority non-white, while low-income and minority neighborhoods are spatially dispersed across most metropolitan areas.

Objective:Health inequalities were often exacerbated during the emerging epidemic. This study examined urban and non-urban inequalities in health services among COVID-19 patients aged 65 years or above in Florida, USA, from 2 March to 27 May 2020.Methods:A retrospective time series analysis was conducted using individual patient records. Multivariable Poisson’s and logistic models were used to calculate adjusted incidence of COVID-19 and the associated rates of emergency department visits, hospitalizations, and deaths.Results:As of 27 May 2020, there were 13,659 elderly COVID-19 patients (people aged 65 years or above) in Florida and 14.9% of them died. Elderly people living in small metropolitan areas might be less likely to be confirmed with COVID-19 infection than those living in large metropolitan areas. The emergency department visit and hospitalization rates decreased significantly across metropolitan statuses for both men and women. Those patients living in small metropolitan or rural areas were less likely to be hospitalized than those living in large metropolitan areas (35% and 34% vs 41%). Elderly women aged 75 years or above living in rural areas had 113% higher adjusted incidence of COVID-19 than those living in large metropolitan areas, and the rates of hospitalizations were lower compared with those counterparts living in large metropolitan areas (29% vs 46%; odds ratio: 0.37 (0.25–0.54), p < 0.001).Conclusion:For elderly people living in Florida, USA, those living in small metropolitan or rural areas were less likely to receive adequate health care than those living in large or medium metropolitan areas during the COVID-19 pandemic.

Other: Pain medication availability in minority and caucasian neighborhoods in Michigan: a preliminary analysis

Using geo-referenced data from the Panel Study of Income Dynamics, in conjunction with decennial census data, this research examines metropolitan-area variation in the ability of residentially mobile blacks, Hispanics, and whites to convert their income into two types of neighborhood outcomes-neighborhood racial composition and neighborhood socioeconomic status. For destination tract racial composition, we find strong and near-universal support for the "weak version" of place stratification theory; relative to whites, the effect of individual income on the percent of the destination tract population that is non-Hispanic white is stronger for blacks and Hispanics, but even the highest earning minority group members move to tracts that are "less white" than the tracts that the highest-earning whites move to. In contrast, for moves into neighborhoods characterized by higher levels of average family income, we find substantial heterogeneity across metropolitan areas in minorities' capacity to convert income into neighborhood quality. A slight majority of metropolitan areas evince support for the "strong version" of place stratification theory, in which blacks and Hispanics are less able than whites to convert income into neighborhood socioeconomic status. However, a nontrivial number of metropolitan areas also evince support for spatial assimilation theory, where the highest-earning minorities achieve neighborhood parity with the highest-earning whites. Several metropolitan-area characteristics, including residential segregation, racial and ethnic composition, immigrant population size, poverty rates, and municipal fragmentation, emerge as significant predictors of minority-white differences in neighborhood attainment.

Predictors of Neighborhood Risk for Late-Stage Melanoma: Addressing Disparities through Spatial Analysis and Area-Based Measures

This study examines how population change is associated with changes in sociodemographics and economic outcomes across diverse geographic contexts in the United States from 2000 to 2020. Using Census Tract-level data and generalized additive models (GAMs), we found that communities experiencing population growth showed significant improvements in socioeconomic indicators: for example, a 50% population increase in Northeast metropolitan non-coastal areas was associated with a $10,062 rise [95% confidence interval (CI) = $9,181, $10,944] in median household income. Conversely, areas with population decline faced increasing challenges to community composition: communities experiencing a 50% population decline in West coastal metropolitan areas saw their median age increase by 2.556 years (95% CI = 2.23, 2.89 years), indicating an accelerated aging population. We observed a positive relationship between population growth and local economic growth, with areas experiencing population decline or slow growth showing below-average economic growth. While population change alone explained 10.1% of the variance in county-level GDP growth, incorporating sociodemographic shifts alongside population change using a partial least squares regression (PLSR) more than doubled the explanatory power to 21.4%. Overall, we often found the strength of relationships and sometimes the direction varied by geographic context: coastal areas showed distinct patterns from inland regions, and metropolitan areas responded differently than rural ones. For instance, the percentage of owner-occupied housing was negatively associated with population growth in metropolitan areas, but positively associated in non-metropolitan areas. Our research provides valuable insights for policymakers and planners working to address community changes, particularly in the context of anticipated climate-induced migration. The results suggest that strategies for maintaining economic vitality need to consider not just population retention, but also demographic profiles and socioeconomic opportunities across different geographic contexts.

COVID-19 mortality among kidney transplant candidates is strongly associated with social determinants of health.

The Czech Republic is typical for its fragmented settlement structure with a high number of small self-governing municipalities. Such a distinction causes many problems, including a non-effective way of their management. Even though various integration methods have already been applied in Europe for several decades, the Czech Republic, due to its historical development influenced by the period of communism, is still looking for intermunicipal cooperation opportunities at a higher (metropolitan) level. The presented text aims to evaluate a municipalities’ willingness to cooperate on particular activities and participate in selected topics on strategic and spatial planning at a metropolitan level. Their willingness is analysed according to the population size category of municipalities so that diverse approaches towards the willingness to cooperate could be identified for municipalities in different categories. The results are demonstrated by the example of the Brno Metropolitan Area, which is regarded to be a leader in the development of inter-municipal cooperation at a metropolitan level in the Czech Republic. The results were obtained from a questionnaire survey carried out with the mayors of the Brno metropolitan area in 2020. From a total of 184 municipalities, 175 municipalities took part in the questionnaire survey. Thus, the return was 95%. The results show that the assumption of the importance of the municipality population size is significant when making decisions about future development and cooperation within a metropolitan area. Although most municipalities in all size categories consider it useful to engage in cooperation and solve selected topics together at a metropolitan level, the municipalities in the smallest size categories of up to 1,000 inhabitants had a significantly lower proportion of very positive responses (definitely yes) than municipalities in the other categories. This fact may be attributed to the specific development of the Czech settlement system and the so-called historical memory of the residents from small municipalities during the directive merger in the 1970s and 1980s.

Effective planning for natural disasters requires accurate local data relevant to disaster mitigation efforts. However, data on topics such as individual risk perceptions or household preparedness behaviors are often unavailable at the spatial or temporal resolution necessary for local mitigation efforts. To address these data needs, this paper presents new nationwide estimates of one element of household preparedness — setting aside supplies in the home to use in case of a disaster — across states and metropolitan areas in the United States. Using national online survey data collected in 2015, a multilevel regression and poststratification (MRP) model was fit using demographic, social, and geographic predictors. The resulting model estimates are presented for all U.S. states and all 917 metropolitan and micropolitan areas. Estimates were externally validated against representative surveys at the state and metropolitan level, including data from the 2013 American Housing Survey. Validation indicated that model estimates have low absolute errors and are strongly correlated with external data. Comparing the estimates against historical disaster losses demonstrates broad variation in preparedness even among places with historically high rates of death and injury from natural disasters. These findings suggest that cost-effective online data collection techniques combined with MRP methods can be an effective tool for researchers and decision makers interested in understanding geographic variation in social survey data at subnational scales.