Search Results (263)

While there are studies on bicycle crashes, no study has investigated the spatial analysis of fatal and injury bicycle crashes in the state of Ohio. This study fills this gap in the literature by mapping and investigating the bicycle crash-prone areas in the state. It analyzes fatal and injury bicycle crashes from 2014 to 2023 by utilizing four exploratory spatial data analysis techniques: nearest neighbor index, global Moran’s I index, hotspot and cold spot analysis, and local Moran’s I index at the state, county, census tract, and block group levels. Results vary slightly across techniques and spatial scales but consistently show that bicycle crash locations are clustered statewide, particularly in the state’s major metropolitan areas such as Columbus, Cincinnati, Toledo, Cleveland, and Akron. These urban centers have emerged as hotspots, indicating a higher vulnerability to bicycle crashes. While global Moran’s I analysis at the county level does not reveal significant spatial autocorrelation, a strong positive autocorrelation is observed at both the census tract (p = 0.01) and block group (p = 0.00) levels, indicating significant high clustering, signifying that finer geographical units yield more robust results. Identifying specific hotspots and vulnerable areas provides valuable insights for policymakers and urban planners to implement effective safety measures and improve conditions for non-motorized road users in Ohio. The study highlights the need for targeted mitigation strategies in high-risk areas, including comprehensive safety measures, infrastructure improvements, policy changes, and community-focused initiatives to reduce crash risk and create safer environments for cyclists throughout Ohio’s urban fabric. Full article

Zoning policies play a critical role in shaping the geography of urban and suburban development in the United States. Using data from the National Zoning and Land-Use Database and tract-level census data from 42 Metropolitan Statistical Areas, we classify metros into four zoning regime types based on the relative restrictiveness of urban and suburban land-use policies and compare trends in population growth and neighborhood change across these regimes. Our findings show that suburban areas have outpaced urban cores in population growth across all zoning configurations, with the most pronounced growth occurring in metros where restrictive urban zoning coexists with permissive suburban regulation. This growth is disproportionately concentrated in affluent suburban neighborhoods, suggesting a spatial sorting of access to resources and amenities. We also find that urban–suburban gentrification gaps are the smallest in these asymmetrical zoning regimes, suggesting that permissive suburban land use may facilitate spillover effects from constrained cores. These findings suggest that zoning asymmetries shape not only the geography of growth but also the spatial dynamics of gentrification. We argue for a metropolitan perspective on land-use governance to better understand the interconnected nature of suburbanization and the spatial expansion of gentrification. Full article

This study investigates the critical link between child care accessibility and local labor force participation, addressing a gap in current research that often lacks local spatial granularity. While over half of the U.S. population resides in child care deserts, disproportionately affecting rural, low-income, and minority communities, the economic implications for local labor markets remain underexplored. Leveraging Michigan child care license data and Census tract-level demographic and employment characteristics, this research employs a spatial econometric approach to estimate the impact of geographic distance to child care facilities on labor supply using descriptive data. Our findings consistently demonstrate that increased distance to child care is significantly associated with reduced labor force participation. While female labor force participation is lower in areas with constrained access to child care, we also found that households with two parents are also less likely to have full labor force participation when access to child care is constrained. The cost-effective framework used here can be replicated to identify specific communities most impacted by child care-related employment disruptions. The analytical findings can be instrumental in targeting and prioritizing child care policy interventions. Full article

Geographic disparities threaten equitable access for children to health-promoting safe green spaces, and quality early education in the communities in which they live and grow. To address gaps in the field, we integrated the fields of developmental psychology, public health, and environmental science to examine, at the population level, associations between the environmental burden, socioeconomic vulnerability, and kindergarten readiness in a diverse urban county. Three administrative datasets were integrated through an early childhood data sharing research partnership in Miami-Dade County. The Bruner Child Raising Vulnerability Index, the five domains of the Environmental Burden module from the Environmental Justice Index, and public school kindergarten readiness scores were aggregated at the census tract level. Analysis of variance and multiple regression analyses found associations between socioeconomic vulnerability and race/ethnicity. The socioeconomic vulnerability levels were highest in census tracts with a higher percentage of Black residents, compared to all other races/ethnicities. Areas of greater social vulnerability had lower kindergarten readiness and a higher environmental burden. A higher environmental burden predicted lower kindergarten readiness scores above and beyond race/ethnicity and socioeconomic vulnerability. The findings advance our understanding of global challenges to sustainable healthy child development, such as the persistence of a disproportionate environmental burden and inequitable access to resources such as green spaces and early education programs. The present study results can inform community health improvement plans to reduce risk exposures and promote greater access to positive environmental and educational resources for all children. Full article

Rapid population growth and urbanization have greatly impacted the environment, causing a sharp rise in city temperatures—a phenomenon known as the Urban Heat Island (UHI) effect. While previous research has extensively examined the influence of land use characteristics on urban heat islands, their impact on community demographics and UHI severity remains unexplored. Moreover, most previous studies have focused on specific locations, resulting in relatively homogeneous environmental data and limiting understanding of variations across different areas. To address this gap, this paper develops ensemble learning models to predict UHI severity based on demographic, meteorological, and land use/land cover factors in Midwestern United States. Analyzing over 11,000 data points from urban census tracts across more than 12 states in the Midwestern United States, this study developed Random Forest and XGBoost classifiers achieving weighted F1-scores up to 0.76 and excellent discriminatory power (ROC-AUC > 0.90). Feature importance analysis, supported by a detailed SHAP (SHapley Additive exPlanations) interpretation, revealed that the difference in vegetation between urban and rural areas (DelNDVI_summer) and imperviousness were the most critical predictors of UHI severity. This work provides a robust, large-scale predictive tool that helps urban planners and policymakers identify key UHI drivers and develop targeted mitigation strategies. Full article

Most health studies use cross-sectional data to examine neighborhood context because of the difficulty of collecting and analyzing longitudinal data; this prevents an examination of historical trends that may influence health outcomes. Using the Neighborhood Change Database, we categorized longitudinal (1990–2010) poverty and White concentration trajectories (long-term low, long-term moderate, long-term high, increasing, or decreasing) for Texas census tracts and linked them to tract-level health-related characteristics (social determinants of health [SDOH] in 2010, health risk and preventive behaviors [HRPB] in 2017, and health status/outcomes [HSO] in 2017) from multiple sources (N = 2961 tracts). We conducted univariate and bivariate descriptive analyses, followed by linear regressions adjusted for population density. SDOH, HRPB, and HSO measures varied widely across census tracts. Both poverty and White concentration trajectories were strongly and consistently associated with a wide range of SDOH. Long-term high-poverty and low-White tracts showed the greatest disadvantages, while long-term low-poverty and high-White tracts had the most advantages. Neighborhoods undergoing changes in poverty or White concentrations, either increasing or decreasing, had less advantageous SDOH compared with long-term low-poverty or long-term high-White neighborhoods. While associations between poverty, White concentration trajectories, and SDOH were consistent, those with HRPB and HSO were less so. Understanding impact of the relationships between longitudinal neighborhood poverty and racial/ethnic composition on health can benefit stakeholders designing policy proposals and intervention strategies. Full article

The COVID-19 pandemic is a catastrophic event highlighting numerous health disparities. The social vulnerability index (SVI) has been widely utilized in COVID-19 research to assess vulnerable communities and to examine how social determinants influence various COVID-19 outcomes. This population-based study aims to determine whether COVID-19 testing and vaccination rates mediate the relationship between the SVI and COVID-19 prevalence. Mediation analysis was conducted using data from 535 census tracts in Clark County, Nevada. Findings indicate that COVID-19 testing rates were lower in areas with high SVI scores, potentially leading to more undetected cases. Moreover, COVID-19 testing, full vaccination, and follow-up vaccination rates significantly mediated the relationship between SVI and COVID-19 prevalence. These results suggest that greater location-based social vulnerability is associated with a sequential pathway of reduced testing and vaccination rates, contributing to underreported COVID-19 cases. Full article

This study investigates the relationships between air quality, social vulnerability, and health outcomes at the census tract-level in Harris County, Texas. Spatial and regression analyses were conducted using sociodemographic data, air quality indicators, including PM2.5, diesel particulate matter (DPM), nitrogen dioxide (NO₂), and ozone, and health metrics, such as coronary heart disease, chronic obstructive pulmonary disease (COPD), asthma, and stroke prevalence. The results indicated variability in sociodemographic challenges, air pollution, and health outcomes. Social vulnerability strongly correlated with increased prevalence of respiratory and cardiovascular diseases, notably COPD, asthma, and stroke. The air quality metrics showed significant geospatial variability: PM2.5 and NO₂ were concentrated centrally near transportation corridors, DPM was elevated near eastern industrial regions, and ozone peaked in western parts of the county, potentially due to atmospheric transport and photochemical processes. PM2.5 exposure significantly correlated with increased cardiovascular and respiratory health outcomes, particularly at elevated concentrations. In contrast, ozone demonstrated a plateauing effect, increasing the health risks but with a diminishing impact at higher concentrations. The correlations between social vulnerability and air quality were modest, suggesting homogenous distributions of PM2.5, NO₂, and DPM across socioeconomically diverse areas, whereas ozone exposure slightly increased with higher social vulnerability. The findings pointed to the complexity of spatial relationships between socioeconomic status, air pollution, and health, highlighting the need for additional monitoring and targeted interventions to improve health outcomes in socio-demographically and economically challenged communities. Full article

In the European Union, 75% of the residential building stock is estimated to have energy inefficiencies, which increases the probability of falling into energy poverty. Poor thermal conditions reduce the quality of life of dwelling occupants. Renovating the residential building stock is essential to reduce energy consumption, CO₂ emissions, and energy poverty in cities. This study aims to assess and map the energy efficiency and energy poverty of residential buildings in Seville at the urban district and census tract level. A total of 45,908 dwellings were evaluated using data from the Energy Performance Certificates database and demographic and economic information from national and official databases. The analysis considers dwelling typology, year of construction, average household income, and geographic location at the district and census tract level. The results show that Seville’s residential building stock performs poorly, with 83% and 92% of dwellings rated “E” or lower for energy consumption and CO₂ emissions, respectively. The findings of this GIS-based study help identify urban areas with less efficient buildings and higher energy poverty risk, providing valuable information to develop targeted renovation strategies and reduce the climate impact of Seville’s residential building stock. Full article

Background: The disparities observed in COVID-19 vaccine access at the early stages of vaccine distribution highlight the need for vaccine distribution plans that consider equitable access. Strategies to identify areas with low access to vaccines that use a single pre-specified distance or time as a threshold to define accessibility may not represent reality. We propose a novel mobility data-driven (MDD) definition to identify areas that have low access to the COVID-19 vaccine. Methods: We collected geospatial mobility data for our MDD approach to determine areas of low access. We identified census tracts in Oregon with low access to the COVID-19 vaccine through two approaches—(1) an adapted United States Department of Agriculture (USDA) food desert definition and (2) our proposed MDD framework. Ten spatial and social measures of access were utilized to compare these two approaches. Results: Compered with USDA, low-access census tracts identified by the MDD definition have a lower spatial accessibility; higher rates of poverty, unemployment, uninsured individuals, and a population without high school diplomas; and a low per capita income. Moreover, we found that the proportion of older populations, as well as American Indian and Alaskan Native populations, as identified in the MDD low-access census tracts, is higher than that in the USDA definition. Conclusions: We believe that the new proposed framework using mobility data can identify more representative areas that have low access to COVID-19 vaccines. Our proposed framework provides a starting point for achieving the goal of the equitable distribution of resources. Full article

Alternative Fuel Vehicle (AFV) rebate programs incentivize the transition from fossil fuels to alternative fuels. Unfortunately, research on the people who are rebate program recipients is more evident than research on the places where the programs distribute rebates. To that end, this study retrospectively analyzes rebates in a statewide, AFV rebate program known as the California Vehicle Rebate Program (CVRP), from 2011 to 2022, to explore the statewide distribution of rebates. The specification of novel multilevel models nests rebates within different levels of analysis to control for programmatic income eligibility changes for rebate recipients as well as infrastructural, racial, transactional, environmental, and demographic differences between census tracts. The different levels of analysis include spatial attributes of the CVRP as well as temporal attributes of the CVRP to control for implicit heterogeneity in the outcomes of interest. Results suggest that the CVRP does not distribute rebates to places where infrastructure is accessible, but the CVRP does distribute rebates to places where pollution is burdensome and people are socioeconomically vulnerable. Full article

Our study tests residential segregation as an explanation for biological aging disparities between Black and White Americans. We analyze data from 288 Black and White older-age adults who participated in Wave 6 (2019) of the Americans’ Changing Lives study, a nationally representative cohort of adults in the contiguous United States. Our outcome of interest is epigenetic age acceleration assessed via five epigenetic clocks: GrimAge, PhenoAge, SkinBloodAge, HannumAge, and HorvathAge. Residential segregation is operationalized at the census tract level using the Getis-Ord G_i* statistic and multilevel modeling procedures that adjust for state-level clustering. We uncover three key findings. First, epigenetic age profiles are comparable among White respondents regardless of where they live. Second, Black respondents express roughly three years of accelerated epigenetic age (GrimAge), relative to White counterparts, regardless of where they live. Third, diminished education levels and homeownership rates, coupled with elevated levels of traumatic stress and smoking, explain why Black residents in segregated Black areas exhibit accelerated epigenetic age. However, these factors do not explain why Black respondents living outside segregated Black areas also exhibit epigenetic age acceleration. Our findings suggest residential segregation only partially explains why Black Americans tend to live shorter lives than White Americans. Full article

Environmental justice research in the United States (US) documents greater air pollution exposures for Hispanic/Latino vs. non-Hispanic White groups. EJ research has not focused on the intersection of race/ethnicity and older age nor short-term fine particulate matter (PM_2.5) exposures. We address these knowledge gaps by studying US metropolitan area census tracts within 100 km of the US–Mexico border, a region with serious air quality issues. We use US Census American Community Survey data to construct sociodemographic variables and Environmental Protection Agency Downscaler data to construct long-term and short-term measures of PM_2.5 exposure. Using multivariable generalized estimating equations, we test for differences in PM_2.5 exposures between census tracts with higher vs. lower proportions of older Hispanic/Latino residents and older non-Hispanic White residents. The results indicate that as the proportion of the Hispanic/Latino population ≥ 65 years of age increases, long-term and short-term PM_2.5 exposures significantly increase. In contrast, as the proportion of the non-Hispanic White population ≥ 65 years of age increases, changes in long-term and short-term PM_2.5 exposures are statistically non-significant. These findings illuminate how race/ethnicity and older age intersect in shaping PM_2.5 exposure disparities and may inform efforts to mitigate air pollution exposures for overburdened people along the US–Mexico border. Full article

While multiple federal screening tools have previously been developed for mapping communities facing environmental injustice and health disparities, many states across the United States have seen value in developing state-specific screening tools. This article provides an overview of a recent addition to the list of state screening tools, the Connecticut Environmental Justice Screening Tool (CT EJScreen). CT EJScreen identifies communities disproportionately affected by environmental and socioeconomic burdens at the census tract level. The tool integrates geospatial data on potential pollution sources, exposures, health sensitivities, and socioeconomic factors to produce a cumulative Environmental Justice Index. This article describes the development process of the tool, its methodological framework, the multi-pronged public engagement during the development process, preliminary correlation analyses, lessons learned, and recommendations for future iterations. Spearman correlation and Principal Component Analysis were applied to assess variable relationships and guide indicator refinement. Stakeholder engagement with Connecticut’s environmental justice communities ensured that the tool reflects both quantitative data and lived experiences. CT EJScreen provides important information for policy implementation covering areas such as funding, public health issues, and permitting. The CT EJScreen process might serve as a useful template for other states looking to devise state-specific adjunct screening systems. Full article

This study examines access to rental housing in the metropolitan area of Las Palmas de Gran Canaria, linking it to socio-economic inequalities and the increasing precarization. In recent years, housing affordability has worsened due to rising rents, stagnant wages, and speculative dynamics—particularly those linked to tourism and platform-based economies. Drawing on official data from the State Reference System for Rental Housing Prices (SERPAVI) and income statistics at the census tract level, this research quantifies housing affordability and spatial disparities through indicators such as economic effort rates. The analysis identifies patterns of exclusion and urban fragmentation, showing that large sectors of the population—especially those earning the minimum age—face severe barriers to accessing adequate housing. The findings highlight the insufficiency of current public policies and propose the expansion of social rental housing and stricter rental market regulation as necessary steps to ensure fairer urban conditions. Full article