A spatially explicit approach to the study of socio-demographic inequality in the spatial distribution of trees across Boston neighborhoods

Everyday discrimination and satisfaction with nature experiences

Introduction

There is growing interest in creating public green spaces to promote health. Yet, discussions about these efforts often overlook how experiences of chronic discrimination-which may manifest as racism, sexism, or homophobia, and more-could undermine satisfaction with nature experiences.

Methods

Using data from the 2018 wave of the National Opinion Research Center (NORC) General Social Survey (GSS), we quantified associations of frequency of everyday discrimination, operationalized using the Everyday Discrimination Scale (EDS, the primary independent variable), with respondents' perceptions of nature experiences and with their reported time spent in nature. Specifically, we quantified associations with the following three variables: (1) dissatisfaction with day-to-day experiences of nature, (2) not spending as much time as they would like in natural environments, and (3) usually spending at least one day per week in nature. We used survey-weighted robust Poisson models to estimate overall associations, and also stratified analyses by racial/ethnic and gender identity categories.

Results

Of 768 GSS respondents, 14% reported dissatisfaction with nature experiences, 36% reported not spending as much time as they would like in nature, and 33% reported that they did not spend at least one day per week in nature. The median non-standardized EDS, coded such that a higher value indicates greater frequency of discrimination, was 11 (interquartile range: 8, 15). Prevalence of reporting dissatisfaction with day-to-day experiences in nature was 7% higher in association with every one unit increase in EDS score above the median (PR: 1.07, 95% CI: 1.02-1.11). The prevalence of reporting not spending as much time as one would like in nature was 2% higher for every unit increase in higher than median everyday discrimination frequency (PR: 1.02, 95% CI: 1.00-1.05). Higher than median frequency in everyday discrimination was not associated with spending less than one day per week in nature. Race/ethnicity and gender identity did not modify associations.

Conclusion

Greater frequency of everyday discrimination is associated with less satisfaction with experiences in nature. This relationship could undermine efforts to promote health equity through green interventions.

Neighborhood segregation, tree cover and firearm violence in 6 U.S. cities, 2015-2020

Neighborhood segregation by race and income is a structural determinant of firearm violence. Addressing green space deficits in segregated neighborhoods is a promising prevention strategy. This study assessed the potential for reducing firearm violence disparities by increasing access to tree cover. Units of analysis were census tracts in six U.S. cities (Baltimore, MD; Philadelphia, PA; Richmond, VA; Syracuse, NY; Washington, DC; Wilmington, DE). We measured segregation using the index of concentration at the extremes (ICE) for race-income. We calculated proportion tree cover based on 2013-2014 imagery. Outcomes were 2015-2020 fatal and non-fatal shootings from the Gun Violence Archive. We modeled firearm violence as a function of ICE, tree cover, and covariates representing the social and built environment. Next, we simulated possible effects of "tree equity" programs, i.e., raising tract-level tree cover to a specified baseline level. In our fully-adjusted model, higher privilege on the ICE measure (1 standard deviation, SD) was associated with a 42% reduction in shootings (incidence rate ratio (IRR) = 0.58, 95% CI [0.54 0.62], p < 0.001). A 1-SD increase in tree cover was associated with a 9% reduction (IRR = 0.91, 95% CI [0.86, 0.97], p < 0.01). Simulated achievement of 40% baseline tree cover was associated with reductions in firearm violence, with the largest reductions in highly-deprived neighborhoods. Advancing tree equity would not disrupt the fundamental causes of racial disparities in firearm violence exposure, but may have the potential to help mitigate those disparities.

The tree cover and temperature disparity in US urbanized areas: Quantifying the association with income across 5,723 communities

Urban tree cover provides benefits to human health and well-being, but previous studies suggest that tree cover is often inequitably distributed. Here, we use National Agriculture Imagery Program digital ortho photographs to survey the tree cover inequality for Census blocks in US large urbanized areas, home to 167 million people across 5,723 municipalities and other Census-designated places. We compared tree cover to summer land surface temperature, as measured using Landsat imagery. In 92% of the urbanized areas surveyed, low-income blocks have less tree cover than high-income blocks. On average, low-income blocks have 15.2% less tree cover and are 1.5⁰C hotter than high-income blocks. The greatest difference between low- and high-income blocks was found in urbanized areas in the Northeast of the United States, where low-income blocks in some urbanized areas have 30% less tree cover and are 4.0⁰C hotter. Even after controlling for population density and built-up intensity, the positive association between income and tree cover is significant, as is the positive association between proportion non-Hispanic white and tree cover. We estimate, after controlling for population density, that low-income blocks have 62 million fewer trees than high-income blocks, equal to a compensatory value of $56 billion ($1,349/person). An investment in tree planting and natural regeneration of $17.6 billion would be needed to close the tree cover disparity, benefitting 42 million people in low-income blocks.

Examining the distributional equity of urban tree canopy cover and ecosystem services across United States cities

Examining the distributional equity of urban tree canopy cover (UTCC) has increasingly become an important interdisciplinary focus of ecologists and social scientists working within the field of environmental justice. However, while UTCC may serve as a useful proxy for the benefits provided by the urban forest, it is ultimately not a direct measure. In this study, we quantified the monetary value of multiple ecosystem services (ESD) provisioned by urban forests across nine U.S. cities. Next, we examined the distributional equity of UTCC and ESD using a number of commonly investigated socioeconomic variables. Based on trends in the literature, we predicted that UTCC and ESD would be positively associated with the variables median income and percent with an undergraduate degree and negatively associated with the variables percent minority, percent poverty, percent without a high school degree, percent renters, median year home built, and population density. We also predicted that there would be differences in the relationships between each response variable (UTCC and ESD) and the suite of socioeconomic predictor variables examined because of differences in how each response variable is derived. We utilized methods promoted within the environmental justice literature, including a multi-city comparative analysis, the incorporation of high-resolution social and environmental datasets, and the use of spatially explicit models. Patterns between the socioeconomic variables and UTCC and ESD did not consistently support our predictions, highlighting that inequities are generally not universal but rather context dependent. Our results also illustrated that although the variables UTCC and ESD had largely similar relationships with the predictor variables, differences did occur between them. Future distributional equity research should move beyond the use of proxies for environmental amenities when possible while making sure to consider that the use of ecosystem service estimates may result in different patterns with socioeconomic variables of interest. Based on our findings, we conclude that understanding and remedying the challenges associated with inequities requires an understanding of the local social-ecological system if larger sustainability goals are to be achieved.

Under one canopy? Assessing the distributional environmental justice implications of street tree benefits in Barcelona

Street trees are an important component of green infrastructure in cities, providing multiple ecosystem services (ES) and hence contributing to urban resilience, sustainability and livability. Still, access to these benefits may display an uneven distribution across the urban fabric, potentially leading to socio-environmental inequalities. Some studies have analyzed the distributional justice implications of street tree spatial patterns, but generally without quantifying the associated ES provision. This research estimated the amount of air purification, runoff mitigation and temperature regulation provided by circa 200,000 street trees in Barcelona, Spain, using the i-Tree Eco tool. Results were aggregated at neighborhood (n = 73) and census tract (n = 1068) levels to detect associations with the distribution of five demographic variables indicating social vulnerability, namely: income, residents from the Global South, residents with low educational attainment, elderly residents, and children. Associations were evaluated using bivariate, multivariate and cluster analyses, including a spatial autoregressive model. Unlike previous studies, we found no evidence of a significant and positive association between the distribution of low income or Global South residents and a lower amount of street tree benefits in Barcelona. Rather, higher ES provision by street trees was associated with certain types of vulnerable populations, especially elderly citizens. Our results also suggest that street trees can play an important redistributive role in relation to the local provision of regulating ES due to the generally uneven and patchy distribution of other urban green infrastructure components such as urban forests, parks or gardens in compact cities such as Barcelona. In the light of these findings, we contend that just green infrastructure planning should carefully consider the distributive implications associated with street tree benefits.

Location of Pre-exposure Prophylaxis Services Across New York City Neighborhoods: Do Neighborhood Socio-demographic Characteristics and HIV Incidence Matter?

Despite an increasing pre-exposure prophylaxis (PrEP) use among populations at highest risk of HIV acquisition, comprehensive and easy access to PrEP is limited among racial/ethnic minorities and low-income populations. The present study analyzed the geographic distribution of PrEP providers and the relationship between their location, neighborhood characteristics, and HIV incidence using spatial analytic methods. PrEP provider density, socio-demographics, healthcare availability, and HIV incidence data were collected by ZIP-code tabulation area in New York City (NYC). Neighborhood socio-demographic measures of race/ethnicity, income, insurance coverage, or same-sex couple household, were not associated with PrEP provider density, after adjusting for spatial autocorrelation, and PrEP providers were located in high HIV incidence neighborhoods (P < 0.01). These findings validate the need for ongoing policy interventions (e.g. public health detailing) vis-à-vis PrEP provider locations in NYC and inform the design of future PrEP implementation strategies, such as public health campaigns and navigation assistance for low-cost insurance.

The relationship between urban forests and race: A meta-analysis

There is ample evidence that urban trees benefit the physical, mental, and social health of urban residents. The environmental justice hypothesis posits that environmental amenities are inequitably low in poor and minority communities, and predicts these communities experience fewer urban environmental benefits. Some previous research has found that urban forest cover is inequitably distributed by race, though other studies have found no relationship or negative inequity. These conflicting results and the single-city nature of the current literature suggest a need for a research synthesis. Using a systematic literature search and meta-analytic techniques, we examined the relationship between urban forest cover and race. First, we estimated the average (unconditional) relationship between urban forest cover and race across studies (studies = 40; effect sizes = 388). We find evidence of significant race-based inequity in urban forest cover. Second, we included characteristics of the original studies and study sites in meta-regressions to illuminate drivers of variation of urban forest cover between studies. Our meta-regressions reveal that the relationship varies across racial groups and by study methodology. Models reveal significant inequity on public land and that environmental and social characteristics of cities help explain variation across studies. As tree planting and other urban forestry programs proliferate, urban forestry professionals are encouraged to consider the equity consequences of urban forestry activities, particularly on public land.

The relationship between urban forests and income: A meta-analysis

Urban trees provide substantial public health and public environmental benefits. However, scholarly works suggest that urban trees may be unequally distributed among poor and minority urban communities, meaning that these communities are potentially being deprived of public environmental benefits, a form of environmental injustice. The evidence of this problem is not uniform however, and evidence of inequity varies in size and significance across studies. This variation in results suggests the need for a research synthesis and meta-analysis. We employed a systematic literature search to identify original studies which examined the relationship between urban forest cover and income (n=61) and coded each effect size (n=332). We used meta-analytic techniques to estimate the average (unconditional) relationship between urban forest cover and income and to estimate the impact that methodological choices, measurement, publication characteristics, and study site characteristics had on the magnitude of that relationship. We leveraged variation in study methodology to evaluate the extent to which results were sensitive to methodological choices often debated in the geographic and environmental justice literature but not yet evaluated in environmental amenities research. We found evidence of income-based inequity in urban forest cover (unconditional mean effect size = 0.098; s.e. = .017) that was robust across most measurement and methodological strategies in original studies and results did not differ systematically with study site characteristics. Studies that controlled for spatial autocorrelation, a violation of independent errors, found evidence of substantially less urban forest inequity; future research in this area should test and correct for spatial autocorrelation.

Remote sensing-based measurement of Living Environment Deprivation: Improving classical approaches with machine learning

This paper provides evidence on the usefulness of very high spatial resolution (VHR) imagery in gathering socioeconomic information in urban settlements. We use land cover, spectral, structure and texture features extracted from a Google Earth image of Liverpool (UK) to evaluate their potential to predict Living Environment Deprivation at a small statistical area level. We also contribute to the methodological literature on the estimation of socioeconomic indices with remote-sensing data by introducing elements from modern machine learning. In addition to classical approaches such as Ordinary Least Squares (OLS) regression and a spatial lag model, we explore the potential of the Gradient Boost Regressor and Random Forests to improve predictive performance and accuracy. In addition to novel predicting methods, we also introduce tools for model interpretation and evaluation such as feature importance and partial dependence plots, or cross-validation. Our results show that Random Forest proved to be the best model with an R2 of around 0.54, followed by Gradient Boost Regressor with 0.5. Both the spatial lag model and the OLS fall behind with significantly lower performances of 0.43 and 0.3, respectively.

The role of neighborhood characteristics in racial/ethnic disparities in type 2 diabetes: results from the Boston Area Community Health (BACH) Survey

Racial/ethnic disparities in the prevalence of type 2 diabetes mellitus (T2DM) are well documented and until recently, research has focused almost exclusively on individual-based determinants as potential contributors to these disparities (health behaviors, biological/genetic factors, and individual-level socio-demographics). Research on the role of neighborhood characteristics in relation to racial/ethnic disparities in T2DM is very limited. Therefore, the aim of this research is to identify and estimate the contribution of specific aspects of neighborhoods that may be associated with racial/ethnic disparities in T2DM. Data from the Boston Area Community Health III Survey (N = 2764) was used in this study, which is a community-based random-sample survey of adults in Boston, Massachusetts from three racial/ethnic groups (Black, Hispanic, and White). We applied two-level random intercepts logistic regression to assess the associations between race/ethnicity, neighborhood characteristics (census tract socioeconomic status, racial composition, property and violent crime, open space, geographic proximity to grocery stores, convenience stores, and fast food, and neighborhood disorder) and prevalent T2DM (fasting glucose > 125 mg/dL, HbA1c ≥ 6.5%, or self-report of a T2DM diagnosis). Black and Hispanic participants had 2.89 times and 1.48 times the odds of T2DM as White participants, respectively. Multilevel models indicated a significant between-neighborhood variance estimate of 0.943, providing evidence of neighborhood variation. Individual demographics (race/ethnicity, age and gender) explained 22.3% of the neighborhood variability in T2DM. The addition of neighborhood-level variables to the model had very little effect on the magnitude of the racial/ethnic disparities and on the between-neighborhood variability. For example, census tract poverty explained less than 1% and 6% of the excess odds of T2DM among Blacks and Hispanics and only 1.8% of the neighborhood variance in T2DM. While the findings of this study overall suggest that neighborhood factors are not a major contributor to racial/ethnic disparities in T2DM, further research is needed including data from other geographic locations.