Street-view greenspace distribution across racial/ethnic, neighborhood income, and individual education subgroups
Does the amount of greenspace (trees and grass) you have in your neighborhood depend on a combination of your race, education level, and neighborhood income - rather than just one of these factors alone?
College of Health researcher(s)
Abstract
Background
The maldistribution of greenspaces across Black, Hispanic, and low-income communities can contribute to health disparities. It is unclear whether the interaction of race/ethnicity and socioeconomic status may explain the maldistribution of green-space, or whether the maldistribution varies by type of greenspace.
Methods
Applying deep learning algorithms to street-view images, we calculated percentages of specific types of residential green-space (i.e., %Trees, %Grass) for each Multi-Ethnic Study of Atherosclerosis participant (N = 5,858; 2000–2002). We used multilevel analysis of individual heterogeneity and discriminatory accuracy to quantify inequities in greenspace type by intersecting stratum of race/ethnicity (Black, Chinese American, Hispanic, and White), education (high school, some college, and bachelor’s degree), and neighborhood socioeconomic status (NSES; low, moderate, and high). Models adjusted for age, sex, individual income, and study site.
Results
The mean %Trees was 19.0 (SD 8.8) and the mean %Grass was 5.1 (4.6). Distribution of %Trees varied across strata, for example, 13.1% (95% confidence interval [CI] = 9.1, 23.8) for Hispanic participants in the lowest education and NSES group versus 20.5% (14.0, 30.4) for Hispanic participants in the highest education and NSES group. Patterns were similar among corresponding strata of Black and Chinese American participants. However, the lowest %Trees among White participants was in the highest NSES and education stratum (20.6, 95% CI = 14.8, 31.5). About 16% of the variability of %Trees and 11% of the variability of %Grass was explained by intersecting stratum of race/ethnicity, education, and NSES.
Conclusion
Maldistribution of greenspace types may be explained by combinations of race/ethnicity, education, and NSES sub-groups, as opposed to each factor alone.
Frequently Asked Questions: Greenspace, Equity, and Your Neighborhood
Access to neighborhood greenspace, such as local parks and street trees, is consistently linked to better health and well-being. However, these beneficial environmental features are not always distributed equally, often as a result of historical and ongoing systemic racism. This document addresses common questions about a recent study that used innovative technology to investigate how a person's race, education, and neighborhood socioeconomic status relate to the amount and type of greenspace in their residential area.
What is "greenspace" and why is it important for health?
In this context, greenspace refers to all vegetation found in both natural and urban landscapes. Research has associated greenspace with a wide range of health benefits. It can provide opportunities for physical activity and social interaction while also reducing exposure to negative environmental factors like noise, air pollution, extreme heat, and stress.
What is the core problem the study investigated?
The study investigated the issue of "environmental injustice"—the unequal distribution of beneficial environmental features, like greenspace. This problem is not random; it is rooted in structural racism. As the researchers note, from the late-19th century until the 1960s, practices and policies like Jim Crow laws and redlining were designed to enforce residential segregation. These policies deliberately concentrated marginalized communities in areas of economic and environmental disinvestment, creating inequities that persist today. The study aimed to understand how the unequal spread of greenspace is explained by the intersection of an individual's race/ethnicity and socioeconomic status.
How was this study different from previous research on greenspace?
This study employed two key innovations that set it apart from previous research:
- Technology: Older studies often relied on satellite-based indexes (like NDVI) that could measure overall greenness but couldn't distinguish between different types of vegetation. This study used deep learning algorithms applied to ground-level street-view images, which allowed researchers to identify and measure specific types of greenspace, such as trees versus grass, as experienced by a person on the street.
- Analytical Approach: Rather than examining race, education, and neighborhood socioeconomic status as separate, independent factors, this study used an "intersectional" approach (known as MAIHDA). This method allowed researchers to see how these factors combine to predict an individual's exposure to greenspace, providing a more holistic view of lived experience.
Who participated in this research?
The data came from 5,858 participants in the Multi-Ethnic Study of Atherosclerosis (MESA), a long-term research project. The participants were from six different U.S. communities and self-identified as Black, Chinese American, Hispanic, or White.
What was the main finding about greenspace distribution?
The study's primary conclusion was that the combined, intersecting factors of a person's race/ethnicity, education level, and neighborhood socioeconomic status (NSES) explained a significant portion of the difference in their exposure to residential greenspace. Specifically, these combined social factors accounted for 16% of the variability in exposure to trees and 11% of the variability in exposure to grass.
Did racial and ethnic groups have different levels of greenspace?
Yes, the study found clear disparities. On average, White participants at all education and neighborhood socioeconomic levels had higher exposure to both trees and grass compared to Black, Chinese American, and Hispanic participants.
Did higher socioeconomic status and education always mean more trees?
Not always, which was one of the study's more nuanced findings. For Black, Chinese American, and Hispanic participants, higher levels of education and neighborhood socioeconomic status (NSES) were generally associated with having more trees. However, the study found a surprising and opposite trend for White participants that held only in high-population-density areas: the group with the lowest average percentage of trees was the one in the highest education and NSES category.
Why is it important to analyze different types of greenspace, like trees versus grass?
Distinguishing between vegetation types is critical because different forms of greenspace may offer different health benefits. The study's authors cite other research which found that a higher percentage of trees in a neighborhood was associated with better cardiovascular health, whereas a higher percentage of grass was not found to have the same beneficial association. This suggests that simply increasing "greenness" may be less effective than targeted planting of specific vegetation like trees.
What does it mean that the factors had "additive" versus "interactive" effects?
These terms describe how different social factors combine to influence an outcome. An additive effect means the separate influences of race, education, and neighborhood status simply add up. An interactive effect occurs when these factors combine in more complex ways to produce a unique result. The study found that the unequal distribution of trees was mostly explained by additive effects, while the distribution of grass was explained more by interactive effects. This distinction matters for policy. The additive effects for trees suggest that separately addressing disparities in race, education, and neighborhood income could make a difference. The interactive effects for grass, however, imply that policies might need to be tailored to specific intersectional groups to be effective.
What is the overall conclusion of this research?
The research concludes that the unequal distribution of residential greenspace is driven by complex combinations of social and economic factors rooted in historic and ongoing systemic racism and segregation. The study recommends that efforts to achieve greenspace equity must consider not only the quantity of greenspace but also the specific types of vegetation. Promoting tree planting, in particular, may be a more effective public health strategy than a "one type of greenspace fits all" approach.
Conclusion: Understanding these complex patterns of greenspace distribution is a critical step toward achieving environmental justice and improving public health for all communities.