Environmental epidemiology (Philadelphia, Pa.) · 2025
Street-view greenspace distribution across racial/ethnic, neighborhood income, and individual education subgroups.
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Jenson Tara E, Lin Pi-I Debby, James Peter, Hystad Perry, Diez-Roux Ana V, Coull Brent, Besser Lilah, Suel Esra, Weuve Jennifer, Pescador Jimenez Marcia
Аннотация
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 greenspace, 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 greenspace (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 subgroups, as opposed to each factor alone.
METHODS: Applying deep learning algorithms to street-view images, we calculated percentages of specific types of residential greenspace (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 subgroups, as opposed to each factor alone.