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

The racial/ethnic and income composition of neighborhoods often influences local amenities, including the potential spatial distribution of trees, which are important for population health and community wellbeing, particularly in urban areas. This ecological study used spatial analytical methods to assess the relationship between neighborhood socio-demographic characteristics (i.e. minority racial/ethnic composition and poverty) and tree density at the census tact level in Boston, Massachusetts (US). We examined spatial autocorrelation with the Global Moran's I for all study variables and in the ordinary least squares (OLS) regression residuals as well as computed Spearman correlations non-adjusted and adjusted for spatial autocorrelation between socio-demographic characteristics and tree density. Next, we fit traditional regressions (i.e. OLS regression models) and spatial regressions (i.e. spatial simultaneous autoregressive models), as appropriate. We found significant positive spatial autocorrelation for all neighborhood socio-demographic characteristics (Global Moran's I range from 0.24 to 0.86, all P=0.001), for tree density (Global Moran's I=0.452, P=0.001), and in the OLS regression residuals (Global Moran's I range from 0.32 to 0.38, all P<0.001). Therefore, we fit the spatial simultaneous autoregressive models. There was a negative correlation between neighborhood percent non-Hispanic Black and tree density (rS=-0.19; conventional P-value=0.016; spatially adjusted P-value=0.299) as well as a negative correlation between predominantly non-Hispanic Black (over 60% Black) neighborhoods and tree density (rS=-0.18; conventional P-value=0.019; spatially adjusted P-value=0.180). While the conventional OLS regression model found a marginally significant inverse relationship between Black neighborhoods and tree density, we found no statistically significant relationship between neighborhood socio-demographic composition and tree density in the spatial regression models. Methodologically, our study suggests the need to take into account spatial autocorrelation as findings/conclusions can change when the spatial autocorrelation is ignored. Substantively, our findings suggest no need for policy intervention vis-à-vis trees in Boston, though we hasten to add that replication studies, and more nuanced data on tree quality, age and diversity are needed.

The built environment and depressive symptoms among urban youth: A spatial regression study.

This study evaluated spatial relationships between features of the built environment and youth depressive symptoms. Data used in this study came from the 2008 Boston Youth Survey Geospatial Dataset, which includes Boston high school students with complete residential information (n=1170). Features of the built environment (such as access to walking destinations and community design features) were created for 400- and 800-m street network buffers of the youths' residences. We computed standard Ordinary Least Squares (OLS) regression and spatial simultaneous autoregressive models. We found significant positive spatial autocorrelation in all of the built environment features at both spatial scales (all p=0.001), depressive symptoms (p=0.034) as well as in the OLS regression residuals (all p<0.001), and, therefore, fit spatial regression models. Findings from the spatial regression models indicate that the built environment can have depressogenic effects, which can vary by spatial scale, gender and race/ethnicity (though sometimes in unexpected directions, i.e. associations opposite to our expectations). While our results overall suggest that the built environment minimally influences youth depressive symptoms, additional research is needed, including to understand our results in the unexpected direction.