Abstract
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.
Highlights
A multitude of physical, ecological, social, aesthetic and economic benefits of trees have been widely recognized and documented (Dwyer et al, 1992; Tyrväinen et al 2005; Sarajevs, Spatial Demography 2014 2(1): 1-29
We sought to examine the relationship between neighborhood sociodemographic characteristics and the spatial distribution of trees using a spatial perspective
We found significant positive spatial autocorrelation in the neighborhood sociodemographic characteristics, tree density and in the ordinary least squares (OLS) regression residuals
Summary
A multitude of physical, ecological, social, aesthetic and economic benefits of trees have been widely recognized and documented (Dwyer et al, 1992; Tyrväinen et al 2005; Sarajevs, Spatial Demography 2014 2(1): 1-29. Variations in the composition and configuration of urban trees within and between urban areas can be attributed to the confluence of current and historical conditions of biophysical and sociodemographic factors While biophysical factors, such as temperature, soil and precipitation are important in explaining the differences in the abundance and diversity of particular species of trees among different urban areas, social contextual factors may be more influential in explaining the spatial distribution of trees within specific urban areas (Conway et al, 2011; Luck et al, 2009), especially areas with residential segregation and/or high concentrations of certain racial/ethnic groups. Given the importance of trees and potential inequitable spatial distribution of trees by neighborhood socio-demographic composition, we reviewed the literature on the relationship between the spatial distribution of trees and neighborhood socio-demographic composition
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