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Urban green infrastructure like public trees can deliver ecosystem services that help cities respond to modern environmental challenges like pollution, climate change, public health, crime, equitability, and biodiversity loss. However, studies have shown that urban trees are inequitably distributed along various socioeconomic variables, translating to inequitable provisioning of ecosystem services. This research aims to explore how street trees are distributed across Washington, DC, USA, as a function of social demographics and urban landscape features. Using data from DC’s Urban Forestry Street Trees dataset, we fit a series of both linear and multi-scale geographically weighted regression models to assess patterns in overall tree canopy cover, native species diversity, oak abundance, and recent street tree plantings. Both linear and MGWR models agreed on the direction of correlation between all dependent and independent variables. However, our MGWR models revealed spatial variability in some results. For example, we found that new tree plantings had a positive relationship with the proportion of renters in some localized areas but a negative relationship in other portions of the city, indicating that areas with high renting populations are not equally receiving new trees across the city. We also found a positive relationship with new tree plantings and heat sensitivity index and this relationship was generally uniform across the city, indicating that new tree plantings are being conducted in areas that need them the most. Our analysis can help identify local neighborhoods in which street tree inequalities exist and inform planting efforts that prioritize the equitable distribution of green infrastructure across Washington, DC.
Nadler et al. (Fri,) studied this question.