Integrating social and ecological data to inform urban green infrastructure planning in the City of Akron, Ohio, USA

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Andrea G. Kornbluh, Ecology, Evolution, and Natural Resources, Rutgers University, New Brunswick, NJ

Background/Question/Methods
The urban forest contributes to environmental quality through ecosystem services provided by street trees and green spaces. Research methods are being developed to examine whether urban trees also directly confer human health benefits. The pursuit of health equity demands that we consider economic and demographic factors when siting urban green spaces. The objective of this study was to complete a preliminary land cover assessment of the City of Akron, Ohio, USA and address the questions: Are parkland and canopy cover evenly distributed across neighborhoods? Are there relationships between demographic factors, park locations, and expected tree benefits which can inform green infrastructure planning? Using 2014 US Census data, I examined population structure at the neighborhood scale favored in municipal planning. I digitized Akron’s neighborhoods (n=24) and performed a land cover classification and tree benefit analysis with iTree Canopy, then imported the resulting shapefiles into Google Earth to calculate area and evaluate the distribution of parks (n=108). I used cluster analysis and principal components analysis (PCA) to group neighborhoods with similar land cover composition and overlaid this result on maps of income and population density. All results were integrated with a scorecard to prioritize neighborhoods for green infrastructure planning.
Results/Conclusions
The number of parks was 0–16 per neighborhood, or 0.0–24.3% of total area. Canopy cover ranged from 3.0-56.0%, with a mean of 27.9% (SD 12.3%). There was no relationship between these two variables or between either variable and household income, but there was a weak negative correlation between park area and population density (r=–0.478). A cluster analysis of the land cover classification partitioned the neighborhoods into 5 groups. Locating the groups on income and population density gradient maps revealed general patterns – such as low-income neighborhoods tend to be more developed and high-income neighborhoods greener, with notable exceptions – but did not suggest bias in park placement. Prioritization for future green infrastructure was based on hypothesized indicators of quality of life (median household income, population density, canopy cover, and amount of existing parkland); 10 neighborhoods were selected for tree planting and/or new parks. However, in the absence of clear relationships between available demographic data and land cover composition, the question of health equity remains open. Future research would benefit from collaboration with social scientists to obtain health outcomes data and document human perception and use of green spaces at the neighborhood scale.