Date of Award


Document Type


Degree Name

Master of City and Regional Planning (MCRP)


School of Architecture

Committee Chair/Advisor

John Gabor, Ph.D., AICP

Committee Member

Caitlin Dyckman, J.D. Ph.D.

Committee Member

L. Enrique Ramos-Santiago, Ph.D.


We are living in an era that necessitates adaptation and resilience. The Earth is warming. Our climate has changed (EPA, 2016). Our planet is also rapidly urbanizing. It is predicted that 68% of people will live in cities by 2050. The City of Greenville is a rapidly growing city in South Carolina that has been losing its tree canopy to development(City of Greenville, 2023). The Urban Tree Canopy (UTC) is a community asset that provides many quality-of-life benefits including improved air quality, stormwater management, carbon sequestration, mental and physical well-being, increased mobility and access, aesthetics, a reduction in energy costs, and increased home values (R. Kaplan, 1989) (Yang et al., 2023)(Ko, 2018)(Ulmer et al., 2016)(Anderson & Cordell, 1988). An intact tree canopy can also reduce the ambient temperature in urban areas, which is increasingly important as global temperatures rise (Schwaab et al., 2021a). Planners have a role in protecting and managing the UTC, to mitigate the effects of climate change. In urban areas, the tree canopy is not always distributed equally across neighborhoods (Volin et al., 2020a). Thus, city planners could benefit from a UTC analysis method to use at the neighborhood scale. Planners can use these analysis tools and methods to address environmental injustice through a more equitable distribution of the tree canopy (Swanson, 2021) to the benefit of the entire city. This study suggests a standardized low-cost method to accurately assess the UTC at the neighborhood scale. First, a qualitative visual analysis was conducted using Google Earth and historical imagery. Next, US Census data and parcel data from the City of Greenville were downloaded from its GIS database to define boundaries and collect demographic and spatial information. Then the UTC percent for each neighborhood was determined with point data using i-Tree Canopy. Next, ANOVA was used to compare UTC percent to the following category data categories in each neighborhood: population density, home value, lot size, race, income, and percent of income spent on housing. The results showed a higher percentage of tree canopy correlated with higher home values and lot sizes amongst the three Greenville neighborhoods.



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