Date of Award
2024
First Advisor
Amanda Landi
Second Advisor
Jack Burkart
Abstract
Investors seek confidence in upscaling properties, while policymakers need information to prevent displacement in gentrifying communities. In an attempt to better help stakeholders, this thesis focuses on a newfound approach to detecting noticeable changes in neighborhoods. Traditionally, census data has been used to detect trends in known classifiers. This thesis incorporates spatial analysis with the United States Census and green space data to reveal changes not evident from field-sourced data alone. Visual data will be sourced from pairs of historical and current images using the Google Static Street View API in New Jersey. I show the effectiveness and accuracy of my novel approach in predicting gentrification by comparing it with current studies in New Jersey. My framework is capable of enhancing future research in the respective field.
Recommended Citation
Sapun, Justin, "Predicting Neighborhood Gentrification in New Jersey" (2024). Senior Theses. 1696.
https://digitalcommons.bard.edu/sr-theses/1696
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