Developing a machine learning model to map new-build gentrification: A mixed-methods approach.

Abstract

New-build gentrification, a type of gentrification which is connected to newly built development, has radically transformed the appearance of neighborhoods across the United States. However, the literature is lacking discussion on the built component of the new-build gentrification process, which can lead to inaccurate maps and projections of gentrification trends. Recent advancements in machine learning (ML), specifically computer vision models that apply neural network "deep mapping" algorithms, have found application in the research for their ability to track changes in urban streetscapes. In our research, we trained machine learning models to identify new-build development with architectural traits that reflect visual cues of gentrification according to local residents. With Philadelphia as our study area, we drew on the insight of community-based focus groups to identify characteristics that denote new-build gentrification for the city. We compared our audit of new-build gentrification development with municipal permit License and Inspections (L&I) data, using Kernel Density Estimate (KDE) maps to visualize the spatial trends of both datasets. Our final fine-tuned ResNet-50 model achieved an 84.0% test accuracy and an 84.0% Area Under the Curve (AUC) score. Our research contributes a novel mixed-methods approach that integrates community input with Artificial Intelligence (AI) to identify locally-specific gentrification traits.