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

## TL;DR

This paper uses machine learning and community input to map new-build gentrification in Philadelphia, improving accuracy and local relevance.

## Contribution

The study introduces a mixed-methods approach combining community insights with AI to identify gentrification traits specific to a local context.

## Key findings

- The fine-tuned ResNet-50 model achieved 84.0% test accuracy and 84.0% AUC score in identifying new-build gentrification traits.
- Community-based focus groups helped identify architectural cues of gentrification specific to Philadelphia.
- Kernel Density Estimate maps revealed spatial trends aligning community insights with municipal permit data.

## 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.