Journal article
Developing a machine learning model to map new-build gentrification: A mixed-methods approach
PloS one, v 21(1), e0341844
2026
PMID: 41616032
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.
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Details
- Title
- Developing a machine learning model to map new-build gentrification: A mixed-methods approach
- Creators
- Maya Mueller (Corresponding Author) - Drexel UniversityIsaac Quaye - Temple UniversityShengao Yi - University City Science CenterJames Foley - Temple UniversityReeya Shah - Temple UniversityXiaojiang Li - University City Science CenterHamil Pearsall - Temple UniversitySimi Hoque - Drexel University
- Publication Details
- PloS one, v 21(1), e0341844
- Publisher
- PLOS
- Number of pages
- 18
- Grant note
- National Science Foundation: 2312047
This research was supported through a grant from the National Science Foundation Award #2312047 (awarded to SH) and #2312048 (awarded to HP). The url to the funder website is https://www.nsf.gov/ for both awards. The full name of both funders of both awards is the organization of the National Science Foundation. The sponsors and funders did not play a role in the study design, data collection, analysis, decision to publish, nor the preparation of the manuscript.
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Civil, Architectural, and Environmental Engineering
- Web of Science ID
- WOS:001684295500014
- Other Identifier
- 991022157469304721