Dissertations, Theses, and Capstone Projects
Date of Degree
2-2026
Document Type
Master's Capstone Project
Degree Name
Master of Science
Program
Data Analysis & Visualization
Advisor
Matthew Gold
Subject Categories
Anthropological Linguistics and Sociolinguistics | Artificial Intelligence and Robotics | Categorical Data Analysis | Data Science | Demography, Population, and Ecology | Digital Humanities | Environmental Design | Geographic Information Sciences | Human Geography | Photography | Politics and Social Change | Public Affairs | Race and Ethnicity | Regional Economics | Urban, Community and Regional Planning | Urban Studies | Urban Studies and Planning
Keywords
New York City, retail, artificial intelligence, image recognition, semiotics, urban economics
Abstract
Gentrification—broadly, the replacement of a less powerful group by a more powerful one in an urban context—is oft-discussed in the popular press, but its definition is much-debated in the urban planning literature. Furthermore, academic treatments of displacement understandably focus on measurable yet fairly abstract indicators like changes in rent or income, whereas neighborhood change is often registered by residents on the ground using visual, but difficult-to-quantify markers like retail turnover. This project uses image recognition technology on a set of storefront photos to index the visual streetscape of a neighborhood, as well as to track changes to that portrait over time, and presents the findings in an accessible format on the web. The model relies on a binary classification developed by New York City cultural anthropologist Edward Snajdr and sociolinguist Shonna Trinch (2020), wherein colorful, text-heavy “old-school” storefront signage evokes openness, diversity and accessibility, while the spartan, symbolic and glass-laden “new-school” design signals clubbiness, cultural capital and upscale. The neighborhood focus is Bedford-Stuyvesant, a historically Black section of Brooklyn that has lately been in the top three community districts for proportional increases in rent, income, and white share of the population. The model finds a small but significant likelihood that old-school stores have closed, while more newly opened stores reflect the new-school style.
Recommended Citation
McQuilkin, Alexander, "Typeface: Machine-Viewing Gentrification on Storefront Imagery in Bedford-Stuyvesant, Brooklyn" (2026). CUNY Academic Works.
https://academicworks.cuny.edu/gc_etds/6575
bed-stuy-signs-archive.warc.gz (39 kB)
Bed-Stuy-signs-repo.zip (13785 kB)
BKCB3export(Closed_operating_only) with predictions_updated.csv (3856 kB)
Training data.csv (18 kB)
Included in
Anthropological Linguistics and Sociolinguistics Commons, Artificial Intelligence and Robotics Commons, Categorical Data Analysis Commons, Data Science Commons, Demography, Population, and Ecology Commons, Digital Humanities Commons, Environmental Design Commons, Geographic Information Sciences Commons, Human Geography Commons, Photography Commons, Politics and Social Change Commons, Public Affairs Commons, Race and Ethnicity Commons, Regional Economics Commons, Urban, Community and Regional Planning Commons, Urban Studies Commons, Urban Studies and Planning Commons
Comments
Online component: https://www.typeface.nyc/