A small area index of gentrification, applied to New York City

ABSTRACT

This paper presents a small-area index of the multifactorial phenomenon loosely referred to as gentrification, with application to New York City (NYC). The relative change of key input variables (median family income, median rent and proportions of non-Hispanic white, 20–34-year-olds and adults with a 4-year college degree) was computed from the years 2000 to 2016 for NYC census tracts that are spatially normalized to the year 2010. Raw scores derived from principal components analysis were then spatially smoothed through a fully Bayesian conditional autoregressive model, presenting an innovation over previous methods since it essentially blurs the otherwise artificial tract boundaries and results in a simulated posterior distribution of scores for each tract. The median and upper/lower percentiles provide a point estimate and assessment of uncertainty, respectively, of gentrification for each tract. The mapped index is visually consistent with the general understanding of gentrification in NYC, and the index is positively associated with census tract-level change in home values, as measured separately from a real property database. Although we developed this index as the quantitative part of a mixed methods approach to understanding gentrification in New York City, the methodology is intended to be applicable in other cities.

KEYWORDS:

• Gentrification
• gentrification index
• principal components analysis
• Bayesian conditional autoregressive model
• New York City
• home value
• housing cost

Acknowledgments

This research was supported by the City University of New York Interdisciplinary Research Grant number 2519.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data and codes availability statement

The R code and dependent files for replicating this work are available in figshare.com at https://doi.org/10.6084/m9.figshare.11861229 .

The input and output data, along with a data dictionary, are available in figshare.com at DOI: https://doi.org/10.6084/m9.figshare.11862090 .

A kml file of the gentrification index, visualized as pentiles of the spatially smoothed values, is available in figshare.com at https://doi.org/10.6084/m9.figshare.11862258

Additional information

Funding

This work was supported by the City University of New York [IRG 2519].

Notes on contributors

Glen D. Johnson

Glen Johnson is an Associate Professor in the Department of Environmental, Occupational and Geospatial Health Sciences at the City University of New York Graduate School of Public Health and Health Policy. He specializes in quantitative geo-spatial analyses for a variety of public health and environmental applications, with a particular passion for partnering with specialists in urban studies and urban planning for promoting healthy and equitable city living.

Melissa Checker

Melissa Checker is an Associate Professor of Urban Studies at Queens College and of Anthropology and Environmental Psychology at the CUNY Graduate Center. Her research focuses on environmental justice, urban sustainability in the United States, the social justice implications of the green economy, and grassroots activism.

Scott Larson

Scott Larson is a lecturer in the Urban Studies Department at Queens College, the City University of New York. His research focuses on urban space and social justice, including issues of redevelopment, gentrification and community-led urbanism.

Hanish Kodali

Hanish Kodali is a PhD candidate in Epidemiology and Biostatistics at the City University of New York Graduate School of Public Health and Health Policy, where he is currently a research assistant. Building upon a clinical background as a trained physician from India, he currently investigates non-traditional risk factors for chronic disease.