Authors: Nicholas Finio*, University of Maryland, Elijah Knaap, Postdoctoral Scholar, University of California Riverside
Topics: Urban and Regional Planning, Urban Geography, United States
Keywords: gentrification, urban planning, demographics
Session Type: Paper
Start / End Time: 3:55 PM / 5:35 PM
Room: Washington 5, Marriott, Exhibition Level
Presentation File: No File Uploaded
Gentrification is the process by which middle class families move into urban areas, causing increases in property values, and displacement of other groups (Lees et al. 2008). Brown Saracino (2017) noted that “scholars... restrict gentrification to parameters... in the central city.” This focus on urban areas in research has yielded focus on neighborhoods within central cities. We expand the geographic and temporal focus of gentrification research into the suburbs using novel spatial and temporal methods. We identify gentrification and show how it is occurring in neighborhoods both inside and outside of central cities. We argue that gentrification is a process of neighborhood change which can occur in any neighborhood. We identify gentrification in the 25 largest US metropolitan areas from 1980-2015 at the census tract level. A variety of neighborhoods have experienced neighborhood change, or upgrading, that meets qualifications for gentrification. While many of these neighborhoods are in the revitalized, central places associated with the “back-to-the city movement” (Smith, 1979), many others are in neighborhoods outside of central cities. We classify our data into central city or suburban based on established methodology using political boundaries (Hanlon & Vicino, 2008), then utilize an unsupervised machine learning algorithm known as affinity propagation to identify gentrification via four variables: race, income, home prices, and education. We conclude that after a slow start in the 20th century suburban gentrification has accelerated in certain metropolitan areas in the 21st. We also show that the spatial pattern of gentrification is vastly different across different metropolitan areas.