Authors: Karen Chapple*, UC-Berkeley
Topics: Urban Geography, Geographic Information Science and Systems, Urban and Regional Planning
Keywords: neighborhood change, user-generated data, GIS, gentrification
Session Type: Paper
Start / End Time: 1:10 PM / 2:50 PM
Room: Washington 3, Marriott, Exhibition Level
Presentation File: No File Uploaded
Researchers have long struggled in using secondary census data to measure neighborhood change, specifically in the form of gentrification and displacement. Though some researchers have devised typologies of neighborhood change that predict future transformation, for instance using machine learning to assign gentrification risk to neighborhoods, their predictive power remains questionable, perhaps in part because of the use of census data that is out-of-date or unreliable at a fine geographic scale (Chapple and Zuk, 2016; Reades, De Souza and Hubbard, 2018). Moreover, most analyses focus on gentrification rather than displacement, due to the greater ease of measuring the influx of capital and/or high-educated newcomers than forced moves or exclusion.
Might real-time data on activity patterns improve the accuracy of these models by pinpointing the areas of dynamic change? In this paper we develop typologies of neighborhood change in the 2000s for four regions: the San Francisco Bay Area, New York, Buenos Aires, and Bogota. After identifying areas which have not gentrified, but are considered at risk (based on loss of low-income households and increase in property values), we validate and refine the models using geotagged tweets from 2012 to 2015 (based on the methodology in Shelton, Poorthuis, and Zook 2015). We expect to find that real-time geographic information refines and narrows the set of neighborhoods considered at risk for gentrification and displacement via conventional data. We conclude by exploring ways to incorporate these data into our understanding of neighborhood change – and policy-making for cities -- on a more real-time basis.