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Friday, April 6 • 3:30pm - 4:00pm
FR3.30.21 Gentrification in Pittsburgh: A Comparison Across Methods and Communities

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Gentrification has attracted a great deal of attention from policymakers, community groups, residents, and the media across cities today. Gentrification is a complicated social and economic process that generally refers to changes in a neighborhood that result in some residents being displaced. The counter is that gentrification is often argued as a means to revitalize urban neighborhoods with new residents and populations, while opponents of gentrification argue that it displaces long-time residents. While much has been written on gentrification over the past thirty years, there has been little research on gentrification or possible gentrification in Pittsburgh. In Pittsburgh, a shrinking city with decades of population loss, gentrification seemed far from most declining neighborhoods in need of revitalization. This poster will reflect our research on an analysis of gentrification in Pittsburgh by employing three different research methods and comparing their results. Though the indicators are similar in these three methods, each includes a slightly different focus to understand neighborhood change and potential gentrification outcomes. The data will focus on demographic conditions and housing market factors, beginning with a baseline year of 2000 and analyzing change through 2015 with a number of secondary datasets. We identify neighborhoods of gentrification in Pittsburgh, with new residents distinguished from long-standing neighborhood residents. This analysis will also identify communities continuing to experience decline and disinvestment. By identifying communities that are vulnerable to rapidly changing demographic and housing prices, policymakers and community groups can better plan for such shifts.


Xiaoying Yu, University of Pittsburgh; Jennifer Bert, University of Pittsburgh; Sabina Deitrick, University of Pittsburgh


Friday April 6, 2018 3:30pm - 4:00pm
Provincial Ballroom (2nd Floor)