Measuring Gentrification: a Data Driven Approach

• Activity Number: 86 - SPEED: Data Challenge Part 2
• Type: Contributed
• Date/Time: Sunday, July 28, 2019 : 4:00 PM to 4:45 PM
• Sponsor: Government Statistics Section
• Author(s): Steven Stier* and Hend Aljobaily and Kofi Wagya and Michael Oduro-Safo
• Companies: and University of Northern Colorado and University of Northern Colorado and University of Northern Colorado
• Keywords: machine learning; big data; gentrification; New York

Abstract

Gentrification is a term that has come to be viewed by many as a corrupt practice that allows for the destruction of poor and oppressed communities. The term officially means the improving or development of a neighborhood. Gentrification usually begins with the arrival of wealthier people in an existing urban district, a related increase in rent and property values, and changes in the district's character and culture. The positive side to these changes is that gentrification has been proven to be related to reduced crime and increased economic activities in the gentrified neighborhood. The negative and often ignored result of the changes is the displacement of poor communities living in the gentrified neighborhood. The reality is that the effects of gentrification are complex, and the real impact varies from one community to another. Gentrification is currently a widespread practice in the United Stated, especially in large and densely populated cities such as New York. In this study, a measurement for gentrification is created to show how it has affected and continues to affect the residents of New York City using the NYCHVS dataset. Recommendations for implementation are also gi