Gentrification Prediction Using Machine Learning

Alejandro, Yesenia; Palafox Novack, Leon Felipe

doi:10.1007/978-3-030-33749-0_16

Gentrification Prediction Using Machine Learning

Journal

Advances in Soft Computing

Lecture Notes in Computer Science

ISSN

0302-9743

1611-3349

Date Issued

2019

Author(s)

Alejandro, Yesenia

Palafox Novack, Leon Felipe

Facultad de Ingeniería - CampCM

Type

text::book::book part

DOI

10.1007/978-3-030-33749-0_16

URL

https://scripta.up.edu.mx/handle/20.500.12552/4170

Abstract

Gentrification is a problem in big cities that confounds economic, political and population factors. Whenever it happens, people in the higher brackets of income replace people of low income. This replacement generates population displacement, which force people to change their lives radically. In this work, we use Classification Trees to generate an index, which will indicate the likelihood for a neighborhood to gentrify. This index uses many population variables that include things like age, education and transportation. This system can be used later to inform decisions regarding urban housing and transportation. We can prevent areas of the city of overflowing with private investment in lieu of public housing policy that allows people to stay in their places of living. We expect this work to be a stepping zone on working towards a generalization of gentrification effects in different cities in the world. © Springer Nature Switzerland AG 2019.