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.
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Alejandro, Y., Palafox, L. (2019). Gentrification Prediction Using Machine Learning. In: Martínez-Villaseñor, L., Batyrshin, I., Marín-Hernández, A. (eds) Advances in Soft Computing. MICAI 2019. Lecture Notes in Computer Science(), vol 11835. Springer, Cham. https://doi.org/10.1007/978-3-030-33749-0_16
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DOI: https://doi.org/10.1007/978-3-030-33749-0_16
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