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Main Authors: Mauro, Giovanni, Pedreschi, Nicola, Lambiotte, Renaud, Pappalardo, Luca
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2410.18004
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author Mauro, Giovanni
Pedreschi, Nicola
Lambiotte, Renaud
Pappalardo, Luca
author_facet Mauro, Giovanni
Pedreschi, Nicola
Lambiotte, Renaud
Pappalardo, Luca
contents The phenomenon of gentrification of an urban area is characterized by the displacement of lower-income residents due to rising living costs and an influx of wealthier individuals. This study presents an agent-based model that simulates urban gentrification through the relocation of three income groups -- low, middle, and high -- driven by living costs. The model incorporates economic and sociological theories to generate realistic neighborhood transition patterns. We introduce a temporal network-based measure to track the outflow of low-income residents and the inflow of middle- and high-income residents over time. Our experiments reveal that high-income residents trigger gentrification and that our network-based measure consistently detects gentrification patterns earlier than traditional count-based methods, potentially serving as an early detection tool in real-world scenarios. Moreover, the analysis also highlights how city density promotes gentrification. This framework offers valuable insights for understanding gentrification dynamics and informing urban planning and policy decisions.
format Preprint
id arxiv_https___arxiv_org_abs_2410_18004
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Dynamic models of gentrification
Mauro, Giovanni
Pedreschi, Nicola
Lambiotte, Renaud
Pappalardo, Luca
Physics and Society
The phenomenon of gentrification of an urban area is characterized by the displacement of lower-income residents due to rising living costs and an influx of wealthier individuals. This study presents an agent-based model that simulates urban gentrification through the relocation of three income groups -- low, middle, and high -- driven by living costs. The model incorporates economic and sociological theories to generate realistic neighborhood transition patterns. We introduce a temporal network-based measure to track the outflow of low-income residents and the inflow of middle- and high-income residents over time. Our experiments reveal that high-income residents trigger gentrification and that our network-based measure consistently detects gentrification patterns earlier than traditional count-based methods, potentially serving as an early detection tool in real-world scenarios. Moreover, the analysis also highlights how city density promotes gentrification. This framework offers valuable insights for understanding gentrification dynamics and informing urban planning and policy decisions.
title Dynamic models of gentrification
topic Physics and Society
url https://arxiv.org/abs/2410.18004