Dynamic models of gentrification

TL;DR

This paper introduces an agent-based model of urban gentrification that uses a temporal network measure to detect gentrification patterns earlier than traditional methods, providing insights for urban planning.

Contribution

The study develops a novel agent-based simulation incorporating economic and sociological theories, and introduces a network-based measure for early gentrification detection.

Findings

High-income residents trigger gentrification.

Network-based measure detects gentrification earlier than count-based methods.

City density promotes gentrification.

Abstract

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.