Dynamic models of gentrification (2410.18004v2)

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.

• The paper demonstrates that high-income agent mobility is the key driver of gentrification in urban neighborhoods.
• It introduces a temporal network-based detection method that outperforms traditional count-based approaches in identifying early gentrification patterns.
• It finds that increased city density correlates with more frequent gentrification events, suggesting urban planning should consider density management.

Dynamic Models of Gentrification: A Agent-based Approach

The paper presents an intricate agent-based model aimed at elucidating the dynamics of urban gentrification, a phenomenon marked by the displacement of lower-income residents due to the influx of wealthier individuals, thereby transforming the socioeconomic landscape of neighborhoods. This investigation explores the mechanisms driving such urban transformations and provides novel quantitative tools to detect gentrification patterns using temporal network measures.

Model Overview

The model simulates a city as a grid with cells representing neighborhoods, each populated by agents divided into low-, middle-, and high-income groups based on real-world income distributions. The interaction dynamics are governed by the propensity to relocate based on socioeconomic conditions. The model assumes low-income agents relocate upon being priced out, middle-income agents move toward neighborhoods with economically similar conditions, and high-income agents seek areas of economic growth to maximize investment returns.

Key Findings

1. Role of High-Income Agents: The paper establishes that the mobility of high-income residents is the primary catalyst for gentrification. Even minimal movements by high-income agents significantly elevate the occurrence of gentrification, highlighting their critical influence on urban dynamics.
2. Network-Based Detection: The introduction of a temporal network-based measure allows for the early detection of gentrification, outperforming traditional count-based methods. This measure assesses the inflow of middle- and high-income individuals and the simultaneous outflow of low-income residents, capturing gentrification patterns before they become apparent through demographic transitions alone.
3. Impact of City Density: The research underscores city density as a facilitator of gentrification, wherein increased population density correlates with a higher frequency of gentrification events. This finding suggests that urban planning and policy interventions could consider density management to mitigate gentrification impacts.

Implications and Future Directions

The findings have substantial implications for urban policy and planning. By offering a robust framework to simulate gentrification and a tool for its early detection, this research provides policymakers with insights to preemptively address the displacement effects associated with gentrification. Furthermore, it opens avenues for incorporating dynamic models in urban studies to simulate the effects of various socioeconomic policies, potentially guiding more equitable urban development strategies.

In future research, expanding the model to account for population growth, migration flows, and diverse urban topographies could provide a more comprehensive understanding of gentrification. Additionally, exploring the effects of multiple property ownership and short-term rentals on neighborhood dynamics could further enhance the model's applicability to contemporary urban challenges.

This paper contributes significantly to the urban studies literature by providing a quantitative and mechanistic perspective on gentrification, employing advanced modeling techniques that yield actionable insights for cities navigating the complex challenges of socioeconomic change.