Dynamic models of gentrification
Mauro G., Pedreschi N., Lambiotte R., Pappalardo L.
urban dynamics Gentrification Temporal networks Physics and Society Urban dynamics FOS: Physical sciences Multilayer networks Physics and Society (physics.soc-ph)
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 highlights how city density promotes gentrification. This framework offers valuable insights for understanding gentrification dynamics and informing urban planning and policy decisions.
Source: ADVANCES IN COMPLEX SYSTEM, vol. 28 (issue 06)
[2] Jacobs, J. The Death and Life of Great American Cities (1961).
[3] Pappalardo, L., Manley, E., Sekara, V. & Alessandretti, L. Future directions in human mobility science. Nature computational science 3, 588-600 (2023).
[4] Cornacchia, G. et al. How routing strategies impact urban emissions. In Proceedings of the 30th international conference on advances in geographic information systems, 1-4 (2022).
[5] Lucchini, L. et al. Living in a pandemic: changes in mobility routines, social activity and adherence to covid-19 protective measures. Scientific reports 11, 24452 (2021).
[6] Oliver, N. et al. Mobile phone data for informing public health actions across the covid-19 pandemic life cycle (2020).
[7] Schelling, T. C. Dynamic models of segregation. Journal of mathematical sociology 1, 143-186 (1971).
[8] Gambetta, D., Mauro, G. & Pappalardo, L. Mobility constraints in segregation models. Scientific Reports 13, 12087 (2023).
[9] Moro, E., Calacci, D., Dong, X. & Pentland, A. Mobility patterns are associated with experienced income segregation in large us cities. Nature communications 12, 4633 (2021).
[10] Glass, R. Aspects of change. In The gentrification debates , 19-29 (Routledge, 2013).
[11] Slater, T. Gentrification of the city. the city 571-585 (2011).
[13] LeRoy, S. F. & Sonstelie, J. Paradise lost and regained: Transportation innovation, income, and residential location. Journal of urban economics 13, 67-89 (1983).
[14] Smith, N. Toward a theory of gentrification a back to the city movement by capital, not people. Journal of the American planning association 45, 538-548 (1979).
[15] Smith, N. Gentrification and the rent gap (1987).
[16] Lowry, I. S. Filtering and housing standards: A conceptual analysis. Land Economics 36, 362-370 (1960).
[17] Simpson, J. Planning gentrification. Planning (2017).
[18] Ley, D. Artists, aestheticisation and the field of gentrification. studies 40, 2527-2544 (2003).
[25] Rigolon, A. & Nem´eth, J. Toward a socioecological model of gentrification: How people, place, and policy shape neighborhood change. Journal of Urban Afairs 41, 887-909 (2019).
[26] Reades, J., De Souza, J. & Hubbard, P. Understanding urban gentrification through machine learning. Urban Studies 56, 922-942 (2019).
[27] Gardiner, O. & Dong, X. Mobility networks for predicting gentrification. In Complex Networks & Their Applications IX: Volume 2, Proceedings of the Ninth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2020, 181-192 (Springer, 2021).
[28] Shaw, J. D., Restrepo, J. G. & Rodıgr´ uez, N. A dynamical system model of gentrification: Exploring a simple rent control strategy. arXiv preprint arXiv:2406.12092 (2024).
[29] Christafore, D. & Leguizamon, S. Neighbourhood inequality spillover effects of gentrification. Papers in Regional Science 98, 1469-1485 (2019).
[30] Banabak, S., Kadi, J. & Schneider, A. E. Gentrification and the suburbanization of poverty: evidence from a highly regulated housing system. Urban Geography 1-23 (2024).
[31] Hu, M., Tsang, D. & Wan, W. X. Corporate relocation and housing market spillovers. Management Science (2024).
[32] Mantzaris, A. V. Incorporating a monetary variable into the schelling model addresses the issue of a decreasing entropy trace. Scientific reports 10, 17005 (2020).
[33] Masuda, N. & Lambiotte, R. A guide to temporal networks (World Scientific, 2016).
[34] Gueuning, M., Cheng, S., Lambiotte, R. & Delvenne, J.-C. Rock-paperscissors dynamics from random walks on temporal multiplex networks. Journal of Complex Networks 8, cnz027 (2020).
[35] Barbosa, H. et al. Human mobility: Models and applications. Physics Reports 734, 1-74 (2018).
[36] Noulas, A., Scellato, S., Lambiotte, R., Pontil, M. & Mascolo, C. A tale of many cities: universal patterns in human urban mobility. PloS one 7, e37027 (2012).
[37] Pappalardo, L. et al. A survey on the impact of ai-based recommenders on human behaviours: methodologies, outcomes and future directions. arXiv preprint arXiv:2407.01630 (2024).
[38] Mauro, G., Luca, M., Longa, A., Lepri, B. & Pappalardo, L. Generating mobility networks with generative adversarial networks. EPJ data science 11, 58 (2022).
[39] Social Security Administration. 2022 compensation data (2022). URL https://www.ssa.gov/cgi-bin/netcomp.cgi?year=2022. Accessed: 2024-09-11.
[40] Barthelemy, M. The structure and dynamics of cities (Cambridge University Press, 2016).
[41] Arribas-Bel, D. & Sanz-Gracia, F. The validity of the monocentric city model in a polycentric age: Us metropolitan areas in 1990, 2000 and 2010. Urban Geography 35, 980-997 (2014).
[42] Kazil, J., Masad, D. & Crooks, A. Utilizing python for agent-based modeling: The mesa framework. In Thomson, R., Bisgin, H., Dancy, C., Hyder, A. & Hussain, M. (eds.) Social, Cultural, and Behavioral Modeling, 308-317 (Springer International Publishing, Cham, 2020).
[43] Holme, P. & Sarama¨ki, J. Temporal networks. Physics Reports 519, 97-125 (2012).
[44] Holme, P. Modern temporal network theory: a colloquium. The European Physical Journal B 88, 234 (2015).
[45] Kiveal¨, M. et al. Multilayer networks. Journal of Complex Networks 2, 203-271 (2014).
[46] Barrat, A., Barthel´emy, M., Pastor-Satorras, R. & Vespignani, A. The architecture of complex weighted networks. Proceedings of the National Academy of Sciences 101, 3747-3752 (2004).
[47] Kobayashi, T., Takaguchi, T. & Barrat, A. The structured backbone of temporal social ties. Nature Communications 10, 220 (2019).
[48] Ciaperoni, M. et al. Relevance of temporal cores for epidemic spread in temporal networks. Scientific Reports 10, 12529 (2020).
[49] Pedreschi, N., Battaglia, D. & Barrat, A. The temporal rich club phenomenon. Nature Physics 18, 931-938 (2022).
[50] Janssen, K. M. J., Cottineau-Mugadza, C., Kleinhans, R. & van Bueren, E. Spatial dynamics of incoming movers and the state-led gentrification process: The case of rotterdam. Population, Space and Place n/a, e2851.
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@article{oai:iris.cnr.it:20.500.14243/562961,
title = {Dynamic models of gentrification},
author = {Mauro G. and Pedreschi N. and Lambiotte R. and Pappalardo L.},
doi = {10.1142/s0219525925400065 and 10.48550/arxiv.2410.18004},
year = {2025}
}
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