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Simulating spatiotemporal dynamics of urbanization with multi-agent systems—A case study of the Phoenix metropolitan region, USA

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  • Tian, Guangjin
  • Ouyang, Yun
  • Quan, Quan
  • Wu, Jianguo

Abstract

Urbanization is a human-dominated process and has greatly impacted biodiversity, ecosystem processes, and regional climate. To understand the socioeconomic drivers of urbanization and project future urban landscape changes, multi-agent systems provide a powerful tool. We develop an agent-based model of urban growth for the Phoenix metropolitan region of the United States, which simulates the behavior of regional authorities, real estate developers, residents, and environmentalists. The BDI (Beliefs–Desires–Intentions) structure is employed to simulate the agents behavior and decision models. The heterogeneity of agents is reflected by adjusting parameters according to the agents’ beliefs, desires and preferences. Three scenarios, baseline, economic development priority and environmental protection, are developed and analyzed. The combination of multi-agent system and spatial regression model is employed to predict the future urban development of the Phoenix metropolitan region. Landscape metrics are used to compare the spatial patterns of the urban landscape resulting from different scenarios in different times. In general, with the rapid urban expansion, the shape of urban patches will become more regular as many of them become coalesced. The spatial analysis of urban development through modeling individual and group decisions and human–environment interactions with a multi-agent systems approach can enhance our understanding of the socioeconomic driving forces and mechanisms of urban development.

Suggested Citation

  • Tian, Guangjin & Ouyang, Yun & Quan, Quan & Wu, Jianguo, 2011. "Simulating spatiotemporal dynamics of urbanization with multi-agent systems—A case study of the Phoenix metropolitan region, USA," Ecological Modelling, Elsevier, vol. 222(5), pages 1129-1138.
    • Handle: RePEc:eee:ecomod:v:222:y:2011:i:5:p:1129-1138
    DOI: 10.1016/j.ecolmodel.2010.12.018
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    References listed on IDEAS

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    5. Dongya Liu & Xinqi Zheng & Lei Zhang, 2021. "Simulation of Spatiotemporal Relationship between COVID-19 Propagation and Regional Economic Development in China," Land, MDPI, vol. 10(6), pages 1-15, June.
    6. Egidi, Gianluca & Mosconi, Enrico Maria & Turco, Rosario & Salvati, Luca, 2023. "Functions follow structures? The long-term evolution of economic dynamics, social transformations, and landscape morphology in a Mediterranean metropolis," Land Use Policy, Elsevier, vol. 129(C).
    7. Tian, Guangjin & Qiao, Zhi & Zhang, Yaoqi, 2012. "The investigation of relationship between rural settlement density, size, spatial distribution and its geophysical parameters of China using Landsat TM images," Ecological Modelling, Elsevier, vol. 231(C), pages 25-36.
    8. Mingruo Chu & Jiayi Lu & Dongqi Sun, 2022. "Influence of Urban Agglomeration Expansion on Fragmentation of Green Space: A Case Study of Beijing-Tianjin-Hebei Urban Agglomeration," Land, MDPI, vol. 11(2), pages 1-19, February.
    9. Yang, Xin & Zheng, Xin-Qi & Lv, Li-Na, 2012. "A spatiotemporal model of land use change based on ant colony optimization, Markov chain and cellular automata," Ecological Modelling, Elsevier, vol. 233(C), pages 11-19.
    10. Richard R. Rushforth & Benjamin L. Ruddell, 2015. "The Hydro-Economic Interdependency of Cities: Virtual Water Connections of the Phoenix, Arizona Metropolitan Area," Sustainability, MDPI, vol. 7(7), pages 1-26, June.
    11. Shivangi S. Somvanshi & Oshin Bhalla & Phool Kunwar & Madhulika Singh & Prafull Singh, 2020. "Monitoring spatial LULC changes and its growth prediction based on statistical models and earth observation datasets of Gautam Budh Nagar, Uttar Pradesh, India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(2), pages 1073-1091, February.
    12. Yang, Xin & Zheng, Xin-Qi & Chen, Rui, 2014. "A land use change model: Integrating landscape pattern indexes and Markov-CA," Ecological Modelling, Elsevier, vol. 283(C), pages 1-7.

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