TY - JOUR
T1 - Simulating spatiotemporal dynamics of urbanization with multi-agent systems-A case study of the Phoenix metropolitan region, USA
AU - Tian, Guangjin
AU - Ouyang, Yun
AU - Quan, Quan
AU - Wu, Jianguo
N1 - Funding Information:
This research was partly supported by the National Science Foundation under Grant No. BCS-0508002 (Biocomplexity/CNH) and under Grant No. DEB-0423704 (CAP LTER). Any opinions, findings and conclusions or recommendation expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation (NSF). GJ and QQ were also supported by National Natural Science Foundation of China under grant 41071357 .
PY - 2011/3/10
Y1 - 2011/3/10
N2 - 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.
AB - 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.
KW - Landscape metrics
KW - Multi-agent systems
KW - Scenario analysis
KW - Spatiotemporal pattern of urbanization
KW - The Phoenix metropolitan area
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U2 - 10.1016/j.ecolmodel.2010.12.018
DO - 10.1016/j.ecolmodel.2010.12.018
M3 - Article
AN - SCOPUS:79551508529
SN - 0304-3800
VL - 222
SP - 1129
EP - 1138
JO - Ecological Modelling
JF - Ecological Modelling
IS - 5
ER -