IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2022i1p290-d1013798.html
   My bibliography  Save this article

Urban Expansion Simulation Coupled with Residential Location Selection and Land Acquisition Bargaining: A Case Study of Wuhan Urban Development Zone, Central China’s Hubei Province

Author

Listed:
  • Heng Liu

    (College of Forestry and Horticulture, Hubei Minzu University, Enshi 445000, China)

  • Lu Zhou

    (College of Forestry and Horticulture, Hubei Minzu University, Enshi 445000, China)

  • Diwei Tang

    (College of Forestry and Horticulture, Hubei Minzu University, Enshi 445000, China)

Abstract

The urban expansion process involves multiple stakeholders whose interactions and decision-making behaviors have a complex impact on urban land conversion. In this study, we established an urban expansion simulation model that couples two sub-models: the residential location selection model and the land acquisition bargaining model. Those sub-models include four types of agents: resident agent (RA), real estate developer agent (DA), government agent (GA), and farmer agent (FA). The residential location selection model is composed of three agents, RA, DA, and GA, and is first used to select residential locations, while an artificial neural network (ANN) is used to define the behavior rules of RA and RA selects pixels as candidate locations according to the joint decision probability. Then the land acquisition bargaining model is used, which is composed of GA and FA. If the land acquisition is successful, a pixel is converted into urban land, which is occupied by the corresponding RA; otherwise, the RA selects the next pixel and enters the bargaining process again, and so on, until the RA successfully selects a residential location. Each iteration represents the selection process of an agent. We used this model to simulate urban expansion within the Wuhan Urban Development Zone (WHUDZ) of central China from 2009 to 2019. The overall accuracy and Kappa coefficient of the simulation results were 92.78% and 55.24%, respectively, which were higher than the results using logistic regression cellular automata. Moreover, we obtained the relative contributions of various influencing factors in the ANN on the residential location selection, revealing the influence of the land acquisition process on land expansion. In addition, the coupled model predicted that the WHUDZ’s urban land area will reach 1415.82 km 2 in 2029, mainly through extensional expansion, and the southeast and northwest will be expansion hot spots.

Suggested Citation

  • Heng Liu & Lu Zhou & Diwei Tang, 2022. "Urban Expansion Simulation Coupled with Residential Location Selection and Land Acquisition Bargaining: A Case Study of Wuhan Urban Development Zone, Central China’s Hubei Province," Sustainability, MDPI, vol. 15(1), pages 1-20, December.
    • Handle: RePEc:gam:jsusta:v:15:y:2022:i:1:p:290-:d:1013798
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/1/290/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/1/290/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jiang, Xue & Li, Bingxin & Zhao, Hongyu & Zhang, Qiqi & Song, Xiaoya & Zhang, Haoran, 2022. "Examining the spatial simulation and land-use reorganisation mechanism of agricultural suburban settlements using a cellular-automata and agent-based model: Six settlements in China," Land Use Policy, Elsevier, vol. 120(C).
    2. Feng, Weilun & Liu, Yansui & Qu, Lulu, 2019. "Effect of land-centered urbanization on rural development: A regional analysis in China," Land Use Policy, Elsevier, vol. 87(C).
    3. An, Li, 2012. "Modeling human decisions in coupled human and natural systems: Review of agent-based models," Ecological Modelling, Elsevier, vol. 229(C), pages 25-36.
    4. Liu, Dongya & Zheng, Xinqi & Wang, Hongbin, 2020. "Land-use Simulation and Decision-Support system (LandSDS): Seamlessly integrating system dynamics, agent-based model, and cellular automata," Ecological Modelling, Elsevier, vol. 417(C).
    5. Sadooghi, Seyed Ehsan & Taleai, Mohammad & Abolhasani, Somaie, 2022. "Simulation of urban growth scenarios using integration of multi-criteria analysis and game theory," Land Use Policy, Elsevier, vol. 120(C).
    6. Tingting Xu & Dingjie Zhou & Yuhua Li, 2022. "Integrating ANNs and Cellular Automata–Markov Chain to Simulate Urban Expansion with Annual Land Use Data," Land, MDPI, vol. 11(7), pages 1-15, July.
    7. Gyourko, Joseph & Shen, Yang & Wu, Jing & Zhang, Rongjie, 2022. "Land finance in China: Analysis and review," China Economic Review, Elsevier, vol. 76(C).
    8. Qingxu Huang & Dawn C Parker & Tatiana Filatova & Shipeng Sun, 2014. "A Review of Urban Residential Choice Models Using Agent-Based Modeling," Environment and Planning B, , vol. 41(4), pages 661-689, August.
    9. F Wang, 1994. "The Use of Artificial Neural Networks in a Geographical Information System for Agricultural Land-Suitability Assessment," Environment and Planning A, , vol. 26(2), pages 265-284, February.
    10. Xu, Hongtao & Song, Youcheng & Tian, Yi, 2022. "Simulation of land-use pattern evolution in hilly mountainous areas of North China: A case study in Jincheng," Land Use Policy, Elsevier, vol. 112(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jiang, Xue & Li, Bingxin & Zhao, Hongyu & Zhang, Qiqi & Song, Xiaoya & Zhang, Haoran, 2022. "Examining the spatial simulation and land-use reorganisation mechanism of agricultural suburban settlements using a cellular-automata and agent-based model: Six settlements in China," Land Use Policy, Elsevier, vol. 120(C).
    2. Alice Owen & Alison Heppenstall, 2020. "Making the case for simulation: Unlocking carbon reduction through simulation of individual ‘middle actor’ behaviour," Environment and Planning B, , vol. 47(3), pages 457-472, March.
    3. Song, Youcheng & Wang, Haijun & Peng, Xiaotao & Sun, Duan & Chen, Rui, 2023. "Modeling land use change prediction using multi-model fusion techniques: A case study in the Pearl River Delta, China," Ecological Modelling, Elsevier, vol. 486(C).
    4. Ficko, Andrej & Boncina, Andrej, 2013. "Probabilistic typology of management decision making in private forest properties," Forest Policy and Economics, Elsevier, vol. 27(C), pages 34-43.
    5. Qingxu Huang & Dawn C Parker & Tatiana Filatova & Shipeng Sun, 2014. "A Review of Urban Residential Choice Models Using Agent-Based Modeling," Environment and Planning B, , vol. 41(4), pages 661-689, August.
    6. Kamel Louhichi & Aymeric Ricome & Sergio Gomez y Paloma, 2022. "Impacts of agricultural taxation in Sub‐Saharan Africa: Insights from agricultural produce cess in Tanzania," Agricultural Economics, International Association of Agricultural Economists, vol. 53(5), pages 671-686, September.
    7. Bindewald, Eckart, 2017. "A survey suggests individual priorities are virtually unique: Implications for group dynamics, goal achievement and ecology," Ecological Modelling, Elsevier, vol. 362(C), pages 69-79.
    8. James D. A. Millington & Hang Xiong & Steve Peterson & Jeremy Woods, 2017. "Integrating Modelling Approaches for Understanding Telecoupling: Global Food Trade and Local Land Use," Land, MDPI, vol. 6(3), pages 1-18, August.
    9. Xia, Min & Zhang, Yanyuan & Zhang, Zihong & Liu, Jingjie & Ou, Weixin & Zou, Wei, 2020. "Modeling agricultural land use change in a rapid urbanizing town: Linking the decisions of government, peasant households and enterprises," Land Use Policy, Elsevier, vol. 90(C).
    10. Coronese, Matteo & Occelli, Martina & Lamperti, Francesco & Roventini, Andrea, 2023. "AgriLOVE: Agriculture, land-use and technical change in an evolutionary, agent-based model," Ecological Economics, Elsevier, vol. 208(C).
    11. Grimm, Volker & Berger, Uta, 2016. "Structural realism, emergence, and predictions in next-generation ecological modelling: Synthesis from a special issue," Ecological Modelling, Elsevier, vol. 326(C), pages 177-187.
    12. Anshuka Anshuka & Floris F. Ogtrop & David Sanderson & Simone Z. Leao, 2022. "A systematic review of agent-based model for flood risk management and assessment using the ODD protocol," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 112(3), pages 2739-2771, July.
    13. Hao Wang & Yunfeng Hu, 2021. "Simulation of Biocapacity and Spatial-Temporal Evolution Analysis of Loess Plateau in Northern Shaanxi Based on the CA–Markov Model," Sustainability, MDPI, vol. 13(11), pages 1-17, May.
    14. Handi Chandra‐Putra & Clinton J. Andrews, 2020. "An integrated model of real estate market responses to coastal flooding," Journal of Industrial Ecology, Yale University, vol. 24(2), pages 424-435, April.
    15. Yi Xiao & Yuantao Liao & Zhe Li & Zhuojun Li & Shaojian Wang, 2023. "Impacts of Land Urbanization on CO 2 Emissions: Policy Implications Based on Developmental Stages," Land, MDPI, vol. 12(10), pages 1-15, October.
    16. Weilun Feng & Yurui Li, 2021. "Measuring the Ecological Safety Effects of Land Use Transitions Promoted by Land Consolidation Projects: The Case of Yan’an City on the Loess Plateau of China," Land, MDPI, vol. 10(8), pages 1-15, July.
    17. Xiang Li & Xiaoqin Guo, 2023. "Can Policy Promote Agricultural Service Outsourcing? Quasi-Natural Experimental Evidence from China," Sustainability, MDPI, vol. 15(2), pages 1-18, January.
    18. Nicholas R. Magliocca, 2020. "Agent-Based Modeling for Integrating Human Behavior into the Food–Energy–Water Nexus," Land, MDPI, vol. 9(12), pages 1-25, December.
    19. Panagiotis Reklitis & Damianos P. Sakas & Panagiotis Trivellas & Giannis T. Tsoulfas, 2021. "Performance Implications of Aligning Supply Chain Practices with Competitive Advantage: Empirical Evidence from the Agri-Food Sector," Sustainability, MDPI, vol. 13(16), pages 1-21, August.
    20. Jonas Friege & Georg Holtz & Emile Chappin, 2016. "Exploring Homeowners’ Insulation Activity," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 19(1), pages 1-4.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2022:i:1:p:290-:d:1013798. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.