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Land-use Simulation and Decision-Support system (LandSDS): Seamlessly integrating system dynamics, agent-based model, and cellular automata

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  • Liu, Dongya
  • Zheng, Xinqi
  • Wang, Hongbin

Abstract

This article aims to construct a Land-use Simulation and Decision-Support system (LandSDS) to comprehensively explore the interference and impact of natural environment, individual choice, and national macro-policies on land use and cover change. LandSDS takes full advantage of system dynamics (SD) model, agent-based model (ABM), cellular automata (CA) model, and Geographic Information System (GIS): (1) On the one hand, SD model controls land-use area change on macro level. On the other hand, by passing the parameters values from SD model to CA model, the iterations of CA model are controlled macroscopically, and at the same time, the transition rules in the CA model are affected microscopically by SD model; (2) By simulating the complex selection behaviors of different agents, ABM enforces or participates in the cells’ transformation behaviors in CA model on the micro evolution scale; (3) Taking CA model as a breakthrough point, the micro-cell unit is used to realize the data exchange and communication between SD-ABM-CA by integrating CA with SD and CA with ABM; and (4) Using GIS’s powerful spatial computing and data processing capabilities, the simulation results are further analyzed and processed to obtain more effective information. LandSDS can solve the top-down data analysis and graphic visualization problems caused by target data or policy mutations, and can also handle the data and graphic visualization problems caused by the temporal and spatial changes of cell units. The system solves the analysis methods and technical problems about the top-down and bottom-up seamless integration of the spatial and temporal models. Beijing urban expansion was taken as an example to demonstrate the availability of the system.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:ecomod:v:417:y:2020:i:c:s0304380019304326
    DOI: 10.1016/j.ecolmodel.2019.108924
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    References listed on IDEAS

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    Cited by:

    1. Yin Ma & Minrui Zheng & Xinqi Zheng & Yi Huang & Feng Xu & Xiaoli Wang & Jiantao Liu & Yongqiang Lv & Wenchao Liu, 2023. "Land Use Efficiency Assessment under Sustainable Development Goals: A Systematic Review," Land, MDPI, vol. 12(4), pages 1-21, April.
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    3. 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).
    4. Jingjie Liu & Min Xia, 2023. "Influencing Factors Analysis and Optimization of Land Use Allocation: Combining MAS with MOPSO Procedure," Sustainability, MDPI, vol. 15(2), pages 1-17, January.
    5. 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).
    6. Yabo Zhao & Dixiang Xie & Xiwen Zhang & Shifa Ma, 2021. "Integrating Spatial Markov Chains and Geographically Weighted Regression-Based Cellular Automata to Simulate Urban Agglomeration Growth: A Case Study of the Guangdong–Hong Kong–Macao Greater Bay Area," Land, MDPI, vol. 10(6), pages 1-19, June.
    7. Alysha van Duynhoven & Suzana Dragićević, 2021. "Exploring the Sensitivity of Recurrent Neural Network Models for Forecasting Land Cover Change," Land, MDPI, vol. 10(3), pages 1-29, March.
    8. 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.
    9. Qihao Wang & Dongya Liu & Feiyao Gao & Xinqi Zheng & Yiqun Shang, 2023. "A Partitioned and Heterogeneous Land-Use Simulation Model by Integrating CA and Markov Model," Land, MDPI, vol. 12(2), pages 1-20, February.
    10. Yang Yi & Chen Zhang & Guilian Zhang & Luqi Xing & Qicheng Zhong & Jialin Liu & Yichen Lin & Xiewei Zheng & Na Yang & Hao Sun & Mingchang Shi & Hongzhang Kang, 2021. "Effects of Urbanization on Landscape Patterns in the Middle Reaches of the Yangtze River Region," Land, MDPI, vol. 10(10), pages 1-17, September.
    11. 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.
    12. Chasia, Stanley & Olang, Luke O. & Sitoki, Lewis, 2023. "Modelling of land-use/cover change trajectories in a transboundary catchment of the Sio-Malaba-Malakisi Region in East Africa using the CLUE-s model," Ecological Modelling, Elsevier, vol. 476(C).
    13. 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.
    14. Wang, Han & Lu, Siying & Lu, Bo & Nie, Xin, 2021. "Overt and covert: The relationship between the transfer of land development rights and carbon emissions," Land Use Policy, Elsevier, vol. 108(C).

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