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A New Perspective for Urban Development Boundary Delineation Based on the MCR Model and CA-Markov Model

Author

Listed:
  • Siqi Yi

    (Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Central China Normal University, Wuhan 430079, China
    The College of Urban & Environmental Sciences, Central China Normal University, Wuhan 430079, China)

  • Yong Zhou

    (Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Central China Normal University, Wuhan 430079, China
    The College of Urban & Environmental Sciences, Central China Normal University, Wuhan 430079, China)

  • Qing Li

    (Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Central China Normal University, Wuhan 430079, China
    The College of Urban & Environmental Sciences, Central China Normal University, Wuhan 430079, China)

Abstract

In order to control the development of urban space, it is important to explore scientific methods to provide a reference for regional territorial space planning. On the basis of the minimum cumulative resistance (MCR) model and the cellular automaton (CA)-Markov model, we constructed a new technical method for delineating urban development boundaries, exploring the temporal and spatial distribution characteristic of land use in Wuhan from 2010 to 2020 through nighttime and remote sensing images, and simulating the urban development boundaries of Wuhan from 2025 to 2035. The results show that: (1) the scales of Wuhan City’s built-up areas in 2010, 2015, and 2020 were 500 km 2 , 566.13 km 2 , and 885.11 km 2 , respectively, and the trends of expansion run to the east and southeast, and (2) on the basis of the MCR model, the urban development boundary scale of Wuhan City in 2025, 2030, and 2035 from the perspective of actual supply will be 903.52 km 2 , 937.48 km 2 , and 1021.44 km 2 , respectively, and based on the CA-Markov model, the urban development boundary scales of Wuhan City in 2025, 2030, and 2035 from the perspective of ideal land demand will be 912.75 km 2 , 946.40 km 2 , and 1041.91 km 2 , respectively. By combining the results of the two methods, we determined areas of 901.62 km 2 , 944.39 km 2 , and 1015.36 km 2 as the urban development boundaries of Wuhan City in 2025, 2030, and 2035, respectively. According to the principle of supply–demand balance, the urban development boundary delineated by the integration of the MCR model and CA-Markov model, which is in line with the spatial expansion trend of growing cities, could optimize the urban development pattern; solve the contradiction between urban development, farmland protection, and ecological protection; and provide a methodological reference and decision-making basis for planning practice.

Suggested Citation

  • Siqi Yi & Yong Zhou & Qing Li, 2022. "A New Perspective for Urban Development Boundary Delineation Based on the MCR Model and CA-Markov Model," Land, MDPI, vol. 11(3), pages 1-16, March.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:3:p:401-:d:767465
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    References listed on IDEAS

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    2. Liang Lv & Shihao Zhang & Jie Zhu & Ziming Wang & Zhe Wang & Guoqing Li & Chen Yang, 2022. "Ecological Restoration Strategies for Mountainous Cities Based on Ecological Security Patterns and Circuit Theory: A Case of Central Urban Areas in Chongqing, China," IJERPH, MDPI, vol. 19(24), pages 1-21, December.
    3. Guanglong Dong & Zhonghao Liu & Yuanzhao Niu & Wenya Jiang, 2022. "Identification of Land Use Conflicts in Shandong Province from an Ecological Security Perspective," Land, MDPI, vol. 11(12), pages 1-18, December.
    4. 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.
    5. Milad Asadi & Amir Oshnooei-Nooshabadi & Samira-Sadat Saleh & Fattaneh Habibnezhad & Sonia Sarafraz-Asbagh & John Lodewijk Van Genderen, 2022. "Urban Sprawl Simulation Mapping of Urmia (Iran) by Comparison of Cellular Automata–Markov Chain and Artificial Neural Network (ANN) Modeling Approach," Sustainability, MDPI, vol. 14(23), pages 1-16, November.
    6. Chang Lu & Xiao Qi & Zhongsen Zheng & Kun Jia, 2022. "PLUS-Model Based Multi-Scenario Land Space Simulation of the Lower Yellow River Region and Its Ecological Effects," Sustainability, MDPI, vol. 14(11), pages 1-17, June.

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