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Simulating Urban Sprawl in China Based on the Artificial Neural Network-Cellular Automata-Markov Model

Author

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  • Xueru Zhang

    (School of Public Administration, Hebei University of Economics and Business, Shijiazhuang 050061, China)

  • Jie Zhou

    (Six 0 Six Teams of Sichuan Metallurgical Geological Prospecting Bureau, Chengdu 611730, China)

  • Wei Song

    (Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China)

Abstract

In recent years, China’s urbanization rate has been increasing rapidly, reaching 59.58% in 2018. Urbanization drives rural-to-urban migration, and inevitably promotes urban sprawl. With the development of remote sensing and geographic information technologies, the monitoring technology for urban sprawl has been constantly innovated. In particular, the emergence of night light data has greatly promoted monitoring research of large-scale and long-time-series urban sprawl. In this paper, the urban sprawl in China in 1992, 1997, 2002, 2007, 2012, and 2017 was identified via night light data, and the Artificial Neural Network-Cellular Automata-Markov (ANN-CA-Markov) model was developed to simulate the future urban sprawl in China. The results show that the suitability of urban sprawl based on the ANN model is as high as 0.864, indicating that the ANN model is very suitable for the simulation of urban sprawl. The Kappa coefficient of simulation results was 0.78, indicating that the ANN-CA-Markov model has a high simulation accuracy on urban sprawl. In the future, the hotspot areas of urban sprawl in China will change over time. Although the urban sprawl in the Beijing-Tianjin-Hebei region, the Yangtze River delta, and the Pearl River delta will still be considerable, the urban sprawl in the Chengdu-Chongqing city cluster, the Guanzhong Plain city cluster, the central plains city cluster, and the middle reaches of the Yangtze River will be more prominent. Overall, China’s urban sprawl will be concentrated in the east of Hu’s line in the future.

Suggested Citation

  • Xueru Zhang & Jie Zhou & Wei Song, 2020. "Simulating Urban Sprawl in China Based on the Artificial Neural Network-Cellular Automata-Markov Model," Sustainability, MDPI, vol. 12(11), pages 1-13, May.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:11:p:4341-:d:362895
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    References listed on IDEAS

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    Cited by:

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    2. Daizhong Tang & Mengyuan Mao & Jiangang Shi & Wenwen Hua, 2021. "The Spatio-Temporal Analysis of Urban-Rural Coordinated Development and Its Driving Forces in Yangtze River Delta," Land, MDPI, vol. 10(5), pages 1-21, May.
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    4. Lan, Hai & Zheng, Puyang & Li, Zheng, 2021. "Constructing urban sprawl measurement system of the Yangtze River economic belt zone for healthier lives and social changes in sustainable cities," Technological Forecasting and Social Change, Elsevier, vol. 165(C).
    5. Linfeng Xu & Xuan Liu & De Tong & Zhixin Liu & Lirong Yin & Wenfeng Zheng, 2022. "Forecasting Urban Land Use Change Based on Cellular Automata and the PLUS Model," Land, MDPI, vol. 11(5), pages 1-16, April.
    6. Jieming Chou & Mingyang Sun & Wenjie Dong & Weixing Zhao & Jiangnan Li & Yuanmeng Li & Jianyin Zhou, 2021. "Assessment and Prediction of Climate Risks in Three Major Urban Agglomerations of Eastern China," Sustainability, MDPI, vol. 13(23), pages 1-21, November.
    7. Shuqing Wang & Xinqi Zheng, 2023. "Dominant transition probability: combining CA-Markov model to simulate land use change," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(7), pages 6829-6847, July.
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