IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i11p4341-d362895.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/11/4341/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/11/4341/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Marais, Lochner & Denoon-Stevens, Stuart & Cloete, Jan, 2020. "Mining towns and urban sprawl in South Africa," Land Use Policy, Elsevier, vol. 93(C).
    2. Opeyemi A. Zubair & Wei Ji & Olusola Festus, 2019. "Urban Expansion and the Loss of Prairie and Agricultural Lands: A Satellite Remote-Sensing-Based Analysis at a Sub-Watershed Scale," Sustainability, MDPI, vol. 11(17), pages 1-12, August.
    3. Pakawan Chotchaiwong & Saowanee Wijitkosum, 2019. "Predicting Urban Expansion and Urban Land Use Changes in Nakhon Ratchasima City Using a CA-Markov Model under Two Different Scenarios," Land, MDPI, vol. 8(9), pages 1-16, September.
    4. Hashem Dadashpoor & Fardis Salarian, 2020. "Urban sprawl on natural lands: analyzing and predicting the trend of land use changes and sprawl in Mazandaran city region, Iran," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(2), pages 593-614, February.
    5. Duo Zheng & Guanshi Zhang & Hui Shan & Qichao Tu & Hongjuan Wu & Sen Li, 2020. "Spatio-Temporal Evolution of Urban Morphology in the Yangtze River Middle Reaches Megalopolis, China," Sustainability, MDPI, vol. 12(5), pages 1-15, February.
    6. Raupach, M.R. & Rayner, P.J. & Paget, M., 2010. "Regional variations in spatial structure of nightlights, population density and fossil-fuel CO2 emissions," Energy Policy, Elsevier, vol. 38(9), pages 4756-4764, September.
    7. Hussam Al-Bilbisi, 2019. "Spatial Monitoring of Urban Expansion Using Satellite Remote Sensing Images: A Case Study of Amman City, Jordan," Sustainability, MDPI, vol. 11(8), pages 1-14, April.
    8. Gomes, Eduardo & Abrantes, Patrícia & Banos, Arnaud & Rocha, Jorge, 2019. "Modelling future land use scenarios based on farmers’ intentions and a cellular automata approach," Land Use Policy, Elsevier, vol. 85(C), pages 142-154.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yuxiang Zhang & Dongjie Guan & Xiujuan He & Boling Yin, 2022. "Simulation on the Evolution Trend of the Urban Sprawl Spatial Pattern in the Upper Reaches of the Yangtze River, China," IJERPH, MDPI, vol. 19(15), pages 1-21, July.
    2. Jinling Zhang & Ying Hou & Yifan Dong & Cun Wang & Weiping Chen, 2022. "Land Use Change Simulation in Rapid Urbanizing Regions: A Case Study of Wuhan Urban Areas," IJERPH, MDPI, vol. 19(14), pages 1-19, July.
    3. 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.
    4. Jing Liu & Chunchun Hu & Xionghua Kang & Fei Chen, 2023. "A Loosely Coupled Model for Simulating and Predicting Land Use Changes," Land, MDPI, vol. 12(1), pages 1-19, January.
    5. Shuangqing Sheng & Wei Song & Hua Lian & Lei Ning, 2022. "Review of Urban Land Management Based on Bibliometrics," Land, MDPI, vol. 11(11), pages 1-25, November.
    6. Xinbei Huang & Chengming Ye & Hongyu Tao & Junjie Zou & Yuzhan Zhou & Shufan Zheng, 2024. "Integrating Future Multi-Scenarios to Evaluate the Effectiveness of Ecological Restoration: A Case Study of the Yellow River Basin," Land, MDPI, vol. 13(7), pages 1-19, July.
    7. 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).
    8. 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.
    9. Yashon O. Ouma & Boipuso Nkwae & Phillimon Odirile & Ditiro B. Moalafhi & George Anderson & Bhagabat Parida & Jiaguo Qi, 2024. "Land-Use Change Prediction in Dam Catchment Using Logistic Regression-CA, ANN-CA and Random Forest Regression and Implications for Sustainable Land–Water Nexus," Sustainability, MDPI, vol. 16(4), pages 1-30, February.
    10. 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.
    11. 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.
    12. Dan Li & Bingjun Liu & Changqing Ye, 2022. "Meteorological and hydrological drought risks under changing environment on the Wanquan River Basin, Southern China," 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. 114(3), pages 2941-2967, December.

    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. Daniela Smiraglia & Luca Salvati & Gianluca Egidi & Rosanna Salvia & Antonio Giménez-Morera & Rares Halbac-Cotoara-Zamfir, 2021. "Toward a New Urban Cycle? A Closer Look to Sprawl, Demographic Transitions and the Environment in Europe," Land, MDPI, vol. 10(2), pages 1-14, January.
    2. Wang, Xueqi & Liu, Gengyuan & Coscieme, Luca & Giannetti, Biagio F. & Hao, Yan & Zhang, Yan & Brown, Mark T., 2019. "Study on the emergy-based thermodynamic geography of the Jing-Jin-Ji region: Combined multivariate statistical data with DMSP-OLS nighttime lights data," Ecological Modelling, Elsevier, vol. 397(C), pages 1-15.
    3. Su, Yongxian & Chen, Xiuzhi & Li, Yong & Liao, Jishan & Ye, Yuyao & Zhang, Hongou & Huang, Ningsheng & Kuang, Yaoqiu, 2014. "China׳s 19-year city-level carbon emissions of energy consumptions, driving forces and regionalized mitigation guidelines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 231-243.
    4. Vishal Chettry & Meenal Surawar, 2021. "Assessment of urban sprawl characteristics in Indian cities using remote sensing: case studies of Patna, Ranchi, and Srinagar," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(8), pages 11913-11935, August.
    5. Bastos, Paulo & Bottan, Nicolas, 2023. "Resource rents, coercion, and local development: Evidence from post-apartheid South Africa," World Development, Elsevier, vol. 169(C).
    6. Gang Xu & Tianyi Zeng & Hong Jin & Cong Xu & Ziqi Zhang, 2023. "Spatio-Temporal Variations and Influencing Factors of Country-Level Carbon Emissions for Northeast China Based on VIIRS Nighttime Lighting Data," IJERPH, MDPI, vol. 20(1), pages 1-17, January.
    7. Huicai Yang & Jingtao Ma & Xinying Jiao & Guofei Shang & Haiming Yan, 2023. "Characteristics and Driving Mechanism of Urban Construction Land Expansion along with Rapid Urbanization and Carbon Neutrality in Beijing, China," Land, MDPI, vol. 12(7), pages 1-17, July.
    8. Ustaoglu, E. & Sisman, S. & Aydınoglu, A.C., 2021. "Determining agricultural suitable land in peri-urban geography using GIS and Multi Criteria Decision Analysis (MCDA) techniques," Ecological Modelling, Elsevier, vol. 455(C).
    9. Qing Lu & Jing Ning & Hong You & Liyan Xu, 2023. "Urban Intensity in Theory and Practice: Empirical Determining Mechanism of Floor Area Ratio and Its Deviation from the Classic Location Theories in Beijing," Land, MDPI, vol. 12(2), pages 1-16, February.
    10. Hashem Dadashpoor & Hossein Panahi, 2021. "Exploring an integrated spatially model for land-use scenarios simulation in a metropolitan region," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(9), pages 13628-13649, September.
    11. Muhirwa, Fabien & Shen, Lei & Elshkaki, Ayman & Hirwa, Hubert & Umuziranenge, Gloriose & Velempini, Kgosietsile, 2023. "Linking large extractive industries to sustainable development of rural communities at mining sites in Africa: Challenges and pathways," Resources Policy, Elsevier, vol. 81(C).
    12. Tao Peng & Hongwei Deng, 2021. "Study on the division of main functional regions based on relative carrying capacity of resources: a case study of Guiyang, southwest China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(6), pages 9493-9513, June.
    13. Rodrigo Castriota, 2024. "HOUSING BEYOND THE METROPOLIS: Inhabiting Extractivism and Extensions in Urban Amazonia," International Journal of Urban and Regional Research, Wiley Blackwell, vol. 48(1), pages 32-52, January.
    14. Somayeh Ahani & Hashem Dadashpoor, 2021. "Urban growth containment policies for the guidance and control of peri-urbanization: a review and proposed framework," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(10), pages 14215-14244, October.
    15. Juan Jose Miranda & Oscar A. Ishizawa & Hongrui Zhang, 2020. "Understanding the Impact Dynamics of Windstorms on Short-Term Economic Activity from Night Lights in Central America," Economics of Disasters and Climate Change, Springer, vol. 4(3), pages 657-698, October.
    16. Cláudia M. Viana & Jorge Rocha, 2020. "Evaluating Dominant Land Use/Land Cover Changes and Predicting Future Scenario in a Rural Region Using a Memoryless Stochastic Method," Sustainability, MDPI, vol. 12(10), pages 1-28, May.
    17. Nadiia Charkovska & Mariia Halushchak & Rostyslav Bun & Zbigniew Nahorski & Tomohiro Oda & Matthias Jonas & Petro Topylko, 2019. "A high-definition spatially explicit modelling approach for national greenhouse gas emissions from industrial processes: reducing the errors and uncertainties in global emission modelling," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 24(6), pages 907-939, August.
    18. Tomasz Zaborowski, 2021. "It’s All about Details. Why the Polish Land Policy Framework Fails to Manage Designation of Developable Land," Land, MDPI, vol. 10(9), pages 1-27, August.
    19. Ling Zhang & Jiawei Zhang & Xiaohui Li & Kaidi Zhou & Jiang Ye, 2023. "The Impact of Urban Sprawl on Carbon Emissions from the Perspective of Nighttime Light Remote Sensing: A Case Study in Eastern China," Sustainability, MDPI, vol. 15(15), pages 1-19, August.
    20. Rostyslav Bun & Zbigniew Nahorski & Joanna Horabik-Pyzel & Olha Danylo & Linda See & Nadiia Charkovska & Petro Topylko & Mariia Halushchak & Myroslava Lesiv & Mariia Valakh & Vitaliy Kinakh, 2019. "Development of a high-resolution spatial inventory of greenhouse gas emissions for Poland from stationary and mobile sources," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 24(6), pages 853-880, August.

    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:12:y:2020:i:11:p:4341-:d:362895. 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.