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The Spatiotemporal Pattern and Driving Mechanism of Urban Sprawl in China’s Counties

Author

Listed:
  • Xu Yang

    (School of Economics and Trade, Hunan University, Changsha 410079, China)

  • Xuan Zou

    (School of Economics and Trade, Hunan University, Changsha 410079, China)

  • Xueqi Liu

    (School of Business Administration, Zhongnan University of Economics and Law, Wuhan 430073, China)

  • Qixuan Li

    (School of Public Administration, Hunan University, Changsha 410012, China)

  • Siqian Zou

    (School of Business, University of Bristol, Bristol BS81TH, UK)

  • Ming Li

    (School of Business Administration, Zhongnan University of Economics and Law, Wuhan 430073, China
    School of Management, Wuhan Technology and Business University, Wuhan 430065, China)

Abstract

Cities in China do not constitute a few global metropolises, but are characterized by heterogeneity. Studying counties can give us a comprehensive picture of urban sprawl in China. This study measured the sprawl index of 1880 counties in China from 2005 to 2020 for the first time and then revealed the evolution of their spatiotemporal characteristics and driving mechanisms. The results revealed the following. (1) China’s counties had a noticeable sprawling trend from 2005 to 2020, and their evolutionary process was characterized by spatiotemporal heterogeneity. (2) From 2005 to 2020, the counties’ sprawl gradually evolved into a spatial distribution pattern of high in the east and low in the west. The spatial distribution of sprawl in county and municipal districts had the characteristics of an interlocking distribution. (3) High–high cluster areas of CSI are mainly distributed in plains, and hilly, basin, and plateau areas tend to be low–low cluster areas. High–low outliers were distributed in a “point–line” pattern along the railroad lines and a cluster pattern near railroad intersections and central cities. Low–high outliers had the trend of encircling the high–high cluster areas. (4) The coefficient of the natural drivers was higher but tended to decrease, while the coefficient of economic and spatial drivers was lower but gradually increased. This study is the first to refine the study of urban sprawl to the county scale, which provides a reference for decision making to optimize the spatial structure of counties and thus promote high-quality development.

Suggested Citation

  • Xu Yang & Xuan Zou & Xueqi Liu & Qixuan Li & Siqian Zou & Ming Li, 2023. "The Spatiotemporal Pattern and Driving Mechanism of Urban Sprawl in China’s Counties," Land, MDPI, vol. 12(3), pages 1-16, March.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:3:p:721-:d:1103511
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    References listed on IDEAS

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    1. Yonghua Zhao & Li Liu & Shuaizhi Kang & Yong Ao & Lei Han & Chaoqun Ma, 2021. "Quantitative Analysis of Factors Influencing Spatial Distribution of Soil Erosion Based on Geo-Detector Model under Diverse Geomorphological Types," Land, MDPI, vol. 10(6), pages 1-17, June.
    2. Antonio M. Bento & Maureen L. Cropper & Ahmed Mushfiq Mobarak & Katja Vinha, 2005. "The Effects of Urban Spatial Structure on Travel Demand in the United States," The Review of Economics and Statistics, MIT Press, vol. 87(3), pages 466-478, August.
    3. Wancong Li & Hong Li & Shijun Wang & Zhiqiang Feng, 2022. "Spatiotemporal Evolution of County-Level Land Use Structure in the Context of Urban Shrinkage: Evidence from Northeast China," Land, MDPI, vol. 11(10), pages 1-19, October.
    4. Ehrlich, Maximilian V. & Hilber, Christian A.L. & Schöni, Olivier, 2018. "Institutional settings and urban sprawl: Evidence from Europe," Journal of Housing Economics, Elsevier, vol. 42(C), pages 4-18.
    5. Christine Bertram & Jan Goebel & Christian Krekel & Katrin Rehdanz, 2022. "Urban Land Use Fragmentation and Human Well-Being," Land Economics, University of Wisconsin Press, vol. 98(2), pages 399-420.
    6. Anele Horn & Amanda Van Eeden, 2018. "Measuring sprawl in the Western Cape Province, South Africa: An urban sprawl index for comparative purposes," Regional Science Policy & Practice, Wiley Blackwell, vol. 10(1), pages 15-23, March.
    7. Edward L. Glaeser & Joshua D. Gottlieb, 2006. "Urban Resurgence and the Consumer City," Urban Studies, Urban Studies Journal Limited, vol. 43(8), pages 1275-1299, July.
    8. Belal N. Fallah & Mark D. Partridge & M. Rose Olfert, 2011. "Urban sprawl and productivity: Evidence from US metropolitan areas," Papers in Regional Science, Wiley Blackwell, vol. 90(3), pages 451-472, August.
    9. Henderson, J. Vernon & Nigmatulina, Dzhamilya & Kriticos, Sebastian, 2021. "Measuring urban economic density," Journal of Urban Economics, Elsevier, vol. 125(C).
    10. Xin Zhang & Jinghu Pan, 2021. "Spatiotemporal Pattern and Driving Factors of Urban Sprawl in China," Land, MDPI, vol. 10(11), pages 1-16, November.
    11. Liu, Yong & Fan, Peilei & Yue, Wenze & Song, Yan, 2018. "Impacts of land finance on urban sprawl in China: The case of Chongqing," Land Use Policy, Elsevier, vol. 72(C), pages 420-432.
    12. Mathanraj Seevarethnam & Noradila Rusli & Gabriel Hoh Teck Ling & Ismail Said, 2021. "A Geo-Spatial Analysis for Characterising Urban Sprawl Patterns in the Batticaloa Municipal Council, Sri Lanka," Land, MDPI, vol. 10(6), pages 1-26, June.
    13. Wang, Xiaoxiao & Shi, Ruiting & Zhou, Ying, 2020. "Dynamics of urban sprawl and sustainable development in China," Socio-Economic Planning Sciences, Elsevier, vol. 70(C).
    14. Mariaflavia Harari, 2020. "Cities in Bad Shape: Urban Geometry in India," American Economic Review, American Economic Association, vol. 110(8), pages 2377-2421, August.
    15. Jintao Wang & Shiyou Qu & Ke Peng & Yanchao Feng, 2019. "Quantifying Urban Sprawl and Its Driving Forces in China," Discrete Dynamics in Nature and Society, Hindawi, vol. 2019, pages 1-14, May.
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