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Simulation on the Evolution Trend of the Urban Sprawl Spatial Pattern in the Upper Reaches of the Yangtze River, China

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  • Yuxiang Zhang

    (School of Smart City, Chongqing Jiaotong University, No. 66 Xuefu Road, Nan’an District, Chongqing 400074, China)

  • Dongjie Guan

    (School of Smart City, Chongqing Jiaotong University, No. 66 Xuefu Road, Nan’an District, Chongqing 400074, China
    State Key Laboratory of Mountain Bridge and Tunnel Engineering, Chongqing Jiaotong University, No. 66 Xuefu Road, Nan’an District, Chongqing 400074, China)

  • Xiujuan He

    (School of Smart City, Chongqing Jiaotong University, No. 66 Xuefu Road, Nan’an District, Chongqing 400074, China)

  • Boling Yin

    (School of Smart City, Chongqing Jiaotong University, No. 66 Xuefu Road, Nan’an District, Chongqing 400074, China)

Abstract

Urban sprawl has become the main pattern of spatial expansion in many large cities in China, and its ecological and environmental effects profoundly impact Chinese urban development. In this paper, nighttime light data and statistical yearbook data are adopted as basic data sources to simulate the evolution trend of the urban sprawl in the upper Yangtze River (UYR), China. First, the urban sprawl index (USI) is employed to assess the level of urban sprawl and to determine the characteristics of urban sprawl under different scales. Second, the spatial autocorrelation model is applied to reveal the spatial pattern change characteristics of urban sprawl from 1992 to 2015. Third, a scenario analysis model of urban sprawl is constructed to simulate the evolution trend of the urban sprawl under different scenarios. Finally, based on the Geodetector, the influence of factors and factor interactions influencing urban sprawl in different time periods is analyzed. The results yield the following main conclusions: (1) The urban sprawl in the UYR first intensifies and then stabilizes over time. The number of cities with high USI in Sichuan province, medium cities, and Chengdu-Chongqing urban agglomeration increases over time, indicating that urban sprawl is intensifying in these areas. (2) The urban sprawl hot spots experience a pattern transformation process of point-like expansion-point-ring expansion-point-axis expansion-axis radiation. (3) Under the scenarios with different scales, the urban land sprawl in large cities is the highest, accounting for more than 47% of the UYR. Urban land sprawl extent in the Chengdu-Chongqing urban agglomeration is the highest, accounting for more than 51% of the UYR. The cities exhibiting the highest sprawl are Chongqing, Lijiang, and Kunming, accounting for 25.84%, 7.37%, and 5.11%, respectively, of the UYR. (4) In the different time scenario simulations, the urban land in large cities exhibits the highest sprawl, accounting for approximately 48.16% of the UYR. The urban land in the Chengdu-Chongqing urban agglomeration demonstrates the highest sprawl, accounting for 50.92% of the UYR. (5) From 1996 to 2002, the driver with the highest influence on urban sprawl was secondary industry share of GDP, with a q -statistic of 0.616. From 2009 to 2015, the driver with the highest influence on urban sprawl was green space per capita with a q -statistic of 0.396.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:15:p:9190-:d:873347
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    2. Xinyu Zhuang & Li Zhang & Jie Lu, 2022. "Past—Present—Future: Urban Spatial Succession and Transition of Rail Transit Station Zones in Japan," IJERPH, MDPI, vol. 19(20), pages 1-35, October.

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