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.
Int J Environ Res Public Health. 2022 Jul 27;19(15):9190. doi: 10.3390/ijerph19159190.
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 -statistic of 0.616. From 2009 to 2015, the driver with the highest influence on urban sprawl was green space per capita with a -statistic of 0.396.
城市蔓延已成为中国许多大城市空间扩展的主要模式,其生态和环境影响深刻影响着中国城市的发展。本文以夜间灯光数据和统计年鉴数据为基本数据源,模拟了中国长江上游(UYR)地区城市蔓延的演变趋势。首先,采用城市蔓延指数(USI)来评估城市蔓延的程度,并确定不同尺度下城市蔓延的特征。其次,运用空间自相关模型揭示了 1992 年至 2015 年城市蔓延的空间格局变化特征。再次,构建城市蔓延情景分析模型,模拟不同情景下城市蔓延的演变趋势。最后,基于地理探测器,分析不同时期影响城市蔓延的因素及其相互作用的影响。结果得出以下主要结论:(1)长江上游地区的城市蔓延程度先加剧后趋于稳定。四川省高 USI 城市、中等城市和成渝城市群的数量随时间推移而增加,表明这些地区的城市蔓延程度正在加剧。(2)城市蔓延热点经历了点状扩展-点环扩展-点轴扩展-轴辐射的格局转换过程。(3)在不同尺度的情景下,大城市的城市土地蔓延程度最高,占长江上游地区的 47%以上。成渝城市群的城市土地蔓延程度最高,占长江上游地区的 51%以上。蔓延程度最高的城市是重庆、丽江和昆明,分别占长江上游地区的 25.84%、7.37%和 5.11%。(4)在不同时间情景的模拟中,大城市的城市土地蔓延程度最高,占长江上游地区的 48.16%。成渝城市群的城市土地蔓延程度最高,占长江上游地区的 50.92%。(5)1996 年至 2002 年,对城市蔓延影响最大的驱动因素是第二产业占 GDP 的比重,-统计量为 0.616。2009 年至 2015 年,对城市蔓延影响最大的驱动因素是人均绿地面积,-统计量为 0.396。