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京津冀城市群集聚扩散的空间分析

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针对京津冀城市群的集聚扩散进行空间分析研究,可促进京津冀区域经济协同发展.基于京津冀城市群2020年城市化水平,通过构建地理加权回归模型(GWR),与最小二乘法(OLS)进行比较,揭示城市用地的空间分异和不同驱动因子的影响.研究发现:人口密度、人均GDP在城市化进程中起到正向促进作用,到铁路的距离与到城市群中心的距离在城市化进程中起到负向削弱作用.同时,地理加权回归模型考虑到空间现象分异性,其分解成局部参数估计优于OLS提供的全局参数估计,可以深刻地揭示出城市化水平与驱动因子间的复杂关系.
Spatial analysis on agglomeration and diffusion of Beijing-Tianjin-Hebei urban agglomerations
The spatial analysis of the agglomeration and diffusion of Beijing-Tianjin-Hebei urban agglomerations can promote the coordinated development of the regional economy in the Beijing-Tianjin-Hebei region. Based on the urbanization level of Beijing-Tianjin-Hebei urban agglomerations in 2020, this paper built a geographically weighted regression (GWR) model and compared it with the ordinary least square (OLS) method to reveal the spatial differentiation of urban land and the influence of different driving factors. The results show that population density and per capita gross domestic product (GDP) play a positive role in promoting the urbanization process, while the distance to the railway and that to the center of the urban agglomeration play a negative role in weakening the urbanization process. At the same time, the GWR model takes into account the heterogeneity of spatial phenomena, and its decomposition into local parameter estimation is superior to the global parameter estimation provided by OLS. The proposed model can deeply reveal the complex relationship between urbanization level and driving factors.

urban agglomerationspatial heterogeneitydriving factorgeographically weighted regression

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国信司南(北京)地理信息技术有限公司,北京 100048

城市群 空间异质性 驱动因子 地理加权回归

2024

北京测绘
北京市测绘设计研究院,北京测绘学会

北京测绘

影响因子:0.55
ISSN:1007-3000
年,卷(期):2024.38(1)
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