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Optimizing the spatial structure of urban agglomeration: based on social network analysis

Author

Listed:
  • Xinhua Zhu

    (Hohai University)

  • Qianli Wang

    (Hohai University)

  • Peifeng Zhang

    (Hohai University)

  • Yunjiang Yu

    (Shanghai Lixin University of Accounting and Finance)

  • Lingling Xie

    (Guangxi University of Finance and Economics)

Abstract

The development of urban agglomerations is fundamentally influenced by their spatial structure. Based on the case of Beijing–Tianjin–Hebei (BTH) urban agglomeration, this study aims to evaluate the spatial connection intensity and spatial structure characteristics of BTH urban agglomeration and provide tenable suggestions to optimize the BTH urban agglomeration. The TOPSIS model, the revised gravity model, and social network analysis are employed respectively. The findings indicate that: (1) Wide discrepancy exists within the agglomeration in terms of spatial connection intensity. Cities in the central regions have the greatest connection intensity with other cities and then the connection intensity decreases from the middle to the northern and southern parts of the agglomeration. The connection intensity between core cities such as Beijing, Tianjin and Shijiazhuang and the other non-core cities is much lower than the connection intensity among core cities. (2) The social network of BTH urban agglomeration is relatively low-dense and unstable. The centrality degrees of Beijing and Tianjin are the highest, making them the core of the spatial structure of the agglomeration. (3) The BTH urban agglomeration can be divided as three circle layers and four cohesive subgroups, among which Shijiazhuang and Hengshui are two cities dissociating from other cities. Given this, it is urgent that the government should disengage Beijing from its non-capital functions, promote Tangshan and Baoding as bridge cities, so as to enhance the connection intensity between core cities and edge cities, turn Shijiazhuang into the third growth pole other than Beijing and Tianjin, and eventually revolutionize the monopolar structure into a multipolar one.

Suggested Citation

  • Xinhua Zhu & Qianli Wang & Peifeng Zhang & Yunjiang Yu & Lingling Xie, 2021. "Optimizing the spatial structure of urban agglomeration: based on social network analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(2), pages 683-705, April.
  • Handle: RePEc:spr:qualqt:v:55:y:2021:i:2:d:10.1007_s11135-020-01016-3
    DOI: 10.1007/s11135-020-01016-3
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    References listed on IDEAS

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    1. Zhu, Xinhua & Qian, Tiannan & Wei, Yigang, 2020. "Do high-speed railways accelerate urban land expansion in China? A study based on the multi-stage difference-in-differences model," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    2. Xinhua Zhu & Yigang Wei & Yani Lai & Yan Li & Sujuan Zhong & Chun Dai, 2019. "Empirical Analysis of the Driving Factors of China’s ‘Land Finance’ Mechanism Using Soft Budget Constraint Theory and the PLS-SEM Model," Sustainability, MDPI, vol. 11(3), pages 1-21, January.
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    2. Ma, Wen & Fang, Zhuoqiong & Zhang, Xiangfeng, 2023. "Comparative analysis of structural characteristics of China's 18 typical urban agglomerations based on flows of various elements," Ecological Modelling, Elsevier, vol. 479(C).
    3. Chen, Zhe & Sarkar, Apurbo & Rahman, Airin & Li, Xiaojing & Xia, Xianli, 2022. "Exploring the drivers of green agricultural development (GAD) in China: A spatial association network structure approaches," Land Use Policy, Elsevier, vol. 112(C).
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    6. Jieping Chen & Chao Ma & Shijun Chen, 2024. "Determinant of the tourism economy in Chinese cities: from an urban centrality perspective," Tourism Economics, , vol. 30(1), pages 44-66, February.

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