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Delineating the Regional Economic Geography of China by the Approach of Community Detection

Author

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  • Kang Wu

    (Beijing Key Laboratory of Megaregions Sustainable Development Modelling and School of Urban Economics and Public Administration, Capital University of Economics and Business, Beijing 100070, China)

  • Jingxian Tang

    (School of Architecture, Tsinghua University, Beijing 100084, China)

  • Ying Long

    (School of Architecture and Hang Lung Center for Real Estate, Tsinghua University, Beijing 100084, China)

Abstract

With the obvious regionalization trend in the new period of urbanization in China, the scientific delineation of functional regions (FRs) at different scales has become a heated topic recently. Since the 20th century, western academia has formed a basic idea of metropolitan areas’ (MAs) delineation based on population density and commuting rate, for which the subjectivity of threshold setting is difficult to overcome. In this study, community detection algorithms from the field of network science are employed, namely the Louvain algorithm with adjustable resolutions and Combo with high-precision output, respectively. We take the nationwide car-hailing data set as an example to explore a bottom-up method for delineating regional economic geography at different scales based on the interconnection strength between nodes. It was found that most of the prefecture-level cities in China have a dominant commuting region and two or three secondary commuting sub-regions, while regional central cities have extended their commuting hinterlands over jurisdictional boundaries, which is not common due to the larger initial administrative divisions and the comprehensive development niveau of cities. The feasibility and limitation of community detection partitioning algorithms in the application of regional science are verified. It is supposed to be widely used in regional delimitation supported by big data. Both of the two algorithms show a shortage of ignorance of spatial proximity. It is necessary to explore new algorithms that can adjust both accuracy and spatial distance as parameters.

Suggested Citation

  • Kang Wu & Jingxian Tang & Ying Long, 2019. "Delineating the Regional Economic Geography of China by the Approach of Community Detection," Sustainability, MDPI, vol. 11(21), pages 1-18, October.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:21:p:6053-:d:282102
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    Cited by:

    1. Peng Gao & Dan He & Zhijing Sun & Yuemin Ning, 2020. "Characterizing functionally integrated regions in the Central Yangtze River Megaregion from a city‐network perspective," Growth and Change, Wiley Blackwell, vol. 51(3), pages 1357-1379, September.
    2. Pengfei Ban & Wei Zhan & Qifeng Yuan & Xiaojian Li, 2021. "Delineating the Urban Areas of a Cross-Boundary City with Open-Access Data: Guangzhou–Foshan, South China," Sustainability, MDPI, vol. 13(5), pages 1-17, March.
    3. Yifu Ou & Euijune Kim & Xingjian Liu & Kyung-Min Nam, 2023. "Delineating functional regions from road networks: The case of South Korea," Environment and Planning B, , vol. 50(6), pages 1677-1694, July.

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