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

Kang Wu, Jingxian Tang and Ying Long
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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

Sustainability, 2019, vol. 11, issue 21, 1-18

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.

Keywords: commute pattern; community detection; regional delineation; big data; functional regions (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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