SCIENTIA GEOGRAPHICA SINICA ›› 2021, Vol. 41 ›› Issue (3): 437-445.doi: 10.13249/j.cnki.sgs.2021.03.008

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Co-agglomeration and Spatial Similarity: Based on the Analysis of Manufacturing and Producer Services in Guangzhou, China

Zhong Yun1(), Zhao Beilei1, Li Han2   

  1. 1. School of Economics, Jinan University, Guangzhou 510632, Guangdong, China
    2. Department of Geography and Regional Studies, University of Miami, Coral Gables, Florida 33146, USA
  • Received:2020-01-02 Online:2021-03-25 Published:2021-05-11
  • Supported by:
    National Natural Science Foundation of China (41371174,41701183), Jinan University’s Central University Basic Scientific Research Funds (19JNYH09)


The symbiotic relationship between producer services and manufacturing leads to the co-agglomeration of two sectors. As most literature focuses on their functional connections, studies from a spatial perspective are limited. Relying on the firm-level data from, this paper analyzes co-agglomeration patterns and characteristics between six manufacturing sub-sectors and five producer service sub-sectors in Guangzhou, China. We utilized kernel density, Moran’s I, and geo-detector to explore the spatial similarities between those sub-sectors and the underlying mechanisms. Kernel density was employed to visualize the collaborative agglomeration directly. Global and local bivariate Moran’s I statistics were employed to explore the spatial autocorrelation. By employing the geographic detector, we further examined the underlying mechanism of varying co-agglomeration patterns. Major findings are as follows: 1) Manufacturing in Guangzhou is more scattered relative to produce services, with several agglomeration centers in the peripheral regions. 2) The distributions of manufacturing and producer services are spatially similar. Such similarities also vary across sub-sectors. Among these six sub-sectors, technology service companies have the most similar manufacturing distribution, with a high level of polycentricity. On the other hand, financial companies have the lowest similarity to the spatial layout of manufacturing companies. 3) The high-high cluster areas of producer service and manufacturing are located near suburbs such as Panyu District and Baiyun District. The low-low cluster areas are concentrated in distant suburbs like Conghua District and Zengcheng District. The high-low and low-high clusters are located in central urban areas such as Tianhe, Yuexiu, and Haizhu districts, suggesting a spatial mismatch of producer service and manufacturing in Guangzhou’s urban center. 4) Technology service has the strongest impact on all six manufacturing sub-sectors’ spatial configurations, with the highest spatial similarity. In contrast, the cluster of financial firms is unlikely to leads to the agglomeration of manufacturing companies. This divergence is highly associated with the trajectory of the city’s industrial development, functional characteristics of service industries, and spatially varying flexibility of private enterprises.

Key words: co-agglomeration, spatial similarity, geo-detector, inter-sectoral, ?Guangzhou City

CLC Number: 

  • F127