Tropical Geography ›› 2021, Vol. 41 ›› Issue (1): 114-123.doi: 10.13284/j.cnki.rddl.003307

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The influence of Subways on Service Industry Agglomeration: Taking Guangzhou as an Example

Hongping Zhu1(), Wentao Zhu2(), Rongbao Zheng1   

  1. 1.Guangdong University of Technology, Guangzhou 510520, China
    2.Jimei University, Xiamen 361021, China
  • Received:2020-08-24 Revised:2020-11-03 Online:2021-01-05 Published:2021-02-19
  • Contact: Wentao Zhu;


Agglomeration is an important feature of the spatial distribution of an urban internal service industry. Most of the previous studies on the influencing factors of urban internal service industry agglomeration have ignored traffic factors, especially the influence of subways. The improvement of traffic convenience can often affect the location of a service industry by gathering a flow of people. With the rapid growth in the number of China's metro cities, it is necessary to evaluate the impact of subways on the service industry activities within the city. In addition, most of the previous literature has not considered the spatial dependence of service activities, and there is little discussion on the heterogeneity of the service industry. In view of this, based on POI(Point of Information) data in Guangzhou as an example, this study uses a spatial autoregressive (SAR) model to identify the impact of subways on service industry agglomeration and analyzes the heterogeneity of different types of service industry. The results are summarized as follows. 1) Using Moran's I to measure the spatial correlation of Guangzhou's service industry agglomeration, the results indicate strong spatial correlation characteristics. In addition, according to the regression results of the SAR model, the spatial lag coefficient is significantly positive, which indicates that the service industry agglomeration has a considerable spatial dependence. Specifically, the degree of local service industry agglomeration will increase with an improvement in the surrounding areas. 2) The opening of subways has a significant positive impact on the spatial agglomeration of Guangzhou's service industry, which could increase the agglomeration levels in urban areas. One possible reason for this is that subways bring a floating population and reduce transaction costs. 3) There are different industries within the service industry, each of which has varying characteristics; thus, the impact of subways is heterogeneous, specifically as follows: the impact of a subway opening is higher on the wholesale and retail industry, accommodation and catering industry, and other life services than on other service industries but not significant on scientific research and technical services. 4) The impact of the metro on service industry agglomeration is also affected by the level of regional economic development. In areas with a higher level of economic development, the promotion effect of the metro on service industry agglomeration is more obvious. 5) Finally, a metro transfer station has a higher impact than a non-transfer station. One possible reason for this is that a metro transfer station is the intersection of multiple metro lines, which can often attract a greater flow of people and promote the flow of production factors, making service industry agglomeration more likely. The main contributions of this paper are as follows: first, taking Guangzhou, which has a well-developed metro network, as an example, this study evaluates the impact of the metro on service industry agglomeration and analyzes industry heterogeneity. Second, on the basis of constructing a spatial distance matrix of each economic unit, when considering the spatial dependence of service activities, the method of spatial economics was used to investigate the impact of subways on service industry agglomeration; third, taking big data represented by POIs as the source data, and taking streets and towns as the units of analysis, it more objectively and accurately reflects the spatial distribution characteristics of service industry agglomeration in cities.

Key words: subway, service industry agglomeration, heterogeneity, spatial autoregressive model, Guangzhou City

CLC Number: 

  • F532