PROGRESS IN GEOGRAPHY ›› 2019, Vol. 38 ›› Issue (12): 1843-1853.doi: 10.18306/dlkxjz.2019.12.002

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City network by mobility and attention indices: A comparison of Guangzhou and Shenzhen

WU Xuan, YANG Jiawen*()   

  1. School of Urban Planning and Design, Peking University Shenzhen Graduate School, Shenzhen 518055, Guangdong, China
  • Received:2019-01-02 Revised:2019-03-19 Online:2019-12-28 Published:2019-12-28
  • Contact: YANG Jiawen
  • Supported by:
    National Natural Science Foundation of China(51678004)


City networks have experienced rapid reconstruction in the past decades due to the development of city-regions. In the Guangdong-Hong Kong-Macao Greater Bay Area, Guangzhou and Shenzhen are two pivotal cities. They play key roles in promoting regional development. Therefore, it is of great significance to identify their influence areas, which can inform urban management and regional planning. Meanwhile, increasing availability of social media data creates opportunities for relevant research. The pervasive presence of location-based services and the associated content make it possible for researchers to gain an unprecedented access to the direct records of human activities and perceptions. Much of existing literature, however, pays little attention to the differences in multi-scale network or to the relationship between the real-world network and virtual network, which are both presented in datasets of this kind. Our research contributes to the literature in both the methodological and the empirical aspects. First, we investigated the node and link characteristics of the influence areas of Guangzhou and Shenzhen by computing social network indicators with a dataset of almost 10 million Sina Microblog records between January 1 and February 6, 2018. Indices of mobility and attention were computed based on characteristics such as consecutive locations, degree centrality, closeness centrality, and average radius of gyration. These indices help to catch the interaction between real and virtual networks. Second, in order to understand inter-city mobility and attention characteristics of Guangzhou and Shenzhen, we mapped city networks of multi-scale, where edge weights denote interaction strengths. Third, our analysis confirmed that the Sina Microblog data exhibit similar statistical properties as other city network datasets. Based on the result of analyses, we argue that Guangzhou had more balanced influence in various directions, representing efficiency in hinterland connection and resource integration. Shenzhen's area of influence was relatively concentrated, with a strong tie with neighboring Hong Kong. Overall, Guangzhou competes better in the mobility network while Shenzhen competes better in the attention network. A complementary relationship was also identified between those two networks. In conclusion, we propose that Guangzhou and Shenzhen took advantage of their respective role as the hubs of regional transportation and innovation as well as what they have already accumulated and their connections with other parts of the world. They should help to build a coordinated and competitive Guangdong-Hong Kong-Macao Greater Bay Area. Our research results offer some insights for policymakers to interpret the geographic dynamics and make relevant decisions in this region. It also provides some references and inputs for analyzing social media data for the research community.

Key words: city network, Microblog, mobility index, attention index, Guangdong-Hong Kong-Macao Greater Bay Area