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Urban networks among Chinese cities along "the Belt and Road": A case of web search activity in cyberspace

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Listed:
  • Lu Zhang
  • Hongru Du
  • Yannan Zhao
  • Rongwei Wu
  • Xiaolei Zhang

Abstract

“The Belt and Road” initiative has been expected to facilitate interactions among numerous city centers. This initiative would generate a number of centers, both economic and political, which would facilitate greater interaction. To explore how information flows are merged and the specific opportunities that may be offered, Chinese cities along “the Belt and Road” are selected for a case study. Furthermore, urban networks in cyberspace have been characterized by their infrastructure orientation, which implies that there is a relative dearth of studies focusing on the investigation of urban hierarchies by capturing information flows between Chinese cities along “the Belt and Road”. This paper employs Baidu, the main web search engine in China, to examine urban hierarchies. The results show that urban networks become more balanced, shifting from a polycentric to a homogenized pattern. Furthermore, cities in networks tend to have both a hierarchical system and a spatial concentration primarily in regions such as Beijing-Tianjin-Hebei, Yangtze River Delta and the Pearl River Delta region. Urban hierarchy based on web search activity does not follow the existing hierarchical system based on geospatial and economic development in all cases. Moreover, urban networks, under the framework of “the Belt and Road”, show several significant corridors and more opportunities for more cities, particularly western cities. Furthermore, factors that may influence web search activity are explored. The results show that web search activity is significantly influenced by the economic gap, geographical proximity and administrative rank of the city.

Suggested Citation

  • Lu Zhang & Hongru Du & Yannan Zhao & Rongwei Wu & Xiaolei Zhang, 2017. "Urban networks among Chinese cities along "the Belt and Road": A case of web search activity in cyberspace," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-20, December.
  • Handle: RePEc:plo:pone00:0188868
    DOI: 10.1371/journal.pone.0188868
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    References listed on IDEAS

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    Cited by:

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    2. Yang Changwei & Li Zonghao & Guo Xueyan & Yu Wenying & Jin Jing & Zhu Liang, 2019. "Application of BP Neural Network Model in Risk Evaluation of Railway Construction," Complexity, Hindawi, vol. 2019, pages 1-12, June.
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