Analyze and process big data to research the competitiveness of urban ports in the Guangdong-Hong Kong-Macao greater bay area

Yuhui Liu

doi:10.1117/12.2625582

9 December 2021 Analyze and process big data to research the competitiveness of urban ports in the Guangdong-Hong Kong-Macao greater bay area

Yuhui Liu

Author Affiliations +

Proceedings Volume 12129, International Conference on Environmental Remote Sensing and Big Data (ERSBD 2021); 121290Y (2021) https://doi.org/10.1117/12.2625582
Event: 2021 International Conference on Environmental Remote Sensing and Big Data, 2021, Wuhan, China

Abstract

Port competitiveness refers to the port's ability to compete for various resources, which reflects the port's position in the region. A correct assessment of port competitiveness will help to better promote the coordinated development of ports and enable ports to participate more deeply in the national development strategy plan, thereby contributing to the sustainable development of ports and hinterland cities. Based on the Automatic Identification System (AIS) data, this paper uses the complex network method to calculate the complex network indicators of 11 urban ports in the Guangdong-Hong Kong-Macao Greater Bay Area, and then uses Borda Count to rank the port competitiveness of the 11 ports. The results of the study show that the ports of Hong Kong, Macao, Guangzhou and Shenzhen in the Greater Bay Area have superior positions and are highly competitive in the internal and external conditions of the ports and in the route network. They are important hubs for the “Belt and Road” construction. Other countries or the port can give priority to cooperating with it. The ports of Jiangmen, Zhongshan and Zhaoqing, which are less competitive, can enhance their competitiveness by improving the investment environment and port operation capabilities.

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Yuhui Liu "Analyze and process big data to research the competitiveness of urban ports in the Guangdong-Hong Kong-Macao greater bay area", Proc. SPIE 12129, International Conference on Environmental Remote Sensing and Big Data (ERSBD 2021), 121290Y (9 December 2021); https://doi.org/10.1117/12.2625582

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