TY - JOUR
T1 - Spatial heterogeneity of ports in the global maritime network detected by weighted ego network analysis
AU - Liu, Chengliang
AU - Wang, Jiaqi
AU - Zhang, Hong
N1 - Publisher Copyright:
© 2017 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2018/1/2
Y1 - 2018/1/2
N2 - More extensive attention has been paid to the heterogeneity of maritime transport network in topological rather than in spatial aspects. However, the importance of links and the roles of neighbors of a node has been ignored if not all. To fill this gap, this article introduced the approach of weighted ego network analysis (WENA) to visualize the spatial heterogeneity of the maritime network at global and local levels. The topological connectivity graph of the global marine network was derived, and its structural properties were analyzed. It is found out that the values of the degree of ports follow power-law distribution, which indicates that the global marine network is scale-free, that is, there are few well-connected ports and a majority of less connected ports. The spatial disparities of the network can be described by a core–periphery pattern. In global, most of the hubs or ports with extremely high values of degree locate in the big-three maritime regions including Far East, North America, and West Europe. Along the peripheral belts of the three regions, there are lots of less connected small ports. A different hierarchical structure of six continents was captured by WENA. It is found that Europe, Asia, North America, and Africa showcase a pyramid-shaped hierarchical structure with a scale-free feature similar to the entire network, while South America and Oceania exhibit the fusiform hierarchy like small-world networks. It is proposed that such spatial inequality and heterogeneity were caused by the geographical environments such as the hub-and-spoke organization, the embedded trade pattern, and the proximity of location. These findings help us to understand the characteristics of the international trade pattern and shed light on the strategies of development for the industry stakeholders.
AB - More extensive attention has been paid to the heterogeneity of maritime transport network in topological rather than in spatial aspects. However, the importance of links and the roles of neighbors of a node has been ignored if not all. To fill this gap, this article introduced the approach of weighted ego network analysis (WENA) to visualize the spatial heterogeneity of the maritime network at global and local levels. The topological connectivity graph of the global marine network was derived, and its structural properties were analyzed. It is found out that the values of the degree of ports follow power-law distribution, which indicates that the global marine network is scale-free, that is, there are few well-connected ports and a majority of less connected ports. The spatial disparities of the network can be described by a core–periphery pattern. In global, most of the hubs or ports with extremely high values of degree locate in the big-three maritime regions including Far East, North America, and West Europe. Along the peripheral belts of the three regions, there are lots of less connected small ports. A different hierarchical structure of six continents was captured by WENA. It is found that Europe, Asia, North America, and Africa showcase a pyramid-shaped hierarchical structure with a scale-free feature similar to the entire network, while South America and Oceania exhibit the fusiform hierarchy like small-world networks. It is proposed that such spatial inequality and heterogeneity were caused by the geographical environments such as the hub-and-spoke organization, the embedded trade pattern, and the proximity of location. These findings help us to understand the characteristics of the international trade pattern and shed light on the strategies of development for the industry stakeholders.
KW - Global maritime network
KW - hierarchical structure
KW - spatial heterogeneity
KW - weighted average centrality rank
KW - weighted ego network analysis
UR - https://www.scopus.com/pages/publications/85021803385
U2 - 10.1080/03088839.2017.1345019
DO - 10.1080/03088839.2017.1345019
M3 - 文章
AN - SCOPUS:85021803385
SN - 0308-8839
VL - 45
SP - 89
EP - 104
JO - Maritime Policy and Management
JF - Maritime Policy and Management
IS - 1
ER -