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Connectivity analysis of the global shipping network by eigenvalue decomposition

Author

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  • Jing-Jing Pan
  • Michael G. H. Bell
  • Kam-Fung Cheung
  • Supun Perera
  • Hang Yu

Abstract

Maritime shipping necessitates flexible and cost-effective port access worldwide through the global shipping network. This paper presents an efficient method to identify major port communities, and analyses the network connectivity of the global shipping network based on community structure. The global shipping network is represented by a signless Laplacian matrix which can be decomposed to generate its eigenvectors and corresponding eigenvalues. The largest gaps between the eigenvalues were then used to determine the optimal number of communities within the network. The eigenvalue decomposition method offers the advantage of detecting port communities without relying on a priori assumption about the number of communities and the size of each community. By applying this method to a dataset collected from seven world leading liner shipping companies, we found that the ports are clustered into three communities in the global container shipping network, which is consistent with the major container trade routes. The sparse linkages between port communities indicate where access is relatively poor.

Suggested Citation

  • Jing-Jing Pan & Michael G. H. Bell & Kam-Fung Cheung & Supun Perera & Hang Yu, 2019. "Connectivity analysis of the global shipping network by eigenvalue decomposition," Maritime Policy & Management, Taylor & Francis Journals, vol. 46(8), pages 957-966, November.
    • Handle: RePEc:taf:marpmg:v:46:y:2019:i:8:p:957-966
    DOI: 10.1080/03088839.2019.1647587
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    Cited by:

    1. Zhang, Qiang & Pu, Shunhao & Luo, Lihua & Liu, Zhichao & Xu, Jie, 2022. "Revisiting important ports in container shipping networks: A structural hole-based approach," Transport Policy, Elsevier, vol. 126(C), pages 239-248.
    2. Asadabadi, Ali & Miller-Hooks, Elise, 2020. "Maritime port network resiliency and reliability through co-opetition," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 137(C).
    3. Zhang, Xu & Zhang, Wei & Lee, Paul Tae-Woo, 2020. "Importance rankings of nodes in the China Railway Express network under the Belt and Road Initiative," Transportation Research Part A: Policy and Practice, Elsevier, vol. 139(C), pages 134-147.
    4. Achilleas Tsantis & John Mangan & Agustina Calatayud & Roberto Palacin, 2023. "Container shipping: a systematic literature review of themes and factors that influence the establishment of direct connections between countries," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 25(4), pages 667-697, December.
    5. César Ducruet, 2020. "The geography of maritime networks: A critical review," Post-Print halshs-02922543, HAL.
    6. Ge, Jiawei & fu, Qiang & Zhang, Qiang & Wan, Zheng, 2022. "Regional operating patterns of world container shipping network: A perspective from motif identification," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    7. Sugimura, Yoshihisa & Akakura, Yasuhiro & Yotsushima, Tatsuki & Kawasaki, Tomoya, 2023. "Evaluation of Japanese port policies through network analysis," Transport Policy, Elsevier, vol. 135(C), pages 59-70.
    8. Nicanor García Álvarez & Belarmino Adenso-Díaz & Laura Calzada-Infante, 2021. "Maritime Traffic as a Complex Network: a Systematic Review," Networks and Spatial Economics, Springer, vol. 21(2), pages 387-417, June.
    9. Ducruet, César, 2020. "The geography of maritime networks: A critical review," Journal of Transport Geography, Elsevier, vol. 88(C).

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