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Measuring the effect of distance on the network topology of the Global Container Shipping Network

Author

Listed:
  • Dimitrios Tsiotas

    (Agricultural University of Athens)

  • César Ducruet

    (EconomiX - EconomiX - UPN - Université Paris Nanterre - CNRS - Centre National de la Recherche Scientifique)

Abstract

This paper examines how spatial distance affects network topology on empirical data concerning the Global Container Shipping Network (GCSN). The GCSN decomposes into 32 multiplex layers, defined at several spatial levels, by successively removing connections of smaller distances. This multilayer decomposition approach allows studying the topological properties of each layer as a function of distance. The analysis provides insights into the hierarchical structure and (importing and exporting) trade functionality of the GCSN, hub connectivity, several topological aspects, and the distinct role of China in the network's structure. It also shows that bidirectional links decrease with distance, highlighting the importance of asymmetric functionality in carriers' operations. It further configures six novel clusters of ports concerning their spatial coverage. Finally, it reveals three levels of geographical scale in the structure of GCSN (where the network topology significantly changes): the neighborhood (local connectivity); the scale of international connectivity (mesoscale or middle connectivity); and the intercontinental market (large scale connectivity). The overall approach provides a methodological framework for analyzing network topology as a function of distance, highlights the spatial dimension in complex and multilayer networks, and provides insights into the spatial structure of the GCSN, which is the most important market of the global maritime economy.

Suggested Citation

  • Dimitrios Tsiotas & César Ducruet, 2021. "Measuring the effect of distance on the network topology of the Global Container Shipping Network," Post-Print halshs-03408185, HAL.
    • Handle: RePEc:hal:journl:halshs-03408185
    DOI: 10.1038/s41598-021-00387-3
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-03408185
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    References listed on IDEAS

    as
    1. César Ducruet & Laurent Beauguitte, 2014. "Spatial Science and Network Science: Review and Outcomes of a Complex Relationship," Networks and Spatial Economics, Springer, vol. 14(3), pages 297-316, December.
    2. J. Buhl & J. Gautrais & N. Reeves & R. V. Solé & S. Valverde & P. Kuntz & G. Theraulaz, 2006. "Topological patterns in street networks of self-organized urban settlements," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 49(4), pages 513-522, February.
    3. César Ducruet, 2013. "Network diversity and maritime flows," Post-Print hal-03246957, HAL.
    4. César Ducruet & Laurent Beauguitte, 2014. "Network science and spatial science : Review and outcomes of a complex relationship," Post-Print hal-03246947, HAL.
    5. Dimitrios Tsiotas & Serafeim Polyzos, 2018. "The Complexity in the Study of Spatial Networks: an Epistemological Approach," Networks and Spatial Economics, Springer, vol. 18(1), pages 1-32, March.
    6. Ducruet, César, 2013. "Network diversity and maritime flows," Journal of Transport Geography, Elsevier, vol. 30(C), pages 77-88.
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    Citations

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    Cited by:

    1. Dimitrios Tsiotas & Vassilis Tselios, 2023. "Dimension Reduction in the Topology of Multilayer Spatial Networks: The Case of the Interregional Commuting in Greece," Networks and Spatial Economics, Springer, vol. 23(1), pages 97-133, March.
    2. Wu, Jiaxin & Lu, Jing & Zhang, Lingye & Fan, Hanwen, 2024. "Spatial heterogeneity among different-sized port communities in directed-weighted global liner shipping network," Journal of Transport Geography, Elsevier, vol. 114(C).
    3. Chen, Yu & Lu, Yuqi & Jin, Cheng, 2024. "Spatiotemporal differentiation calendar for car and truck flow on expressways: A case study of Jiangsu, China," Journal of Transport Geography, Elsevier, vol. 116(C).
    4. Dimitrios TSIOTAS, 2022. "A Network-Based Algorithm For Computing Keynesian Income Multipliers In Multiregional Systems," Regional Science Inquiry, Hellenic Association of Regional Scientists, vol. 0(2), pages 25-46, June.

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