Identifying port maritime communities: application to the Spanish case

Nicanor García,Belarmino Adenso-Díaz,Laura Calzada-Infante

doi:10.1186/s12544-021-00495-1

Abstract

The aim of this paper is to detect port maritime communities sharing similar international trade patterns, by a modelisation of maritime traffic using a bipartite weighted network, providing decision-makers the tools to search for alliances or identify their competitors. Our bipartite weighted network considers two different types of nodes: one represents the ports, while the other represents the countries where there are major import/export activity from each port. The freight traffic among both types of nodes is modeled by weighting the volume of product transported. To illustrate the model, the Spanish case is considered, with the data segmented by each type of traffic for a fine tuning. A sort of link prediction is possible, finding for those communities with two or more ports, countries that are part of the same community but with which some ports do not have yet significant traffic. The evolution of the traffics is analyzed by comparing the communities in 2009 and 2019. The set of communities formed by the ports of the Spanish port system can be used to identify global similarities between them, comparing the membership of the different ports in communities for both periods and each type of traffic in particular.

Highlights

Maritime transport, which is responsible for around four-fifths of the world merchandise trade traffic, has proved to be the backbone of globalised trade and the manufacturing supply chain
It is possible to apply these kinds of algorithms directly to our dataset, experiments on real-world bipartite networks show that random walk based algorithms such as Louvain and Infomap (MDL algorithm) are more functional in detecting the communities in bipartite networks than the aforementioned algorithms [4]
4 Results To illustrate the process, given the large amount of traffic categories, some products having a major impact on the GDP in 2019 [35] have been selected for a detailed analysis

Summary

Introduction

Maritime transport, which is responsible for around four-fifths of the world merchandise trade traffic, has proved to be the backbone of globalised trade and the manufacturing supply chain. Before the global economic and health crisis of COVID-19, in the last global report, forecasts for the period 2019–2024 predicted an increase of 3.4% for maritime transport in that period, with 11 billion tonnes and an estimated maritime trade of 793.26 million TEUs handled in container ports worldwide with the following distribution: 64% in Asia, 16% in Europe, 8% in North America, 7% in Latin America and the Caribbean, 4% Africa, and 2% Oceania [7] In such a competitive and changing environment, it is critical to know the strengths available and all the information that. This identification process requires a great knowledge of the port environment, and the use of high-level resources, involving both technological

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Discussion

Conclusion