A path-based approach to analyzing the global liner shipping network
Network Science Institute, Northeastern University, Boston, MA, USA
2 School of Economics and Management, Dalian University of Technology, Dalian, China
3 Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
Accepted: 9 March 2022
Published online: 25 March 2022
The maritime shipping network is the backbone of global trade. Data about the movement of cargo through this network comes in various forms, from ship-level Automatic Identification System (AIS) data, to aggregated bilateral trade volume statistics. Multiple network representations of the shipping system can be derived from any one data source, each of which has advantages and disadvantages. In this work, we examine data in the form of liner shipping service routes, a list of walks through the port-to-port network aggregated from individual shipping companies by a large shipping logistics database. This data is inherently sequential, in that each route represents a sequence of ports called upon by a cargo ship. Previous work has analyzed this data without taking full advantage of the sequential information. Our contribution is to develop a path-based methodology for analyzing liner shipping service route data, computing navigational trajectories through the network that both respect the directional information in the shipping routes and minimize the number of cargo transfers between routes, a desirable property in industry practice. We compare these paths with those computed using other network representations of the same data, finding that our approach results in paths that are longer in terms of both network and nautical distance. We further use these trajectories to re-analyze the role of a previously-identified structural core through the network, as well as to define and analyze a measure of betweenness centrality for nodes and edges.
Key words: Complex networks / Network representation / Sequential patterns / Path data / Maritime economics / Liner shipping
© The Author(s) 2022
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