Higher-order Representations of Liner Shipping Data

Global maritime cargo shipping handles about 80% of global trade volume and can be represented as a complex network. We examine a dataset of liner shipping service routes, where each route is a walk representing the path of a cargo ship through the port-to-port network. These routes were scheduled by shipping companies during 2015 and aggregated by Alphaliner, a leading source of maritime trade data. Previous work on this data used an undirected co-occurrence representation, treating every route as a clique. We show that this representation cannot accurately model cargo moving through the network, since directionality of the edges is lost, and indirect connections are made direct. We compare 3 representations of service route data, discussing their strengths and weaknesses as well as their relation to existing higher-order network models. We present results from a new method for computing navigational paths for cargo through the network that respect the directionality inherent to the routes and balance factors important to industry practice, such as shipping distance and path length. We conclude by showing the importance of this representational choice in analyzing the structural core of the shipping network.