Community Structures in Networks of Disaggregated Cargo Flows to Maritime Ports

We investigate community structures in disaggregated cargo flows that are part of the global maritime trade. Individual origin–destination flows of containerized cargo on the hinterland-side of the global maritime trade collectively form a complex and large-scale network. We use community-detection algorithms to identify natural communities that exist in such highly asymmetric networks. The analysis compares the structures that are identified in two geographic regions: China and Europe. We trace shipments by their trajectory patterns considering domestic movement, cross-country movement, and trans-shipments. Then we use the Infomap and Walktrap algorithms to detect structural equivalence between shipments from hinterland cities to port cities. Data consist of individual flows of containerized trade bound for the United States in October 2006. We draw conclusions on the relative performance of the two algorithms on asymmetric data networks, on the effects of the geographic context and type of flows on their performance, and on the spatial integrity of community structures and the impact of market contestability on the semantics of detected communities.