An automatic algorithm for generating seaborne transport pattern maps based on AIS
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We provide an automatic algorithm proceeding from raw automatic identification system (AIS) data, through filters, highlighting vessel types, cargo, or regions, to discrete transport patterns, describing the geographic, directional, and total volume of transport. The designed algorithm automatically detects major ports or zones, where vessels are stationary and close to land, and groups them hierarchically in order to determine the end nodes in a transport matrix. In conjunction with vessel type, size, and draught information, the transport matrix is then used to compile transported tonnage and vessel count, to display as trade flows between nodes. In addition, by clicking on individual routes, the system also provides quantitative details, such as summed tonnage, or passing vessel counts. We use trades carried out by VLCC and Supramax bulk carriers as examples to demonstrate the results. The contribution of the research is threefold. First, we use AIS data to aggregate “real-time” trade flows. This substantially reduces latency from the traditional way of mapping flows and generating trade statistics. Second, the ability to visualize changes in seasonal or regional trading patterns enables economists and policy makers to monitor changes at a macro level. Finally, the ability to dripple down to the individual ship level allows commercial traders to monitor vessels or company performances, which provides valuable information in trading and decision-making.
KeywordsAIS Trade flow Visualization Transport matrix
This research was funded by the Research Council of Norway as part of the project: “Mapping vessel behaviour and cargo flows” (CARGOMAP: project no. 239104).
- Bremm, S., T. von Landesberger, G. Andrienko and N. Andrienko. 2011. Interactive analysis of object group changes over time. In Proceedings of the international workshop on visual analytics, eds. S. Miksch and G. Santucci, 41–44. Bergen, Norway: The Eurographics Association.Google Scholar
- Daae Lampe, O., J. Kehrer, and H. Hauser. 2010. Visual analysis of multivariate movement data using interactive difference views. In Proceedings of International Workshop on Vision, Modeling and Visualization, eds. A. Kolb, R. Koch and C. Rezk-Salama, 315–322. Siegen, Germany: The Eurographics Association.Google Scholar
- Dickerson, M., D. Eppstein, M. T. Goodrich, and J. Y. Meng. 2003. Confluent drawings: Visualizing non-planar diagrams in a planar way. In: International Symposium on Graph Drawing. s.l.Google Scholar
- Fearnleys. 2002. Annual Review. Oslo: Fearnleys.Google Scholar
- Gross, D. 2003. The shipping news. The best economic indicator you’ve never heard of. http://www.slate.com/articles/business/moneybox/2003/10/the_shipping_news.2.html.
- Maurer, A. and C. Degain. 2010. Globalization and trade flows: what you see is not what you get! WTO Economic research and statistics division working paper ERSD-2010-12.Google Scholar
- Nossum, B. 1996. The evolution of dry bulk shipping 1945–1990. Oslo: Birger Nossum.Google Scholar
- Patterson, L. 2010. Marine capital monthly report, July 2010.Google Scholar
- Phan, D., L. Xiao, R. Yeh, P. Hanrahan, and T. Winograd. 2005. Flow map layout. In proceedings of the IEEE symposium on information visualization, ed. J. S. Matt Ward, 219–224. Washington DC, USA: IEEE Computer Society.Google Scholar
- Wörner, M. and T. Ertl. 2012. Visual analysis of public transport vehicle movement. In Proceedings of the international workshop on visual analytics (EuroVA 2012), 79–83. Vienna, Austria: Eurographics Association.Google Scholar