skip to main content
10.1145/3275116.3275124acmotherconferencesArticle/Chapter ViewAbstractPublication PagesmindtrekConference Proceedingsconference-collections
research-article
Open Access

Visualising maritime vessel open data for better situational awareness in ice conditions

Published:10 October 2018Publication History

ABSTRACT

Situational awareness of maritime vessels in ice conditions is important for the operation of supply chains. In the artic sea areas, the ice conditions pose a major challenge for maritime vessels getting stuck in the ice and being significantly delayed in arrival to harbor. Data science and open data provide new opportunities to overcome these challenges. This paper introduces available open data sources and data visualizations that can be used to develop applications, for example, for detecting maritime vessel collision, predicting estimated time of arrival to harbor, as well as maritime vessel route optimization in ice conditions. The paper begins by introducing available open data sources and existing computational studies on maritime vessels in ice conditions, then presents the developed data science solution and visualizations of the open data along with the open source software code, and finally concludes with a discussion on the potential application areas and opportunities for further research.

References

  1. Arbetter, T.E., Curry, J.A., Maslanik, J.A., Arbetter, T.E., Curry, J.A. and Maslanik, J.A. 1999. Effects of Rheology and Ice Thickness Distribution in a Dynamic-Thermodynamic Sea Ice Model. Journal of Physical Oceanography. 29, 10 (Oct. 1999), 2656--2670.Google ScholarGoogle ScholarCross RefCross Ref
  2. Bitz, C.M., Holland, M.M., Weaver, A.J. and Eby, M. 2001. Simulating the ice-thickness distribution in a coupled climate model. Journal of Geophysical Research: Oceans. 106, C2 (Feb. 2001), 2441--2463.Google ScholarGoogle Scholar
  3. boyd, danah and Crawford, K. 2011. Six Provocations for Big Data. SSRN Electronic Journal. (Sep. 2011).Google ScholarGoogle Scholar
  4. Bruns, A. 2013. Faster than the speed of print: Reconciling 'big data' social media analysis and academic scholarship. First Monday. 18, 10 (Oct. 2013).Google ScholarGoogle ScholarCross RefCross Ref
  5. Dageville, B. et al. 2016. The Snowflake Elastic Data Warehouse. Proceedings of the 2016 International Conference on Management of Data - SIGMOD '16 (New York, New York, USA, 2016), 215--226. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Ducruet, C. 2017. Advances in Shipping Data Analysis and Modeling: Tracking and Mapping Maritime Flows in the Age of Big Data. Routledge.Google ScholarGoogle Scholar
  7. Goerlandt, F., Montewka, J., Zhang, W. and Kujala, P. 2017. An analysis of ship escort and convoy operations in ice conditions. Safety Science. 95, (Jun. 2017), 198--209.Google ScholarGoogle Scholar
  8. Guinness, R., Saarirnäki, J., Ruotsalainen, L., Kuusniemi, H., Goerlandt, J., Montewka, J., Berglund, R. and Kotovirta, V. 2014. A method for ice-aware maritime route optimization. Position, Location and Navigation Symposium-PLANS 2014 (2014), 1371--1378.Google ScholarGoogle Scholar
  9. Haapala, J., Lönnroth, N. and Stössel, A. 2005. A numerical study of open water formation in sea ice. Journal of Geophysical Research. 110, C9 (2005), C09011.Google ScholarGoogle ScholarCross RefCross Ref
  10. Harati-Mokhtari, A., Wall, A., Brooks, P. and Wang, J. 2007. Automatic Identification System (AIS): Data Reliability and Human Error Implications. Journal of Navigation. 60, 03 (Sep. 2007), 373.Google ScholarGoogle ScholarCross RefCross Ref
  11. Hibler, W.D. 1979. A Dynamic Thermodynamic Sea Ice Model. Journal of Physical Oceanography. 9, 4 (Jul. 1979), 815--846.Google ScholarGoogle ScholarCross RefCross Ref
  12. Ilvonen, I., Jussila, J. and Kärkkäinen, H. 2015. Towards a business-driven process model for knowledge security risk management: Making sense of knowledge risks. International Journal of Knowledge Management (IJKM). 11, 4 (2015), 1--18. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Jalonen, H. and Lönnqvist, A. 2009. Predictive business -- fresh initiative or old wine in a new bottle. Management Decision. 47, 10 (Nov. 2009), 1595--1609.Google ScholarGoogle ScholarCross RefCross Ref
  14. Karvonen, J., Simila, M. and Heiler, I. Ice thickness estimation using SAR data and ice thickness history. IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477) 74--76.Google ScholarGoogle Scholar
  15. Karvonen, J., Simila, M. and Lehtiranta, J. 2007. SAR-based estimation of the baltic sea ice motion. 2007 IEEE International Geoscience and Remote Sensing Symposium (2007), 2605--2608.Google ScholarGoogle ScholarCross RefCross Ref
  16. Kitada, M., Baldauf, M., Mannov, A., Svendsen, P.A., Baumler, R., Schröder-Hinrichs, J.-U., Dalaklis, D., Fonseca, T., Shi, X. and Lagdami, K. 2019. Command of Vessels in the Era of Digitalization. Advances in Human Factors, Business Management and Society. J. Kantola, S. Nazir, and T. Barath, eds. Springer. 339--350.Google ScholarGoogle Scholar
  17. Kotovirta, V., Jalonen, R., Axell, L., Riska, K. and Berglund, R. 2009. A system for route optimization in ice-covered waters. Cold Regions Science and Technology. 55, 1 (Jan. 2009), 52--62.Google ScholarGoogle ScholarCross RefCross Ref
  18. Kuuliala, L., Kujala, P., Suominen, M. and Montewka, J. 2017. Estimating operability of ships in ridged ice fields. Cold Regions Science and Technology. 135, (2017), 51--61.Google ScholarGoogle Scholar
  19. Kvamstad, B., Fjørtoft, K.E., Bekkadal, F., Marchenko, A. V. and Ervik, J.L. 2009. A Case Study from an Emergency Operation in the Arctic Seas. TransNav: International Journal on Marine Navigation and Safety of Sea Transportation. Vol. 3, nr 2 (2009).Google ScholarGoogle Scholar
  20. Linstedt, D. and Olschimke, M. 2015. Building a scalable data warehouse with data vault 2.0. Morgan Kaufmann. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Mazzarella, F., Vespe, M. and Tarchi, D. 2016. AIS reception characterisation for AIS on/off anomaly detection. Information fusion (fusion) (2016), 1867--1873.Google ScholarGoogle Scholar
  22. Mou, J.M., Tak, C. van der and Ligteringen, H. 2010. Study on collision avoidance in busy waterways by using AIS data. Ocean Engineering. 37, 5-6 (Apr. 2010), 483--490.Google ScholarGoogle ScholarCross RefCross Ref
  23. Nasrabadi, N.M. 2007. Pattern Recognition and Machine Learning. Journal of Electronic Imaging. 16, 4 (Jan. 2007), 049901.Google ScholarGoogle Scholar
  24. Nita, S. and Mihailescu, M. 2017. IMPORTANCE OF BIG DATA IN MARITIME TRANSPORT. Scientific Bulletin "Mircea cel Batran" Naval Academy. 20, 1 (2017).Google ScholarGoogle Scholar
  25. Porter, M.E. and Heppelmann, J.E. 2015. How smart, connected products are transforming companies. Harvard Business Review. 93, 10 (2015), 96--114.Google ScholarGoogle Scholar
  26. Puonti, M., Järvi, J. and Mikkonen, T. 2018. A Continuous Delivery Framework for Business Intelligence. Information Modelling and Knowledge Bases XXIX. IOS Press. 248--262.Google ScholarGoogle Scholar
  27. Puonti, M., Lehtonen, T., Luoto, A., Aaltonen, T. and Aho, T. 2016. Towards Agile Enterprise Data Warehousing. The Eleventh International Conference on Software Engineering Advances (Rome, 2016).Google ScholarGoogle Scholar
  28. Ray, C., Iphar, C. and Napoli, A. 2016. Methodology for Real-Time Detection of AIS Falsification. Maritime Knowledge Discovery and Anomaly Detection Workshop (2016), 74--77.Google ScholarGoogle ScholarCross RefCross Ref
  29. Ristic, B., Scala, B., Morelande, M. and Gordon, N. 2008. Statistical analysis of motion patterns in AIS data: Anomaly detection and motion prediction. Fusion 2008: Proceedings of the 11th International Conference on Information Fusion. (Cologne, 2008).Google ScholarGoogle Scholar
  30. Sull, D. and Wang, Y. 2005. Made In China: What Western Managers Can Learn from Trailblazing Chinese entrepreneurs. Harvard Business Press.Google ScholarGoogle Scholar
  31. Tarovik, O. V., Topaj, A., Bakharev, A.A., Kosorotov, A. V., Krestyantsev, A.B. and Kondratenko, A.A. 2017. Multidisciplinary Approach to Design and Analysis of Arctic Marine Transport Systems. Volume 8: Polar and Arctic Sciences and Technology; Petroleum Technology (Jun. 2017), V008T07A005.Google ScholarGoogle Scholar
  32. Tetreault, B.J. Use of the Automatic Identification System (AIS) for Maritime Domain Awareness (MDA). Proceedings of OCEANS 2005 MTS/IEEE 1--5.Google ScholarGoogle Scholar
  33. Valdez Banda, O.A., Goerlandt, F., Montewka, J. and Kujala, P. 2015. A risk analysis of winter navigation in Finnish sea areas. Accident Analysis & Prevention. 79, (Jun. 2015), 100--116.Google ScholarGoogle Scholar
  34. Vassakis, K., Petrakis, E. and Kopanakis, I. 2018. Big Data Analytics: Applications, Prospects and Challenges. Springer, Cham. 3--20.Google ScholarGoogle Scholar
  35. Weintrit, A. 2009. Marine navigation and safety of sea transportation. CRC.Google ScholarGoogle Scholar
  36. Zaman, I., Pazouki, K., Norman, R., Younessi, S. and Coleman, S. 2017. Challenges and Opportunities of Big Data Analytics for Upcoming Regulations and Future Transformation of the Shipping Industry. Procedia engineering. 194, (2017), 537--544.Google ScholarGoogle Scholar
  37. Zanin, M., Papo, D., Sousa, P.A., Menasalvas, E., Nicchi, A., Kubik, E. and Boccaletti, S. 2016. Combining complex networks and data mining: Why and how. Physics Reports. 635, (May 2016), 1--44.Google ScholarGoogle Scholar

Index Terms

  1. Visualising maritime vessel open data for better situational awareness in ice conditions

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Other conferences
        Mindtrek '18: Proceedings of the 22nd International Academic Mindtrek Conference
        October 2018
        282 pages
        ISBN:9781450365895
        DOI:10.1145/3275116

        Copyright © 2018 Owner/Author

        This work is licensed under a Creative Commons Attribution International 4.0 License.

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 10 October 2018

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article
        • Research
        • Refereed limited

        Acceptance Rates

        Mindtrek '18 Paper Acceptance Rate34of68submissions,50%Overall Acceptance Rate110of207submissions,53%

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader