Contour based visualization of vessel movement predictions

References

• Agrawal, R., et al., 1995. Fast similarity search in the presence of noise, scaling, and translation in time-series databases. In: U. Dayal, P.M.D. Gray, and S. Nishio, eds. Proceedings of the 21th international conference on very large data bases, San Francisco, CA, USA. Burlington, MA: Morgan Kaufmann, 490–501.
   Google Scholar
• Andrienko, G., Andrienko, N., and Wrobel, S., 2007. Visual analytics tools for analysis of movement data. SIGKDD Exploration Newsletter, 9 (2), 38–46.
   Google Scholar
• Appel, A., Rohlf, F.J., and Stein, A.J., 1979. The haloed line effect for hidden line elimination. SIGGRAPH Computer Graphics, 13 (2), 151–157.
   Google Scholar
• Berndt, D. and Clifford, J., 1994. Using dynamic time warping to find patterns in time series. In: U.M. Fayyad and R. Uthurusamy, eds. Proceedings knowledge discovery and delivery workshop. Menlo Park, CA: AAAI Press, 359–370.
   Google Scholar
• Buchin, M., Dodge, S., and Speckmann, B., 2012. Context-aware similarity of trajectories. In: N. Xiao, et al., eds. Geographic information science., Vol. 7478 of lecture notes in computer science. Berlin: Springer, 43–56.
   Google Scholar
• Chazal, F., et al., 2011. Data-driven trajectory smoothing. In: I.F. Cruz, et al., eds. Proceedings of the 19th ACM SIGSPATIAL international conference on advances in geographic information systems, Chicago, IL. New York: ACM, 251–260.
   Google Scholar
• Demšar, U. and Virrantaus, K., 2010. Spacetime density of trajectories: exploring spatio-temporal patterns in movement data. International Journal of Geographical Information Science, 24 (10), 1527–1542.
   Web of Science ®Google Scholar
• de Vries, G., van Hage, W., and van Someren, M., 2010. Comparing vessel trajectories using geographical domain knowledge and alignments. In: W. Fan, et al., eds. IEEE international conference on data mining workshops, 13 December 2010. Washington, DC: IEEE Computer Society, 209–216.
   Google Scholar
• Dodge, S., Weibel, R., and Forootan, E., 2009. Revealing the physics of movement: comparing the similarity of movement characteristics of different types of moving objects. Computers, Environment and Urban Systems, 33 (6), 419–434.
   Web of Science ®Google Scholar
• Endsley, M.R. and Connors, E.S., 2008. Situation awareness: state of the art. In: IEEE power and energy society general meeting – conversion and delivery of electrical energy in the 21st century, 20–24 July 2008, Pittsburgh. Washington, DC: IEEE Computer Society, 1–4.
   Google Scholar
• Etienne, L., Devogele, T., and Bouju, A., 2010. Spatio-temporal trajectory analysis of mobile objects following the same itinerary. In: Proceedings of the international symposium on spatial data handling, 26–28 May 2010, Hong Kong. International Society for Photogrammetry and Remote Sensing (ISPRS), 6.
   Google Scholar
• Everts, M., et al., 2009. Depth-dependent halos: illustrative rendering of dense line data. Transactions on Visualization and Computer Graphics, 15, 1299–1306.
   PubMed Web of Science ®Google Scholar
• Hendriks, T. and van de Laar, P., 2013. METIS: dependable cooperative systems for public safety. Procedia Computer Science, 16, 542–551.
   Google Scholar
• Horne, J.S., et al., 2007. Analyzing animal movements using Brownian bridges. Ecology, 88 (9), 2354–2363.
   PubMed Web of Science ®Google Scholar
• ITU, 2010. Technical characteristics for an automatic identification system using time division multiple access in the VHF maritime mobile band. In: Recommendation ITU-R M.1371 –4. Geneva: International Telecommunication Union (ITU).
   Google Scholar
• Kosara, R., Miksch, S., and Hauser, H., 2001. Semantic depth of field. In: K. Andrews, S.F. Roth, and P.C. Wong, eds. Proceedings of IEEE symposium on information visualization. Washington, DC: IEEE Computer Society, 97–104.
   Google Scholar
• Kranstauber, B., et al., 2012. A dynamic Brownian bridge movement model to estimate utilization distributions for heterogeneous animal movement. Journal of Animal Ecology, 81 (4), 738–746.
   PubMed Web of Science ®Google Scholar
• Kuijpers, B., et al., 2010. Anchor uncertainty and space-time prisms on road networks. International Journal of Geographical Information Science, 24 (8), 1223–1248.
   Web of Science ®Google Scholar
• Kuijpers, B., Miller, H.J., and Othman, W., 2011. Kinetic space-time prisms. In: I.F. Cruz, et al., eds. Proceedings of the 19th ACM SIGSPATIAL international conference on advances in geographic information systems, 1–4 November 2011, Chicago, IL. New York: ACM, 162–170.
   Google Scholar
• Lavigne, V., Gouin, D., and Davenport, M., 2011. Visual analytics for maritime domain awareness. In: IEEE international conference on technologies for homeland security, 15–17 November 2011, Waltham, MA. Washington, DC: IEEE Computer Society, 49–54.
   Google Scholar
• Matthews, M.L., et al., 2009. Improving the maritime surface picture with a visualization aid to provide rapid situation awareness of information uncertainty. In: IEEE Toronto international conference on science and technology for humanity, 26–27 September 2009, Toronto. Washington, DC: IEEE Computer Society, 533–538.
   Google Scholar
• Riveiro, M. and Falkman, G., 2010. Evaluating the usability of visualizations of normal behavioral models for analytical reasoning. In: 7th international conference on computer graphics, imaging and visualization, 7–10 August 2010, Sydney. Washington, DC: IEEE Computer Society, 179–185.
   Google Scholar
• Riveiro, M., Falkman, G., and Ziemke, T., 2008. Improving maritime anomaly detection and situation awareness through interactive visualization. In: 11th international conference on information fusion, 30 June–3 July 2008, Cologne. Washington, DC: IEEE Computer Society.
   Google Scholar
• Santiago, C., et al., 2009. Overview and flight-by-flight analysis of trajectory prediction systems using a 2D Galaxy Visualization. In: IEEE/AIAA 28th digital avionics systems conference, 25–29 October 2009, Orlando, FL. Washington, DC: IEEE Computer Society, 3.B.3–1–3.B.3–8.
   Google Scholar
• Sanyal, J., et al., 2010. Noodles: a tool for visualization of numerical weather model ensemble uncertainty. IEEE Transactions on Visualization and Computer Graphics, 16, 1421–1430.
   PubMed Web of Science ®Google Scholar
• Scheepens, R., et al., 2011. Composite density maps for multivariate trajectories. IEEE Transactions on Visualization and Computer Graphics, 17, 2518–2527.
   PubMed Web of Science ®Google Scholar
• Scheepens, R., et al., 2012. Interactive density maps for moving objects. IEEE Computer Graphics and Applications, 32, 56–66.
   Google Scholar
• Van der Spek, S., Van Langelaar, C., and Christiaan Kickert, C., 2013. Evidence-based design: satellite positioning studies of city centre user groups. Urban Design and Planning, 166, 206–216.
   Google Scholar
• Vlachos, M., et al. 2003. Indexing multi-dimensional time-series with support for multiple distance measures. In: L. Getoor, et al., eds. Proceedings of the 9th ACM SIGKDD international conference on knowledge discovery and data mining, Washington, DC. New York: ACM, 216–225.
   Google Scholar
• Wall, A.D., Bole, A.G., and Dineley, W.O., 2005. Radar and ARPA manual: radar and target tracking for professional mariners, yachtsmen and users of marine radar. 2nd ed. Oxford: Elsevier Butterworth-Heinemann.
   Google Scholar
• Ware, C., 2001. Designing with a 2 1/2d attitude. Information Design Journal, 10 (3), 255–262.
   Google Scholar
• Willems, N., et al., 2010. An integrated approach for visual analysis of a multisource moving objects knowledge base. International Journal of Geographical Information Science, 24 (10), 1543–1558.
   Web of Science ®Google Scholar
• Willems, N., van de Wetering, H., and van Wijk, J.J., 2009. Visualization of vessel movements. Computer Graphics Forum, 28 (3), 959–966.
   Web of Science ®Google Scholar