Visual Analytics of Higher Order Information for Trajectory Datasets

Authors: Ye Wang, Ickjai Lee

Abstract:

Due to the widespread of mobile sensing, there is a strong need to handle trails of moving objects, and trajectories. This paper proposes three visual analytics approaches for higher order information of trajectory datasets based on the higher order Voronoi diagram data structure. Proposed approaches reveal geometrical, topological, and directional information. Experimental resultsdemonstrate the applicability and usefulness of proposed three approaches.

Keywords: Visual Analytics, Higher Order Information, Trajectory Datasets, Spatio-temporal data.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1090544

References:

[1] J. Gudmundsson, M. V. Kreveld, "Computing Longest Duration Flocks in Trajectory Data,” in GIS ’06 Proceedings of the 14th annual ACM international symposium on Advances in geographic information systems, 2006, pp. 35-42.
[2] I. Lee, and K. Lee, "A Generic Triangle-based Data Structure of the Complete Set of Higher Order Voronoi Diagrams for Emergency Management”, in Computers, Environments and Urban Systems, 2009, Vol. 33(2), pp: 90-99.
[3] E. Lindahl, B. Hess, D. V. D. Spoel, "GROMACS 3.0: a Package for Molecular Simulation and Trajectory Analysis,” in Springer-Verlag, August 2001, vol. 7, pp. 306-317.
[4] J. D. Palmer, "Using Line and Texture to Visualize Higher-Order Voronoi Diagram,” in Proceedings of the 3rd International Symposium on Voronoi Diagrams in Science and Engineering, IEEE Computer Society, 2006, pp: 166-172.
[5] A. Telea, and J. van Wijk, "Visualization of Generalized Voronoi Diagrams”, in Proceedings of the Joint Eurographics and IEEE TCVG Symposium on Visualization, Springer, 2001.
[6] Y. Wang, K. Lee, and I.Lee, "Representing Ordered Order-k Voronoi Diagrams”, In Proceedings of the 7th International Conference on Advanced Information Management and Service, IEEE Computer Society, 2012, pp: 71-75.
[7] P. Kalnis, N. Mamoulis, S. Bakiras, "On Discovering Moving Clusters in Spatio-temporal Data,” in Springer-Verlag Berlin Heidelberg 2005, 2005, pp. 364-381.
[8] I. Lee, T. B. Christopher, and K. Lee, "Raster Voronoi Tessellation and its Application to Emergency Modeling” in Geo-spatial Information Science, 14 (4), 2011, pp. 235-245.
[9] G. Andrienko, N. Andrienko, U. Demsar, D. Dransch, J. Dykes, S. I. Fabrikant, M. Jern, M. Kraak, H. Schumann, C. Tominski, "Space, time, and visual analytics”, in International Journal of Geographical Information Science, Vol. 24, No. 10, Oct 2010, Pages 1577-1600.
[10] R. Scheepens, N. Willems, H. V. D. Wetering, J. J. V. Wiji, "Interactive Visualization of Multivariate Trajectory Data with Density Maps,” in IEEE Pacific Visualisation Symposium 2011, 1-4 March, Hong Kong, China, pp. 147-154.
[11] T. Schreck, J. Bernard, T. Landesverger, J. Kohlhammer, "Visual Cluster Analysis of Trajectory Data with Interactive Kohonen Maps” in Information Visualization, 2009, vol. 8, pp. 14-29.
[12] http://www.srh.noaa.gov/oun/?n=events-20130520.