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Van Goethem, A.; Staals, F.; Loffler, M.; Dykes, J.; Speckmann, B. (2017)
Languages: English
Types: Article
Subjects: QA75
Time series (such as stock prices) and ensembles (such as model runs for weather forecasts) are two important types of one-dimensional time-varying data. Such data is readily available in large quantities but visual analysis of the raw data quickly becomes infeasible, even for moderately sized data sets. Trend detection is an effective way to simplify time-varying data and to summarize salient information for visual display and interactive analysis. We propose a geometric model for trend-detection in one-dimensional time-varying data, inspired by topological grouping structures for moving objects in two- or higher-dimensional space. Our model gives provable guarantees on the trends detected and uses three natural parameters: granularity, support-size, and duration. These parameters can be changed on-demand. Our system also supports a variety of selection brushes and a time-sweep to facilitate refined searches and interactive visualization of (sub-)trends. We explore different visual styles and interactions through which trends, their persistence, and evolution can be explored.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] W. Aigner, S. Miksch, H. Schumann, and C. Tominski. Visualization of Time-Oriented Data. Springer Publishing Company, Incorporated, 1st edition, 2011.
    • [2] R. Allendes Osorio and K. Brodlie. Contouring with uncertainty. Proceedings of Theory and Practice of Computer Graphics, pages 59-66, 2008.
    • [3] G.-P. Bonneau, H.-C. Hege, C. Johnson, M. Oliveira, K. Potter, P. Rheingans, and T. Schultz. Scientific Visualization: Uncertainty, Multifield, Biomedical, and Scalable Visualization, chapter Overview and Stateof-the-Art of Uncertainty Visualization, pages 3-27. Springer London, 2014.
    • [4] R. Brown. Animated visual vibrations as an uncertainty visualisation technique. In Proceedings of the 2nd International Conference on Computer Graphics and Interactive Techniques in Australasia and South East Asia (GRAPHITE), pages 84-89, 2004.
    • [5] K. Buchin, M. Buchin, M. van Kreveld, B. Speckmann, and F. Staals. Trajectory Grouping Structure. Journal of Computational Geometry, 6(1):75-98, 2015.
    • [6] L. Byron and M. Wattenberg. Stacked Graphs-Geometry & Aesthetics. IEEE Transactions on Visualization and Computer Graphics, 14(6):1245-1252, 2008.
    • [7] T. Cormen, C. Stein, R. Rivest, and C. Leiserson. Introduction to Algorithms. McGraw-Hill Higher Education, 2nd edition, 2001.
    • [8] L. Delle Monache, F. Eckel, D. Rife, B. Nagarajan, and K. Searight. Probabilistic Weather Prediction with an Analog Ensemble. Monthly Weather Review, 141(10):3498-3516, 2013.
    • [9] I. Demir, C. Dick, and R. Westermann. Multi-Charts for Comparative 3D Ensemble Visualization. IEEE Transactions on Visualization and Computer Graphics, 20(12):2694-2703, 2014.
    • [10] J. Dykes and C. Brunsdon. Geographically Weighted Visualization: Interactive Graphics for Scale-Varying Exploratory Analysis. IEEE Transactions on Visualization and Computer Graphics, 13(6):1161-1168, 2007.
    • [11] F. Ferstl, K. Burger, and R. Westermann. Streamline Variability Plots for Characterizing the Uncertainty in Vector Field Ensembles. IEEE Transactions on Visualization and Computer Graphics, 22(1):767-776, 2016.
    • [12] R. Gall, J. Franklin, F. Marks, E. N. Rappaport, and F. Toepfer. The Hurricane Forecast Improvement Project. Bulletin of the American Meteorological Society, 94(3):329-343, 2013.
    • [13] Gapminder (Data provider: World Bank). http://www. gapminder.org/data/, Mar 2016.
    • [14] M. Gleicher, D. Albers, R. Walker, I. Jusufi, C. Hansen, and J. Roberts. Visual comparison for information visualization. Information Visualization, 10(4):289-309, 2011.
    • [15] G. Grigoryan and P. Rheingans. Point-Based Probabilistic Surfaces to Show Surface Uncertainty. IEEE Transactions on Visualization and Computer Graphics, 10(5):564-573, 2004.
    • [16] T. Haiden, L. Magnusson, I. Tsonevsky, F. Wetterhall, L. Alfieri, F. Pappenberger, P. De Rosnay, J. Mun˜oz-Sabater, G. Balsamo, C. Albergel, et al. ECMWF forecast performance during the June 2013 flood in Central Europe. In ECMWF Technical Memorandum No 723. 2014.
    • [17] J. Hintze and R. Nelson. Violin Plots: a Box Plot-Density Trace Synergism. The American Statistician, 52(2):181-184, 1998.
    • [18] D. Holten. Hierarchical Edge Bundles: Visualization of Adjacency Relations in Hierarchical Data. IEEE Transactions on Visualization and Computer Graphics, 12(5):741-748, 2006.
    • [19] D. Holten and J. van Wijk. Force-Directed Edge Bundling for Graph Visualization. In Computer Graphics Forum, volume 28, pages 983-990, 2009.
    • [20] J. Kay, C. Deser, A. Phillips, A. Mai, C. Hannay, G. Strand, J. Arblaster, S. Bates, G. Danabasoglu, J. Edwards, M. Holland, P. Kushner, J.-F. Lamarque, D. Lawrence, K. Lindsay, A. Middleton, E. Munoz, R. Neale, K. Oleson, L. Polvani, and M. Vertenstein. The Community Earth System Model (CESM) large ensemble project: A community resource for studying climate change in the presence of internal climate variability. Bulletin of the American Meteorological Society, 96(8):1333-1349, 2015.
    • [21] Koninklijk Nederlands Meteorologisch Instituut (KNMI). http:// climexp.knmi.nl/start.cgi, Mar 2016.
    • [22] I. Kostitsyna, M. van Kreveld, M. Lo¨ffler, B. Speckmann, and F. Staals. Trajectory Grouping Structure under Geodesic Distance. In Proceedings of the 31st International Symposium on Computational Geometry, 2015.
    • [23] M. Lo¨ffler, F. Staals, and J. Urhausen. New Results on Trajectory Grouping under Geodesic Distance. In Abstracts of the 32nd European Workshop on Computational Geometry, 2016.
    • [24] M. Mirzargar, R. T. Whitaker, and R. M. Kirby. Curve Boxplot: Generalization of Boxplot for Ensembles of Curves. IEEE Transactions on Visualization and Computer Graphics, 20(12):2654-2663, 2014.
    • [25] National Oceanic and Atmospheric Administration (NOAA) / National Weather Service / Storm Prediction Center Plume Viewer. http:// www.spc.noaa.gov/exper/sref/srefplumes/, Mar 2016.
    • [26] H. Obermaier and K. Joy. Future Challenges for Ensemble Visualization. IEEE Computer Graphics and Applications, 34(3):8-11, 2014.
    • [27] C. Perin, F. Vernier, and J.-D. Fekete. Progressive Horizon Graphs: Improving Small Multiples Visualization of Time Series. In IEEE Conference on Information Visualization (INFOVIS), 2012.
    • [28] V. Peysakhovich, C. Hurter, and A. Telea. Attribute-Driven Edge Bundling for General Graphs with Applications in Trail Analysis. In IEEE Pacific Visualization Symposium (PacificVis), pages 39-46, 2015.
    • [29] K. Po¨thkow and H.-C. Hege. Positional Uncertainty of Isocontours: Condition Analysis and Probabilistic Measures. IEEE Transactions on Visualization and Computer Graphics, 17(10):1393-1406, 2011.
    • [30] K. Po¨thkow and H.-C. Hege. Nonparametric Models for Uncertainty Visualization. Computer Graphics Forum, 32(3pt2):131-140, 2013.
    • [31] K. Potter, J. Kniss, R. Riesenfeld, and C. Johnson. Visualizing Summary Statistics and Uncertainty. Computer Graphics Forum, 29(3):823-832, 2010.
    • [32] K. Potter, P. Rosen, and C. Johnson. From Quantification to Visualization: A Taxonomy of Uncertainty Visualization Approaches. In Uncertainty Quantification in Scientific Computing, pages 226-249. 2012.
    • [33] K. Potter, A. Wilson, P. Bremer, D. Williams, C. Doutriaux, V. Pascucci, and C. Johnson. Ensemble-Vis: A Framework for the Statistical Visualization of Ensemble Data. In IEEE International Conference on Data Mining Workshops (ICDMW), pages 233-240, 2009.
    • [34] H. Reijner. The Development of the Horizon Graph. In Proceedings of the Vis08 Workshop From Theory to Practice: Design, Vision and Visualization, 2008.
    • [35] M. Roberts, P. Vidale, M. Mizielinski, M.-E. Demory, R. Schiemann, J. Strachan, K. Hodges, R. Bell, and J. Camp. Tropical Cyclones in the UPSCALE Ensemble of High-Resolution Global Climate Models. Journal of Climate, 28(2):574-596, 2015.
    • [36] G. Robertson, R. Fernandez, D. Fisher, B. Lee, and J. Stasko. Effectiveness of Animation in Trend Visualization. IEEE Transactions on Visualization and Computer Graphics, 14(6):1325-1332, 2008.
    • [37] J. Sanyal, S. Zhang, J. Dyer, A. Mercer, P. Amburn, and R. Moorhead. Noodles: A Tool for Visualization of Numerical Weather Model Ensemble Uncertainty. IEEE Transactions on Visualization and Computer Graphics, 16(6):1421-1430, 2010.
    • [38] I. Tobin, R. Vautard, I. Balog, F.-M. Bre´on, S. Jerez, P. M. Ruti, F. Thais, M. Vrac, and P. Yiou. Assessing climate change impacts on European wind energy from ENSEMBLES high-resolution climate projections. Climatic Change, 128(1-2):99-112, 2015.
    • [39] A. van Goethem, M. van Kreveld, M. Lo¨ffler, B. Speckmann, and F. Staals. Grouping Time-varying Data for Interactive Exploration. In Proceedings of the 32th Annual Symposium on Computational Geometry. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik, 2016.
    • [40] M. van Kreveld, M. Lo¨ffler, F. Staals, and L. Wiratma. A Refined Definition for Groups of Moving Entities and its Computation. In Abstracts of the 32th European Workshop on Computational Geometry (EuroCG), 2016.
    • [41] B. Zehner, N. Watanabe, and O. Kolditz. Visualization of gridded scalar data with uncertainty in geosciences. Computers & Geosciences, 36(10):1268-1275, 2010.
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