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Issue No.03 - May/June (2005 vol.25)
pp: 69-79
Alison L. Love , University of California, Santa Cruz
Alex Pang , University of California, Santa Cruz
David L. Kao , NASA Ames Research Center
Ensemble forecasts, outcomes from conditional simulations, or repeated measurements in an experiment all produce multiple instances of the same physical field. This article refers to this as a multivalue data type. Specifically, a multivalue data point contains multiple values for a single variable. If there is a single multivalue data point, then visualizing it can be carried out using a simple graph either showing the different values, or the frequency of the different values. However, if the multivalue data exist over a spatial domain, then the existing suite of visualization techniques has limited power in visualizing them. This article introduces the multivalue data type, and suggests three ways of visualizing spatial multivalue data sets.
uncertainty, distributions, realizations, ensemble forecast, parametric statistics, shape descriptors
Alison L. Love, Alex Pang, David L. Kao, "Visualizing Spatial Multivalue Data", IEEE Computer Graphics and Applications, vol.25, no. 3, pp. 69-79, May/June 2005, doi:10.1109/MCG.2005.71
1. M.K. Beard, B.P. Buttenfield, and S.B. Clapham, NCGIA Research Initiative 7: Visualization of Spatial Data Quality, tech. paper 91-26, Nat'l Center for Geographic Information and Analysis, Oct. 1991, pp. 59; 9191-26.pdf.
2. A. Pang, C.M. Wittenbrink, and S.K. Lodha, "Approaches to Uncertainty Visualization," The Visual Computer, vol. 13, no. 8, 1997, pp. 370-390; .
3. C.R. Ehlschlaeger, A.M. Shortridge, and M.F. Goodchild, "Visualizing Spatial Data Uncertainty Using Animation," Computers & Geosciences, vol. 23, no. 4, 1997, pp. 387-395.
4. R.M. Srivastava, The Visualization of Spatial Uncertainty, Stochastic Modeling and Geostatistics: Principles, Methods, and Case Studies, J.M Yarus and R.L. Chambers, eds., American Assoc. of Petroleum Geologists, 1994.
5. G.J. Nowacki and M.G. Kramer, "The Effects of Wind Disturbance on Temperate Rain Forest Structure and Dynamics of Southeast Alaska," General Technical Report PNW-GTR-421, US Dept. of Agriculture, Forest Service, Pacific Northwest Research Station, April 1998.
6. J.L. Dungan, "Conditional Simulation: An Alternative to Estimation for Achieving Mapping Objectives," Spatial Statistics for Remote Sensing, Kluwer, 1999, pp. 135-52.
7. P.F.J. Lermusiaux, "Data Assimilation via Error Subspace Statistical Estimation Part II: Middle Atlantic Bight Shelfbreak Front Simulations and ESSE Validation," Monthly Weather Review, vol. 127, no. 7, 1999, pp. 1408-1432.
8. D. Kao, J. Dungan, and A. Pang, "Visualizing 2D Probability Distributions from EOS Satellite Image-Derived Data Sets: A Case Study," Proc. Visualization 2001, IEEE CS Press, 2001, pp. 457-460.
9. D. Kao et al., "Visualizing Spatially Varying Distribution Data," Proc. 6th Int'l Conf. Information Visualization, IEEE CS Press, 2002, pp. 219-225.
10. W.T. Eadie, Statistical Methods in Experimental Physics, North-Holland Pub. Co., 1971.
11. V.A. Gerasimov, B.S. Dobronets, and M. Yu. Shustrov, "Numerical Operations of Histogram Arithmetic and their Applications," Automation and Remote Control, vol. 52, no. 2, 1991, pp. 208-212.
12. A. Gupta and S. Santini, "Toward Feature Algebras in Visual Databases: The Case for a Histogram Algebra," Advances in Visual Information Management: Visual Database Systems, Kluwer, 2000, pp. 177-196.
13. D.L. Darmofal and R. Haimes, "An Analysis of 3D Particle Path Integration Algorithms," J. Computational Physics, vol. 123, no. 1, 1996, pp. 182-195.
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