Abstract
One challenge associated with the visualization of time-dep- endent data is to develop graphical representations that are effective for exploring multiple time-varying quantities. Many existing solutions are limited either because they are primarily applicable for visualizing non-negative values or because they sacrifice the display of overall trends in favor of value-based comparisons. We present a two-dimensional representation we call Data Vases that yields a compact pictorial display of a large number of numeric values varying over time. Our method is based on an intuitive and flexible but less widely-used display technique called a “kite diagram.” We show how our interactive two-dimensional method, while not limited to time-dependent problems, effectively uses shape and color for investigating temporal data. In addition, we extended our method to three dimensions for visualizing time-dependent data on cartographic maps.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Müller, W., Schumann, H.: Visualization methods for time-dependent data - an overview. In: Chick, S., Sanchez, P., Ferrin, D., Morrice, D. (eds.) Proc. of Winter Simulation 2003 (2003)
Aigner, W., Bertone, A., Miksch, S., Tominski, C., Schumann, H.: Towards a conceptual framework for visual analytics of time and time-oriented data. In: WSC 2007: Proceedings of the 39th conference on Winter simulation, Piscataway, NJ, USA, pp. 721–729. IEEE Press, Los Alamitos (2007)
Roddick, J.F., Spiliopoulou, M.: A bibliography of temporal, spatial and spatio-temporal data mining research. SIGKDD Explor. Newsl. 1, 34–38 (1999)
Aigner, W., Miksch, S., Müller, W., Schumann, H., Tominski, C.: Visual methods for analyzing time-oriented data. IEEE TVCG 14, 47–60 (2008)
Hochheiser, H., Shneiderman, B.: Dynamic query tools for time series data sets: timebox widgets for interactive exploration. Info. Vis. 3, 1–18 (2004)
Berry, L., Munzner, T.: Binx: Dynamic exploration of time series datasets across aggregation levels. In: IEEE InfoVIS, Washington, DC, USA. IEEE Computer Society, Los Alamitos (2004)
Peng, R.: A method for visualizing multivariate time series data. Journal of Statistical Software, Code Snippets 25, 1–17 (2008)
Hao, M.C., Dayal, U., Keim, D.A., Schreck, T.: Multi-resolution techniques for visual exploration of large time-series data. In: EuroVis 2007, pp. 27–34 (2007)
Havre, S., Hetzler, B., Nowell, L.: Themeriver (tm). In search of trends, patterns, and relationships (1999)
Heer, J., Kong, N., Agrawala, M.: Sizing the horizon: The effects of chart size and layering on the graphical perception of time series visualizations. In: CHI 2009, Boston, MA, USA (2009)
Emery, D., Myers, K. (eds.): Sequence Stratigraphy. Blackwell Publishing, Malden (1996)
Sheppard, C.R.C.: Species and community changes along environmental and pollution gradients. Marine Pollution Bulletin 30, 504–514 (1995)
Kraak, M.: The space-time cube revisited from a geovisualization perspective. In: Proc. 21st Intl. Cartographic Conf., pp. 1988–1995 (2003)
Eccles, R., Kapler, T., Harper, R., Wright, W.: Stories in geotime. In: VAST 2007. Visual Analytics Science and Technology, pp. 19–26 (2007)
Tominski, C., Schulze-Wollgast, P., Schumann, H.: 3d information visualization for time dependent data on maps. In: IV 2005: Proceedings of the 9th Intl. Conf. on Info. Vis., Washington, DC, USA, pp. 175–181. IEEE Computer Society, Los Alamitos (2005)
Dwyer, T., Eades, P.: Visualising a fund manager flow graph with columns and worms. International Conference on Information Visualisation, 147 (2002)
Elmqvist, N., Tsigas, P.: A taxonomy of 3d occlusion management for visualization. IEEE Transactions on Visualization and Computer Graphics 14, 1095–1109 (2008)
Luo, Z.X.: Transformation and diversification in early mammal evolution. Nature 450, 1011–1019 (2007)
Tufte, E.R.: The visual display of quantitative information. Graphics Press, Cheshire (1986)
Ware, C.: Information Visualization: Perception for Design. Morgan Kaufmann Publishers Inc., San Francisco (2004)
Healey, C.G., Booth, K.S., Enns, J.T.: Visualizing real-time multivariate data using preattentive processing. ACM Trans. Model. Comput. Simul. 5, 190–221 (1995)
Tominski, C., Fuchs, G., Schumann, H.: Task-driven color coding. In: Intl. Conf. Info. Vis., Washington, DC, USA, pp. 373–380. IEEE Computer Society, Los Alamitos (2008)
Shneiderman, B.: Dynamic queries for visual information seeking. IEEE Software 11, 70–77 (1994)
Andrienko, N., Andrienko, G.: Exploratory Analysis of Spatial and Temporal Data: A Systematic Approach. Springer, Heidelberg (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Thakur, S., Rhyne, TM. (2009). Data Vases: 2D and 3D Plots for Visualizing Multiple Time Series. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2009. Lecture Notes in Computer Science, vol 5876. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10520-3_89
Download citation
DOI: https://doi.org/10.1007/978-3-642-10520-3_89
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10519-7
Online ISBN: 978-3-642-10520-3
eBook Packages: Computer ScienceComputer Science (R0)