The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.06 - November/December (2010 vol.16)
pp: 963-972
Rita Borgo , Swansea University
Karl Proctor , Swansea University
Min Chen , Swansea University
Heike Jänicke , Heidelberg University
Tavi Murray , Swansea University
Ian Thornton , Swansea University
ABSTRACT
Pixel-based visualization is a popular method of conveying large amounts of numerical data graphically. Application scenarios include business and finance, bioinformatics and remote sensing. In this work, we examined how the usability of such visual representations varied across different tasks and block resolutions. The main stimuli consisted of temporal pixel-based visualization with a white-red color map, simulating monthly temperature variation over a six-year period. In the first study, we included 5 separate tasks to exert different perceptual loads. We found that performance varied considerably as a function of task, ranging from 75% correct in low-load tasks to below 40% in high-load tasks. There was a small but consistent effect of resolution, with the uniform patch improving performance by around 6% relative to higher block resolution. In the second user study, we focused on a high-load task for evaluating month-to-month changes across different regions of the temperature range. We tested both CIE L*u*v* and RGB color spaces. We found that the nature of the change-evaluation errors related directly to the distance between the compared regions in the mapped color space. We were able to reduce such errors by using multiple color bands for the same data range. In a final study, we examined more fully the influence of block resolution on performance, and found block resolution had a limited impact on the effectiveness of pixel-based visualization.
INDEX TERMS
Pixel-based visualization, evaluation, user study, visual search, change detection.
CITATION
Rita Borgo, Karl Proctor, Min Chen, Heike Jänicke, Tavi Murray, Ian Thornton, "Evaluating the impact of task demands and block resolution on the effectiveness of pixel-based visualization", IEEE Transactions on Visualization & Computer Graphics, vol.16, no. 6, pp. 963-972, November/December 2010, doi:10.1109/TVCG.2010.150
REFERENCES
[1] T. Bachmann, Identification of spatially quantized tachistoscopic images of faces: How many pixels does it take to carry identity? European Journal of Cognitive Psychology, 3: 85–103, 1991.
[2] L. D. Bergman, B. E. Rogowitz, and L. A. Treinish, A rule-based tool for assisting colormap selection. In VIS '95: Proceedings of the 6th conference on Visualization '95, page 118, Washington, DC, USA, 1995. IEEE Computer Society.
[3] I. Biederman, A. L. Glass, and E. W. J. Stacy, Searching for objects in real-world scenes. Journal of Experimental Psychology, 97: 22–27, 1973.
[4] S. Chiaretti, X. Li, R. Gentleman, A. Vitale, M. Vignetti, F. Mandelli, J. Ritz, and F. R., Gene expression profile of adult t-cell acute lymphocytic leukemia identifies distinct subsets of patients with different response to therapy and survival. Blood, (103): 2771–2778, 2004.
[5] M. Chun and Y. Jiang, Top-down attentional guidance based on implicit learning of visual covariation. Psychologicl Science, 10: 360–365, 1999.
[6] M. C. F. de Oliveira and H. Levkowitz, From visual data exploration to visual data mining: A survey. IEEE Transactions on Visualization and Computer Graphics, 9: 378–394, 2003.
[7] L. S. Exchange, 2010.
[8] D. Field, Relations between the statistics of natural images and the response profiles of cortical cells. Journal of the Optical Society of America, 4: 2379–2394, 1987.
[9] R. Gentleman and R. Ihaka, The r project for statistical computing, 2010.
[10] M. C. Hao, U. Dayal, D. A. Keim, and T. Schreck, Multi-resolution techniques for visual exploration of large time-series data. In EuroVis, pages 27–34, 2007.
[11] L. D. Harmon and B. Julesz, Masking in visual recognition: Effects of two-dimensional filtered noise. science 180: 1194, 1973.
[12] M. Harrower and C. A. Brewer, Colorbrewer.org: An online tool for selecting colour schemes for maps. Cartographic Journal, 40 (1): 27–37, Jun 2003.
[13] D. e. a. Hoiem, Putting objects in perspective. Proc. IEEE Comp. Vis. Pattern Recog., 2: 2137–2144, 2006.
[14] D. H. Jeong, A. Darvish, K. Najarian, J. Yang, and W. Ribarsky, Interactive visual analysis of time-series microarray data. Vis. Comput., 24 (12): 1053–1066, 2008.
[15] D. A. Keim, Designing pixel-oriented visualization techniques: Theory and applications. IEEE Transactions on Visualization and Computer Graphics, 6 (1): 59–78, 2000.
[16] D. A. Keim and H.-P. Kriegel, Visualization techniques for mining large databases: A comparison. IEEE Trans. on Knowl. and Data Eng., 8 (6): 923–938, 1996.
[17] D. A. Keim, C. Panse, M. Sips, and S. C. North, Pixelmaps: A new visual data mining approach for analyzing large spatial data sets. Data Mining, IEEE International Conference on, 0: 565, 2003.
[18] D. A. Keim, C. Panse, M. Sips, and S. C. North, Pixel based visual mining of geo-spatial data. Computers and Graphics, 28 (3): 327–344, September 2004.
[19] T. Lammarsch, W. Aigner, A. Bertone, J. Gartner, E. Mayr, S. Miksch, and M. Smuc, Hierarchical temporal patterns and interactive aggregated views for pixel-based visualizations. In IV '09: Proceedings of the 2009 13th International Conference Information Visualisation, pages 44–50. IEEE Computer Society, 2009.
[20] B. Lindbloom, Cie standard color equations, 2010.
[21] C. G. Mueller, Frequency of seeing functions for intensity discrimination at various levels of adapting intensity. Journal of General Physiology, 34: 463–474, 1951.
[22] U.D. of Energy (DOE), 2010.
[23] A. Oliva and A. Torralba, The role of context in object recognition. Trends in Cognitive Sciences, 11: 520–527, 2007.
[24] F. Paas, A. Renkl, and J. Sweller, Cognitive load theory: Instructional implications of the interaction between information structures and cognitive architecture. Instructional Science, 32: 1–8, 2004.
[25] R. A. Rensink, Visual search for change: A probe into the nature of attentional processing. Visual Cognition, 7: 345–376, 2000.
[26] R. A. Rensink, Change detection. Annual Review of Psychology, 53: 245–277, 2002.
[27] J. Schneidewind, M. Sips, and D. A. Keim, An automated approach for the optimization of pixel-based visualizations. Information Visualization, 6 (1): 75–88, 2007.
[28] M. H. Shimabukuro, E. F. Flores, M. C. F. de Oliveira, and H. Levkowitz, Coordinated views to assist exploration of spatio-temporal data: A case study. In CMV '04: Proceedings of the Second International Conference on Coordinated & Multiple Views in Exploratory Visualization, pages 107–117. IEEE Computer Society, 2004.
[29] P. T. Sowden and P. Schyns, Channel surfing in the visual brain. Trends in Cognitive Sciences, 10 (12): 538–545, 2006.
[30] S. S. Stevens, Psychophysics of sensory function. Sensory Communication, 1961.
[31] A. Torralba, How many pixels make an image? Proceedings of the IEEE, 94: :1948–1962, 2006.
[32] A. Torralba and A. Oliva, Statistics of natural images categories. Network Comput. Neural Syst., 14: :391–412, 2003.
[33] A. Torralba and P. Sinha, Detecting faces in impoverished images. Technical report, In AI Memo 2001–028, CBCL Memo 208, 2001.
[34] C. Ware, Color sequences for univariate maps: Theory, experiments and principles. IEEE Computer Graphics and Applications, 8: 41–49, 1988.
[35] H. R. Wilson, D. K. Mcfarlane, and G. C. Phillips, Spatial-frequency tuning of orientation selective units estimated by oblique masking. Vision Research, 23 (9): 873–882, 1983.
[36] J. Wolfe and T. Horowitz, What attributes guide the deployment of visual attention and how do they do it? Nature Reviews Neuroscience, 5: 1–7, 2004.
[37] B. Yost, Y. Haciahmetoglu, and C. North, Beyond visual acuity: the perceptual scalability of information visualizations for large displays. In CHI '07: Proceedings of the SIGCHI conference on Human factors in computing systems, pages 101–110, New York, NY, USA, 2007. ACM.
[38] H. Ziegler, T. Nietzschmann, and D. A. Keim, Visual analytics on the financial market: Pixel-based analysis and comparison of long-term investments. Information Visualisation, International Conference on, 0: 287–295, 2008.
23 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool