The plot, the clutter, the sampling and its lens:
occlusion measures for automatic clutter reduction.

Geoff Ellis
Lancaster University, UK.
http://www.comp.lancs.ac.uk/computing/users/ellisg2/

  

Alan Dix
Lancaster University, UK
http://www.hcibook.com/alan/

Paper at AVI'2006, 24-26 May 2006, Venezia, ITALY.

Abstract

This paper discusses different occlusion metrics that can be used to drive automatic sampling and several ways of calculating them. Previous work has demonstrated that random sampling can aid the visualisation of large data sets, and that a sampling lens can be used to enable focus+context viewing of a region at appropriate sampling rates. Autosampling (automatically choosing the sampling rate) was proposed to make the sampling lens easier to use. This requires rapid and continuous recalculation of the sampling rate. We find that the crudest and fastest occlusion estimate is nearly as good as the more expensive etruef complete measurement.

keywords: Sampling, random sampling, lens, clutter, occlusion, density reduction, overplotting, information visualisation
Full reference:
G. Ellis and A. Dix(2006). The plot, the clutter, the sampling and its lens: occlusion measures for automatic clutter reduction.
Proceedings of AVI2006. pp. 266-269
http://www.hcibook.com/alan/papers/
avi2006-lens/
more:
related work on visualisation at: http://www.hcibook.com/alan/topics/vis/
related papers
G. Ellis, E. Bertini and A. Dix (2005).
The Sampling Lens: making sense of saturated visualisation Proceedings of CHI'2005, Vol. 2, ACM Press. pp. 1351-1354 .
abstract and links
A. Dix and G. Ellis (2002).
By chance - enhancing interaction with large data sets through statistical sampling. Proceedings of Advanced Visual Interfaces - AVI2002, Trento, Italy, ACM Press. pp.167-176.
abstract, contents and references

References

  1. Bertini, E. and Santucci, G.  Improving 2D scatterplots effectiveness through sampling, displacement and user perception. Proceedings of Information Visualisation 2005, London, July 2005, IEEE

  2. Bier, E A., Stone, M C., Pier, K., Buxton, W., De Rose, T D.  Toolglass and magic lenses: the see-through interface. Proceedings of Computer Graphics and Interactive Techniques, 1993, 73-80

  3. Brath, R.  Concept Demonstration: Metrics for Effective Information Visualization. Symposium on Information Visualization, Phoenix, AZ, Oct 1997, IEEE, 108-111

  4. Dix, A. and Ellis, G.P.  by chance: enhancing interaction with large data sets through statistical sampling. Proceedings of the International Working Conference on Advanced Visual Interfaces, L'Aquila, Italy, May 2002, ACM Press, 167-176

  5. Ellis, G.P., Bertini, E. and  Dix, A.  The Sampling Lens:Making Sense of Saturated Visualisations . CHI '05 Extended Abstracts on Human Factors in Computing Systems, Portland, USA, 2005, ACM Press, 1351-1354

  6. Frank, A.U. and Timpf, S. Multiple Representations for Cartographic Objects in a Multi-scale Tree | -n Intelligent Graphical Zoom. Computers and Graphics, 18(6), 1994, 823-829

  7. Rosenholtz, R., Yuanzhen Li, Jonathan Mansfield, Zhenlan Jin.  Feature Congestion: A Measure of Display Clutter. Proceedings of the SIGCHI conference on Human factors in computing systems, Apr 2005, ACM Press, 761-770

  8. Tufte, E.R. The Visual Display of Quantitative Information. Graphics Press, Cheshire, CT, 1983

 


Figure 2.a. the lens at work

 

http://www.hcibook.com/alan/papers/avi2006-lens/

Alan Dix 13/5/2006