The Sampling Lens:
making sense of saturated visualisation

Geoff Ellis
Lancaster University, UK.
< Geoff on the web >

  

Enrico Bertini
Univ. Roma 1, "La Sapienza", Italy
< Enrico on the web >

  

Alan Dix
Lancaster University, UK
< Alan on the web >

Interactive Poster at CHI'2005, 2-7 April 2005, Portland, USA.


Abstract

Information visualisation systems frequently have to deal with large amounts of data, which often leads to saturated areas in the display with considerable overplotting. This paper introduces the Sampling Lens, a novel tool that utilises random sampling to reduce the clutter within a moveable region, thus allowing the user to uncover any potentially interesting patterns and trends in the data. We demonstrate the versatility of the tool by adding sampling lenses to scatter and parallel co-ordinate visualisations. We also consider some implementation issues and present initial user evaluation results.

Keywords: sampling, random sampling, lens, clutter, density reduction, overplotting, information visualisation

Full reference:
G. Ellis, E. Bertini and A. Dix (2005). The Sampling Lens: making sense of saturated visualisation Proceedings of CHI'2005 , ACM Press. pp. 1351-1354.
http://www.hcibook.com/alan/papers/
chi2005-samplinglens/
more:
Download poster (PDF, 15Mb)
see related work on visualisation at: http://www.hcibook.com/alan/topics/vis/


Poster: download in PDF (15Mb)


References

  1. Artero, A O, Ferreira de Oliveira M C, and Levkowitz H. Uncovering Clusters in Crowded Parallel Coordinates Visualizations. Proc. Symposium on Information Visualization 2004, 131- 136.
  2. Bertini, E and Santucci, G. By chance is not enough: preserving relative density through non uniform sampling. Proc.IVf04, IEEE, 622-629
  3. Bier, E., A., Stone, M., C., Pier, K., Buxton, W., De Rose, T., D. Toolglass and magic lenses: the seethrough interface. Proc. Computer graphics and interactive techniques 1993, 73 - 80
  4. Dix, A and Ellis, G P. By chance: enhancing interaction with large data sets through statistical sampling. Proc. AVI'02, ACM Press, 167-176
  5. Ellis, G P and Dix, A. Density control through random sampling : an architectural perspective. Proc IV'02, IEEE, 82-90
  6. Fekete, J and Plaisant, C. Interactive Information Visualization of a Million Items. Proc. InfoVis'02, IEEE, 117
  7. Keim, D A., Hao, M C., Dayal, U., Hsu, M.: Pixel Bar Charts: A Visualization Technique for Very Large Multi-Attribute Data Sets. Information Visualization Journal, Palgrave, Vol. 1, No. 2, 2002
  8. Keim, D A., Panse, C., Schneidewind, J., Sips, M. Geo-Spatial Data Viewer: From Familiar Land-covering to Arbitrary Distorted Geo-Spatial Quadtree Maps, WSCG 2004
  9. Trutschl, M., Grinstein, G., Cvek, U. Intelligently Resolving Point Occlusion. Proc. Symposium on Information Visualization 2003, 131- 136
  10. Waldeck, C. and Balfanz, D. Mobile Liquid 2D Scatter Space. Proc. IV'04, IEEE, 494-498
  11. Wilkinson, L., Rubin, M., Rope, D. and Norton A. nViZn: An Algebra-Based Visualization System. International Symposium on Smart Graphics 2001
  12. Woodruff, A., Landay, J., Stonebraker, M. Constant density visualizations of non-uniform distributions of data. Proc. UIST 98. ACM Press, 19-2


Figure 1. Parcel data scatter plot example


Figure 2. Revealing hidden pattern



Figure 3. Parallel coordinate example


Figure 4. Implementation architectures for sampling lens


http://www.hcibook.com/alan/papers/chi2005-samplinglens/

Alan Dix 8/4/2005