1. Limn: Using Image and Video Technology for Visualizing a Million Cases of Multivariate data Di Cook, Les Miller, Manuel Suarez, Peter Sutherland, Jing Zhang

2. History John McDonald’s 3D rotation and linked brushing in early 90s

3. Why Scale Visual Methods? All three data sets have correlation approximately 0.7

4. Type of Data Real-valued multivariate, spatio-temporal context, class information

5. Goals Provide multiple scatterplot displays, either as several static plots, or a sequence of tour projections. Link brush between them in real-time. For 100Gb of multivariate data:

6. Real-time Graphics GGobi can handle 1 million points, but… There is a lot of overplotting even when using pixel sized glyph. Brush slow to color with continuous updating. Tour is too slow to tolerate: use scramble

7. Problems with Large Data Sets Data Reduction: MV binning produces visual artifacts, screen size induces a binning, though. Scaling of Methods: computation, storage, memory.

8. Screen Resolution Limited screen space induces binning Alpha-blending removes focus from low density pixels Using grey scale

9. Indexing Projections nxp data matrix Pixel resolution plot window

11. Limn Software Two Steps: 1. Create indexing on projected data, or an animation sequence and save as QuickTime movie. 2. View density plots or animation, and interact with it by brushing, and overlaying subsets of data. * Code is written in Java

12. Data Description Seasonal metrics data computed on AVHRR images across the USA in 1989: SoST, SoSN, EoST,EoSN, MaxT, MaxN, TotalNDVI. And land cover classes: agriculture, grassland, deciduous, evergreen, barren.

13. Demo Scatterplot matrix Tour movie

14. Currently we’re also exploring… Sampling distributions of the pixel population. Overlaying movies for working with larger data. Broader array of data parsers.

15. Contact Information Software is archived at: www.sourceforge.net Email: dicook@iastate.edudicook@iastate.edu