1. Graphics and Graphic Information Processing J. Bertin Hilary Browne Jeff Carver CMSC 838 September 9, 1999

2. Our favorite sentence “a problem with n characteristics is not the sum of n problems with two characteristics”

3. Problem Collection of objects that are described by n characteristics Need a way to visually represent that information n = 1, 2, 3 are special cases (easy) n > 3 - “impassable barrier”

4. Data Tables Raw data is transformed into data tables x-axis - objects –ex. movies y-axis - characteristics –ex. rating, length Special case - networks

5. Object Types (x-axis) Reorderable –ex. individuals Ordered –ex. months Topographical –ex. cities

6. Characteristic Types (y-axis) Nominal –ex. Movie titles Ordinal –ex. Movie ratings Quantitative –ex. Movie length

7. Data table --> Graphic Constructs Graph choice depends on object type –diagram –map –network Use “Synoptic” to choose

8. Synoptic

9. Easy case: n < 3 Reorderable objects –Reorderable matrix (bar graph) Ordered objects –image-file –array of curves Topographical objects –map

10. Scatter Plots Applicable to ordered and reorderable objects Useful only when n < 3

11. Hard case: n >3 Useful graphic constructs –Reorderable matrix : permutable in x and y –Image-file : permutable in y –Array of curves : permutable in y Less useful graphic constructs –Collection of scatter plots –Collection of maps

12. Synoptic

13. Special Case: Networks Reordered, Ordered, and Topographical representations Can be converted to data table

14. Using the Synoptic Reordering can lead to discovery of patterns Deviating from suggested construction leads to loss of information and requires justification Choosing between a map and a diagram Size limitations

15. Critique Strengths –Simple, Usable Taxonomy for static graphics –Synoptic diagram Weaknesses –Excerpt from book Lack of examples Terminology –Outdated for visualization

16. Contributions Classification scheme for 2D graphical presentation –Used in other applications e.g. Mackinlay Viewing more than 3 characteristics is hard Using the wrong tool can lead to information loss

17. Related Paper: DeFanti - 1987 NSF report Scientific visualization –“Interactive representations of scientific data” Established the term “visualization” in computing Recommended funding the development and use of new tools Automation and extension of Bertin work Attempt to overcome “impassable barrier”

18. Where has it gone? Automated Extended to 3D Incorporated into Dynamic Visualization tools –ex. Film Finder, Spotfire