1. How to Lie with Visualization Zoran Constantinescu

2. references Al Globus et. al.; 14 Ways to Say Nothing with Scientific Visualization – IEEE Computer, vol. 27, 1994 Nahum Gershon; Presenting Visual Information Responsibly – ACM Computer Graphics, vol. 33, 1999 Nahum Gershon; How to Lie and Confuse with Visualization (VisLies) – special sessions at “Siggraph” and “Visualization” conferences

3. problem definition 14 ways to say nothing with SciViz examples conclusions outline

4. problem “Seeing is believing.” in synthetic imagery this is not always true (anybody can vis. anything in any shape/form) sources of imperfection imperfect presentation

5. can prevent getting the information or reduce the rate of absorption and understanding or the user get perceive it wrongly data can be too complicated to comprehend visualization can misrepresent the information

6. 14 ways to say nothing with SciViz it can be used to produce beautiful pictures usually we fail to appreciate the artistic qualities of these images scientists will use it to understand the data techniques to confound (confuse) such activities

7. 1. never include a color legend many visualization techniques involve assigning colors to scalar values spoils the beauty of an image the viewer may be diverted into contemplation of the reality

8. example 3D global view of Mars

9. example 3D global view of Mars elevation

10. example MRI scan of human head

11. 2. avoid annotation used for pointing out features of interest used in combination with explanatory text promotes clarity of understanding undermines the sense of awe and confusion the best scientific visualization engenders

12. H 2 O molecule example

13. H 2 O molecule electron density

14. 3. never mention error visualization techniques might introduce error scientists might not be properly impressed if mentioning error characteristics never imply by word or deed that the technique introduces any error “if the picture looks good, it must be correct”

15. 4. when in doubt, smooth smooth surfaces look much better than numerous ugly facets can also obscure errors and … … allow users to publish their results earlier always strive for the smoothest possible surface choose lighting normals to hide sharp edges

16. example

17. 5. avoid providing performance data it is completely irrelevant the time it took to calculate the picture e.g.. hours for ray-casting a isosurface, or seconds using marching cubes even if it takes longer, it is much smoother

18. 6. use stop-frame animation each frame of a scientific video usually takes from seconds to hours to produce  generate video frames one at a time then play back at high frame rates can dramatically improve perceived s/w perf.

19. example each frame about 36 sec

20. 7. never learn anything about data debugging is more difficult if worried about producing correct results complex accurate interpolation techniques ad-hoc techniques produce prettier pictures programming bugs can produce stunning images

21. 8. never compare results … … with other visualization techniques may detect bugs to be fixed other techniques may produce prettier pictures

22. example1 view dependent rendering of terrain data set

23. example2 3D global view of Mars flat map view of Mars

24. 9. avoid visualization systems provide mechanisms to add new modules users may violate rule 8 (never compare) usually “not invented here” (so we don’t use them :)

25. 10. never cite references for data don’t cite reference describing the data used someone may read the paper … and discover the visualization bears no relationship to the original experiment will divert attention from the picture’s appeal

26. 11. claim generality … … but show results from a single data set difficult to write vis. algorithms to work properly on a variety of data much effort can be saved, if … … run on one data, then make the image look different, as if from other datasets and use rule 10 (never cite refs)

27. 12. use viewing angle to hide imperfections many vis. algorithms produce 3D objects containing unpleasant imperfections avoid viewing angles exposing them if not, try another data set or …

28. 13. use specularity or shadows specular reflection=reflection from a smooth surface (mirror) maintaining the incident wave use shadows or brilliant highlights to hide the unpleasant 3D imperfections

29. 14. this is easily extended to 3D 3D algorithms much more difficult than 2D the effort of generalizing a 2D alg. can detract from producing pretty pictures simply claim that “the algorithm is easily extended to three ore more dimensions”

30. conclusions details some techniques to divert attention away from data and towards beauty, (audiences love color graphics and animations) and to avoid tedious debugging of software “to lie or not to lie”? variations in the method  influence the user’s perception and interpretation of data

31. - end - http://www.idi.ntnu.no/~zoran