how to lie with visualization zoran constantinescu

How to Liewith Visualization

Zoran Constantinescu

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

• problem definition

• 14 ways to say nothing with SciViz

• examples

• conclusions

outline

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

imperfect presentation

• 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

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

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

example

3D global view of Mars

example

3D global view of Mars

elevation

exampleMRI scan of human head

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

H2O molecule

example

exampleH2O molecule

electron density

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”

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

example

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

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.

example

each frame about 36 sec

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

8. never compare results …

• … with other visualization techniques

• may detect bugs to be fixed

• other techniques may produce prettier pictures

example1

view dependent renderingof terrain data set

example2

3D global view of Mars

flat map view of Mars

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 :)

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

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)

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 …

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

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”

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

- end -

http://www.idi.ntnu.no/~zoran