1. How (not) to lie with visualization cs5984: Information Visualization Chris North

2. Final Quiz 7 information types: 1d 2d 3d Multi-d Trees Graphs Doc collections

3. Where are we? Information Types: Multi-D 1D 2D Hierarchies/Trees Networks/Graphs Document collections 3D Topics: Design Principles Empirical Evaluation Java Development Visual Overviews Multiple Views Peripheral Views Workspaces Debates Vis. Lies

4. How (not) to lie with visualization Show and tell “USA Today” graphs…

5. Stock Market Crash?! 199519961997199819992000 $9000 8875 8750 8625 8500 Market

6. Show entire scale $10,000 7500 5000 2500 0 Market 199519961997199819992000

7. Show in context 195019601970198019902000 $10,000 7500 5000 2500 0 Market

8. Another example

9. Percentages: 0% – 100% Employment rate = 100 – unemployment rate

10. Tufte’s Rule Visual attribute value should be directly proportional to data attribute value Lie factor = (visual effect) / (data effect) truth = 1.0

11. Company financial status

12. The hidden 0-points Lie factor = ?

13. Changing Scale 13 0.5?

14. Changing Scale

15. …with linear time scale

16. Down = Bad ?

17. Make it explicit  Better Other examples: user performance, questionnaire results

18. Logarithmic data  log scale

19. Size Coding

20. Size Coding: width or area? =?

21. Size Coding

22. Width or Area Width = value Height = value Area = value 2 or Area = value width*height = value width = height = value 0.5 Problem: Using 2 dimensions to represent 1 dimension.

23. Volume coding? Height? Diameter? Surface area? Volume? 73 – 79 data difference = 5.5x 73 – 79 volume difference = 270x

24. Problem with area encoding 12345671234567 Width Area Volume

25. Width & height encoding 12345671234567 Width Width & Height

26. Solution: just use width (or height)

27. A Propaganda Classic

28. Hmmm… Low rank = good! Different time scales Not really tuition Artistic mood

29. How not to lie Show entire scale Show data in context Consistent, linear scale Log scale for log data Up vs. down: indicate direction of improvement Avoid size coding Use width OR height Don’t use both for same data attribute Avoid area coding

30. Visualization = Communication Communication is person dependent People have a lot of “baggage”

31. Expectations Paris in the the spring Life is a a highway Now is the the time

32. Re-training Red spades, black hearts Poor user performance even after being told

33. Orientation Who are they?

34. Orientation

35. Homework 3 Results Hinite bites. Too much hypertexty stuff Not enough zooming, infovisy stuff Keep trying to break out of the box!

36. Projects Visualization in the periphery - evaluation »David, Christa, Ali, Jon Visualizing Multi-D functions »Reenal, Priya, Mrinmayee Visualization of data structures – evaluation »Kunal, Vikrant, Anuj Snap-together visualization »Rohit, Varun, Jeevak, Atul Visual Break-down analysis with Financial data »Ganesh, Anusha, Muthukumar, Sandeep Human-eye view »Alex, Qiang, Ming, Vishal, Ahmed Bioinformatics »Quoc, Mudita, Dhananjay Online chat/video-conference visualization (virtual school) »Mahesh, Ben, Samal, Kuljeet, Harsha, Parool Digital libraries »Jun, Supriya, Abhishek, Anil Maps and in-vehicle interfaces »Ying, Xueqi, Zhiping, Rui Dec 4 Dec 6 Dec 11

37. Your Last InfoVis Assignment! Dec 18: Project Final Paper due Dec 7: ACM CHI short papers due Other destinations: March?: IEEE InfoVis papers due …