Representing Data using Static and Moving Patterns

1. Representing Data using Static and Moving Patterns Colin Ware UNH

2. Introduction • Finding patterns is key to information visualization. • Expert knowledge is about understanding patterns (Flynn effect) • Example Queries: We think by making pattern queries on the world • Patterns showing groups? • Patterns showing structure? • When are patterns similar? • How should we organize information on the screen? • What makes a pattern distinct?

4. The dimensions of space

5. The “What” Channel • Objects, any location • Simple features specific locations Patterns of patterns

6. Patterns • Feature heirarchy (learned) • Contours and Regions (formed on the fly)

7. V1 processing Ware:Vislab:CCOM

8. Texture segmentation (regions)

9. Textures and low level features

10. Interference based on spatialfrequency

11. Low level tuning based on feature maps

12. A diagram with same principle

13. Field, Hayes and Hess Contour finding mechanisms

14. Results rt = -4.970 + 1.390spl + 0.01699con + 0.654cr + 0.295br spl: Shortest path length con: continuity cr: crossings br: branches 1 crossing adds .65 sec 100 deg. adds 1.7 sec 1 crossing == 38 deg.

15. Connectedness • Connectedness assumed in Continuity

16. Continuity • Visual entities tend to be smooth and continuous

17. Continuity in Diagrams • Connections using smooth lines

18. The mechanisms of line and contour LOC – generalized contour finding Ware:Vislab:CCOM

19. Closure • Closed contours to show set relationship

20. Extending the Euler diagram

21. Collins bubble sets

22. More Contours • Direct application to vector field display

23. Halle’s “little stroaks” 1868 How to add VS? Asymmetry along path Terminations Some End-Stopped neurons respond only with terminations in the receptive field.

24. Modeling V1 and above Dan Pineo

25. Vector Field Visualization Laidlaw

26. An optimization process (NSF ITR) Define task requirements Advection path perceptio Magnitude perception Direction perception Streaklets: A generalized Flow vis technique Identify a visualization Method and a paramaterization Human In the Loop Perceptually optimize for Some sub-set of task requirements Actual solutions Guidelines Algorithms Theory Characterize solutions

27. Key idea • Almost all solutions can be described as being composed of “streaklets” • Mag  color • Mag  luminance • Mag  size (length, width) • Mag  spacing • Orient  orient • Direction  arrow head • Direction  shape • Direction  lum change • Direction  transparency

28. Task: optimize streaklets. (How?) • 1) Streaklet design optimized according to theory – head to tail, direction cues • Modified Jobard and Lefer (Pete Mitchell) • 2) Human in the loop optimization • Genetic algorithms (NO) • Domain experts with a lot of sliders • Designers with a lot of sliders

32. Possibilities for Evaluation • Direction • Magnitude • Advection • Global pattern • Local pattern • Nodal points

33. Back to the feature hierarchy

35. Scatter plots: comparing variables

36. Parallel coords vsGeneralized draftsmans plot

37. Parallel coord vs gen draftsmans • Parallel • Each line is a data • Dimension • Gen drafts • All pairwise scatterplots. • Results suggest • Gen drafts is best • Clusters & correlations Holten and van Wijk

38. Symmetry • Symmetry create visual whole • Prefer Symmetry

39. Symmetry (cont.) • Using symmetry to show Similarities between time series data

40. Bivariate maps (texture + color)

41. 3 Channels: Color, Texture, Motion

42. Compare to this!!

43. Scribble exercise

44. The Magic of Line and Contour: Chameleon lines Santiago Coltrava Saul Steinberg Ware:Vislab:CCOM

45. Ware:Vislab:CCOM

46. Patterns in Diagrams • Patterns applied

47. Visual Grammar of diagrams Entities represented by Discrete objects Attributes: Shape Colors Textures Relationships represented by Connecting lines or nesting regions

48. Semantics of structure

50. Treemaps and hierarchies • Treemaps use areas (size) • SP tree • Graph Trees use connectivity (structure) www.smartmoney.com