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 spatial frequency

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

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

24. Modeling V1 and above Dan Pineo

25. Vector Field Visualization Laidlaw

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

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

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

51. Top down – Bottom up Tunable attention to patterns Contours and regions + Some are automatic Basic to constructive thinking

52. Part II: Patterns in Motion How can we use motion as a display technique? Gestalt principle of common fate

53. Motion as a visual attribute (Common fate) correlation between points: frequency, phase or amplitude Result: phase is most noticeable

54. Motion is Highly Contextual Group moving objects in hierarchical fashion.

55. Using Causality to display causality Michotte’s claim: direct perception of causality

56. A causal graph

57. Michotte’s Causality Perception

58. Visual Causal Vectors

59. Experiment Evaluate VCVs Symmetry about time of contact.

60. Results Perceived effect

61. Motion Patterns that attract attention (Lyn Bartram) Motion is a good attention getter in periphery The optimal pattern may be things that emerge, as opposed to simply move. We may be able to perceive large field patterns better when they are expressed through motion (untested)

62. Anthropomorphic Form from motion Pattern of moving dots (captured from actor body) – Johansson. Attach meaning to movements (Heider and Semmel)

63. Conclusion Gestalt Laws are useful as design guidelines. Patterns should be present in luminance Patterns should be the appropriate size Motion is under-researched, but evidence suggest its power. Simple motion coding can be used to express communication, causality, urgency, happiness? (Braitenberg)

65. Algorithms Optimizing trace density (poisson disk) Flexible methods for rendering (enhanced particle systems).

67. Figures and Grounds (cont.) Rubin’s Vase Competing recognition processes

68. Show particle solutions Problem: how do we create an optimal solution out of all of these possibilities? Standard solution: do studies and measure the effect of different parameters. Problem: Too many alternatives.

69. Closure (cont.) Segmenting screen Creating frame of reference Position of objects judged based on enclosing frame.

70. Laciness (Cavanaugh) Layered data: be careful with composites of textures

71. Transparency Continuity is important in transparency x y > z y z > w

73. Limitation due to Frame Rate Can only show motions that are limited by the Frame Rate. We can increase by using additional symbols.