Visual thinking colin_ware_lectures_2013_4_patterns
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Representing Data using Static and Moving Patterns discusses various techniques for visualizing patterns in data using static and dynamic representations. Key points include: finding patterns is important for information visualization; static patterns include objects, contours, and regions; motion can be used to show causality, relationships, and attract attention; dynamic representations leverage Gestalt principles of common fate and motion to group related items. The document explores using static contours, connected lines, motion trajectories, and other techniques to effectively display patterns in data.
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Visual thinking colin_ware_lectures_2013_4_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 Patterns of patterns Simple features specific locations
- 6. Patterns Feature heirarchy (learned) Contours and Regions (formed on the fly)
- 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 a c b d
- 16. Continuity Visual entities tend to be smooth and continuous a b c
- 17. Continuity in Diagrams Connections using smooth lines a b
- 18. The mechanisms of line and contour LOC – generalized contour finding Ware:Vislab:CCOM
- 19. Closure Closed contours to show set relationship A B C D
- 20. Extending the Euler diagram
- 22. More Contours a b Direct application to vector field display
- 23. How to add VS? Asymmetry along path Terminations Some End-Stopped neurons respond only with terminations in the receptive field. Halle’s “little stroaks” 1868
- 24. Modeling V1 and above Dan Pineo
- 26. An optimization process (NSF ITR) Define task requirements Advection path perceptio Magnitude perception Direction perception Identify a visualization Method and a paramaterization Perceptually optimize for Some sub-set of task requirements Streaklets: A generalized Flow vis technique Human In the Loop Characterize solutions 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 Gen drafts Each line is a data Dimension 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
- 44. The Magic of Line and Contour: Chameleon lines Saul Steinberg Santiago Coltrava Ware:Vislab:CCOM
- 45. Ware:Vislab:CCOM
- 46. Patterns in Diagrams Patterns applied a c b d
- 47. Visual Grammar of diagrams Entities represented by Discrete objects Attributes: Shape Colors Textures Relationships represented by Connecting lines or nesting regions
- 50. Treemaps and hierarchies Treemaps use areas (size) SP tree Graph Trees use connectivity (structure) a b a b c f d e g h i www.smartmoney.com a bc i de f gh
- 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. a b
- 55. Using Causality to display causality Michotte’s claim: direct perception of causality
- 56. A causal graph
- 57. Michotte’s Causality Perception 10 0% D e t Lu c i g i c a nhn r Dl ydl u c i g ea e a n hn N cua t o a s liy 5% 0 10 0 T e( s c i me .) m 20 0
- 59. Experiment Evaluate VCVs Symmetry about time of contact.
- 60. Results Perceived effect Cu a r lat n h a s l e io s ip p 1 S m re tio s i o e la n hp p 3 N r lat n h o e io s ip p 2 -1 .0 0 .5 -0 .5 0 .0 T ere tiv toco ta (se o d im la e n ct c n s) 1 .0
- 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) a b
- 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) a b Layered data: be careful with composites of textures c d
- 71. Transparency Continuity is important in transparency x < y < z or x > y > z y < z < w or y > z > w x w y b a z
- 73. Limitation due to Frame Rate λ Can only show motions that are limited by the Frame Rate. We can increase by using additional symbols. a b c