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Representing Data using Static and Moving Patterns

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

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Representing Data using Static and Moving Patterns

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  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?

  3. The dimensions of space

  4. The “What” Channel Patterns of patterns

  5. Two parts • Part I: Static Patterns • Part II: Patterns in Motion

  6. Part I: Static Patterns • Gestalt Laws • [Max Westheimer, Kurt Koffka, and Wolfgang Kohler (1912)] • Proximity • Similarity • Continuity • Symmetry • Closure • Relative Size • Figure and Ground

  7. Proximity • Spatial Concentration • Emphasize relationship by proximity a

  8. Proximity

  9. Similarity (Continued) • Separable dimensions • Integral dimensions

  10. Connectedness • Connectedness assumed in Continuity

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

  12. Continuity in Diagrams • Connections using smooth lines

  13. x b a Graph aesthetics (experiment) In Continuity (inv bendiness)

  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. Symmetry • Symmetry create visual whole • Prefer Symmetry

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

  17. Closure • Prefer closed contours

  18. Closure (cont.) • Closed contours to show set relationship

  19. Extending the Euler diagram

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

  21. Relative Size • Smaller components tend to be perceived as objects • prefer horizontal and vertical orientations

  22. Figure and Ground • Symmetry, white space, and closed contour contribute to perception of figure.

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

  24. Field, Hayes and Hess Contour finding mechanisms

  25. More Contours • Direct application to vector field display

  26. Vector fields • Contours and pen strokes, 3D, shading

  27. Vector Field Visualization Laidlaw

  28. Evaluation • Direction • Magnitude • Advection • Global pattern • Local pattern • Nodal points

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

  30. Transparency • Continuity is important in transparency • x < y < z or x > y > z • y < z < w or y > z > w

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

  32. Patterns in Diagrams • Patterns applied

  33. Visual Grammar of diagrams

  34. Semantics of structure

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

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

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

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

  39. Motion is Highly Contextual • Group moving objects in hierarchical fashion.

  40. Frame as motion context • The stationary Dot is perceived as moving in (a). • The circle has no effect on this process in (b).

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

  42. A causal graph

  43. Michotte’s Causality Perception

  44. Visual Causal Vectors

  45. Experiment • Evaluate VCVs • Symmetry about time of contact.

  46. Results Perceived effect

  47. 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)

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