Representing Data using Static and Moving Patterns
Learn about representing data using static and moving patterns. Explore Gestalt Laws, continuity, symmetry, closure, and more. Discover how motion can enhance data perception and attention.
Representing Data using Static and Moving Patterns
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Presentation Transcript
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? • Patterns showing structure? • When are patterns similar? • How should we organize information on the screen?
The “What” Channel Patterns of patterns
Two parts • Part I: Static Patterns • Part II: Patterns in Motion
Part I: Static Patterns • Gestalt Laws • [Max Westheimer, Kurt Koffka, and Wolfgang Kohler (1912)] • Proximity • Similarity • Continuity • Symmetry • Closure • Relative Size • Figure and Ground
Proximity • Spatial Concentration • Emphasize relationship by proximity a
Similarity (Continued) • Separable dimensions • Integral dimensions
Connectedness • Connectedness assumed in Continuity
Continuity • Visual entities tend to be smooth and continuous
Continuity in Diagrams • Connections using smooth lines
x b a Graph aesthetics (experiment) In Continuity (inv bendiness)
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.
Symmetry • Symmetry create visual whole • Prefer Symmetry
Symmetry (cont.) • Using symmetry to show Similarities between time series data
Closure • Prefer closed contours
Closure (cont.) • Closed contours to show set relationship
Closure (cont.) • Segmenting screen • Creating frame of reference • Position of objects judged based on enclosing frame.
Relative Size • Smaller components tend to be perceived as objects • prefer horizontal and vertical orientations
Figure and Ground • Symmetry, white space, and closed contour contribute to perception of figure.
Figures and Grounds (cont.) • Rubin’s Vase • Competing recognition processes
Field, Hayes and Hess Contour finding mechanisms
More Contours • Direct application to vector field display
Vector fields • Contours and pen strokes, 3D, shading
Vector Field Visualization Laidlaw
Evaluation • Direction • Magnitude • Advection • Global pattern • Local pattern • Nodal points
Algorithms • Optimizing trace density (poisson disk) • Flexible methods for rendering (enhanced particle systems).
Transparency • Continuity is important in transparency • x < y < z or x > y > z • y < z < w or y > z > w
Laciness (Cavanaugh) • Layered data: be careful with composites of textures
Patterns in Diagrams • Patterns applied
Treemaps and hierarchies • Treemaps use areas (size) • SP tree • Graph Trees use connectivity (structure) www.smartmoney.com
Part II: Patterns in Motion • How can we use motion as a display technique? • Gestalt principle of common fate
Limitation due to Frame Rate • Can only show motions that are limited by the Frame Rate. • We can increase by using additional symbols.
Motion as a visual attribute (Common fate) • correlation between points: • frequency, phase or amplitude • Result: phase is most noticeable
Motion is Highly Contextual • Group moving objects in hierarchical fashion.
Frame as motion context • The stationary Dot is perceived as moving in (a). • The circle has no effect on this process in (b).
Using Causality to display causality • Michotte’s claim: direct perception of causality
Experiment • Evaluate VCVs • Symmetry about time of contact.
Results Perceived effect
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)
