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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?
What makes a pattern distinct?
The dimensions of space
The “What” Channel
Objects, any location
Simple features specific locations
Patterns of patterns
Patterns
Feature heirarchy (learned) Contours and Regions (formed on the fly)
V1 processing
Ware:Vislab:CCOM
Texture segmentation (regions)
Textures and low level features
Interference based on spatialfrequency
Low level tuning based on feature maps
A diagram with same principle
Field, Hayes and Hess
Contour finding mechanisms
Results
rt = -4.970 + 1.390spl + 0.01699con + 0.654cr + 0.295br
spl: Shortest path lengthcon: continuitycr: crossingsbr: branches
1 crossing adds .65 sec100 deg. adds 1.7 sec
1 crossing == 38 deg.
Connectedness
Connectedness assumed in Continuity
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c d
Continuity
Visual entities tend to be smooth and continuous
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a b c
Continuity in Diagrams
Connections using smooth lines
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Ware:Vislab:CCOM
LOC – generalized contour finding
The mechanisms of line and contour
Closure
Closed contours to show set relationship
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AB
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Extending the Euler diagram
Collins bubble sets
More Contours
Direct application to vector field display
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How to add VS?
TerminationsSome End-Stopped neurons respond only with terminations in the receptive field.
Asymmetry along path
Halle’s “little stroaks” 1868
Modeling V1 and aboveDan Pineo
Vector Field Visualization
Laidlaw
Perceptually optimize forSome sub-set of task
requirements
An optimization process (NSF ITR)
Identify a visualizationMethod and a paramaterization
Streaklets:A generalizedFlow vis technique
Characterize solutions
Define task requirementsAdvection path perceptio
Magnitude perceptionDirection perception
Human In the Loop
Actual solutionsGuidelinesAlgorithmsTheory
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
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
Possibilities for Evaluation
Direction Magnitude Advection Global pattern Local pattern Nodal points
Back to the feature hierarchy
Scatter plots: comparing variables
Parallel coords vsGeneralized draftsmans plot
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
Symmetry
Symmetry create visual whole
Prefer Symmetry
Symmetry (cont.) Using symmetry to show Similarities
between time series data
Bivariate maps (texture + color)
3 Channels: Color, Texture, Motion
Compare to this!!
Scribble exercise
Ware:Vislab:CCOM
The Magic of Line and Contour: Chameleon lines
Saul Steinberg Santiago Coltrava
Ware:Vislab:CCOM
Patterns in Diagrams Patterns applied
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cd
Visual Grammar of diagrams Entitiesrepresented byDiscrete objectsAttributes: ShapeColorsTextures
Relationshipsrepresented byConnecting linesor nesting regions
Semantics of structure
Treemaps and hierarchies Treemaps use areas (size) SP tree Graph Trees use connectivity (structure)
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d e
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d e
f g h
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www.smartmoney.com
Top down – Bottom up Tunable attention to patterns Contours and regions + Some are automatic Basic to constructive thinking
Part II: Patterns in Motion
How can we use motion as a display technique?
Gestalt principle of common fate
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.
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a b
Using Causality to display causality
Michotte’s claim: direct perception of causality
A causal graph
Michotte’s Causality Perception
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100 200
50%
100%
Time (msec.)
Direct LaunchingDelayed launchingNo causality
Visual Causal Vectors
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Experiment
Evaluate VCVs Symmetry about time of contact.
Results
aa
Some relationshipCausal relationship
0.0-0.5-1.0 0.5 1.0
Time relative to contact (seconds)
No relationship
p3p1
p2
Per
ceiv
ed e
ffec
t
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)
Anthropomorphic Form from motion
Pattern of moving dots (captured from actor body) – Johansson.
Attach meaning to movements (Heider and Semmel)
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a b
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)
Algorithms
Optimizing trace density (poisson disk) Flexible methods for rendering (enhanced
particle systems).
Figures and Grounds (cont.) Rubin’s Vase
Competing recognition processes
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.
Closure (cont.)
Segmenting screen Creating frame of reference Position of objects judged based on enclosing
frame.
Laciness (Cavanaugh)
Layered data: be careful with composites of textures
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c d
Transparency
Continuity is important in transparency x < y < z or x > y > z y < z < w or y > z > w
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x
y z
w
Limitation due to Frame Rate
Can only show motions that are limited by the Frame Rate.
We can increase by using additional symbols.
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