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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Continuity

Visual entities tend to be smooth and continuous

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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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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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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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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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Using Causality to display causality

Michotte’s claim: direct perception of causality

A causal graph

Michotte’s Causality Perception

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50%

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Time (msec.)

Direct LaunchingDelayed launchingNo causality

Visual Causal Vectors

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Experiment

Evaluate VCVs Symmetry about time of contact.

Results

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Some relationshipCausal relationship

0.0-0.5-1.0 0.5 1.0

Time relative to contact (seconds)

No relationship

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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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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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Transparency

Continuity is important in transparency x < y < z or x > y > z y < z < w or y > z > w

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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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