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Visualization Design

Visualization Design

What data attributes should be shown?Investigate hypotheses, check assumptions.Start with an overview, then drill-down.Communicate findings to others. (Simplify?)

How should the data be visually encoded?Choose visual variables for data attributes.Adjust other visual properties for aesthetics.

Try Multiple Alternatives & Iterate!

Choosing Visual Encodings

Challenge: Assume we have 8 visual encoding variables.Pick the best encoding from the exponential number of possibilities (n+1)8

Principle of Consistency: The properties of the image (visual variables) should match the properties of the data.

Principle of Importance Ordering: Encode the most important information in the most effective way.

Design Criteria (Mackinlay)

ExpressivenessA set of facts is expressible in a visual language if the sentences (i.e. the visualizations) in the language express all the facts in the set of data, and only the facts in the data.

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Cannot express the facts

A one-to-many (1 → N) relation cannot be expressed in a single horizontal dot plot because multiple tuples are mapped to the same position

Expresses facts not in the dataA length is interpreted as a quantitative value;∴ Length of bar says something untrue about N data

[Mackinlay, APT, 1986]

Design Criteria (Mackinlay)

ExpressivenessA set of facts is expressible in a visual language if the sentences (i.e. the visualizations) in the language express all the facts in the set of data, and only the facts in the data.

EffectivenessA visualization is more effective than another visualization if the information conveyed by one visualization is more readily perceived than the information in the other visualization.

But what makes a visual encoding “effective”?

Graphical Perception(choosing perceptually effective encodings)

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Which best encodes quantities?

PositionLengthAreaVolumeValue (Brightness)Color HueOrientation (Angle)Shape

Detecting Brightness

Which is brighter?

Detecting Brightness

Which is brighter?

(128, 128, 128) (144, 144, 144)

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Detecting Brightness

Which is brighter?

Detecting Brightness

Which is brighter?

(134, 134, 134) (128, 128, 128)

Just Noticeable Difference

Many continuous variations are perceived in discrete steps

Steps in font sizeSizes standardized in 16th century

a a a a a a a a a a a a a a a a6 7 8 9 10 11 12 14 16 18 21 24 36 48 60 72

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Information in color and value

Value is perceived as ordered∴Encode ordinal variables (O)

∴ Encode continuous variables (Q) [not as well]

Hue is normally perceived as unordered∴ Encode nominal variables (N) using color Compare area of circles

Compare length of bars

Steven’s Power Law

p < 1 : underestimatep > 1 : overestimate

[graph from Wilkinson 99, based on Stevens 61]

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Which best encodes quantities?

PositionLengthAreaVolumeValue (Brightness)Color HueOrientation (Angle)Shape

Cleveland & McGill, Graphical Perception 1984

[Cleveland and McGill 84] [Cleveland and McGill 84]

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Most accurate Position (common) scalePosition (non-aligned) scale

Length

Slope

Angle

Area

Volume

Least accurate Color hue-saturation-density

Mackinlay’s Ranking

Conjectured effectiveness of the encoding

Pre-Attentive Processing(highlighting and discrimination)

How many 3’s

1281768756138976546984506985604982826762980985845822450985645894509845098094358590910302099059595957725646750506789045678845789809821677654876364908560912949686

[based on slide from Stasko]

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How many 3’s

1281768756138976546984506985604982826762980985845822450985645894509845098094358590910302099059595957725646750506789045678845789809821677654876364908560912949686

[based on slide from Stasko]

Visual pop-out: Color

http://www.csc.ncsu.edu/faculty/healey/PP/index.html

Visual pop-out: Shape

http://www.csc.ncsu.edu/faculty/healey/PP/index.html

Feature Conjunctions

http://www.csc.ncsu.edu/faculty/healey/PP/index.html

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Pre-Attentive features

[Information Visualization. Figure 5. 5 Ware 04]

Using Pre-attentive Cues

Use pre-attentive features to draw attentionEmphasize important informationInteractive highlighting and selection

Not all pre-attentive features are equalFor example, color can override shape

Combining pre-attentive features often fails

Separation & Layering

Layering: Gridlines

Electrocardiogram tracelines [from Tufte 90]

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Layering: Gridlines

Stravinsky score [from Tufte 90]

Layering: color and line width

IBM Series III Copier [from Tufte 90]

Separation: Small Multiples

[Figure 2.11, p. 38, MacEachren 95]

Separation and Layering

Don’t confuse foreground & backgroundColor luminance or hue to distinguish layers Use faint gridlines

Don’t overload graphicsToo many dimensions can hamper perceptionSmall multiples may show patterns clearly

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Color

Simultaneous contrast

Josef Albers

Simultaneous contrast

Josef Albers

CrispeningPerceived difference depends on background

From Fairchild, Color Appearance Models

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Information in color and value

Value is perceived as ordered∴Encode ordinal variables (O)

∴ Encode continuous variables (Q) [not as well]

Hue is normally perceived as unordered∴ Encode nominal variables (N) using color

Hints for Effective Color Design

Use only a few colors (~6 ideal)Colors should be distinctive and namedStrive for color harmony (natural colors)Use cultural conventions; appreciate symbolismBeware of bad interactions (red/blue etc.)Get it right in black and whiteRespect the color blind

Quantitative Color Encoding

Rainbows are often seen, but do they encode the data effectively?

Avoid rainbow color maps!

1. People segment colors into classes2. Hues are not naturally ordered3. Different lightness emphasizes certain scalar values4. Low luminance colors (blue) hide high frequencies

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Sequential color scheme Sequential color scheme

Diverging color scheme Diverging color scheme

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Quantitative Color Encoding

Sequential Color ScaleConstant hue, vary luminanceMap higher values to darker colors

(for light background)

Diverging Color ScaleUseful when data has a meaningful “midpoint”Use neutral color (e.g., gray) for midpointUse saturated colors for endpoints

Limit number of steps in color to 3-9

Classing Quantitative Data

Age-adjusted mortality rates for the United States.

Classed colors may be more effective with appropriate bins (e.g., quantiles vs. linear steps)

Color BlindnessMissing one or more retinal color receptorsIn USA, 7-10% males are red-green color blind

Protanope Deuteranope Luminance

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VisCheck [Dougherty & Wade]

Simulates color vision deficienciesWeb service or Photoshop plug-inA number of other (free) tools also exist

Deuteranope Protanope Tritanope

Hints for Effective Color Design

Use only a few colors (~6 ideal)Colors should be distinctive and namedStrive for color harmony (natural colors)Use cultural conventions; appreciate symbolismBeware of bad interactions (red/blue etc.)Get it right in black and whiteRespect the color blind

Visualization Design

Choose data attributes to visualize.

Choose appropriate visual encodings.Expressiveness: be faithful to the dataEffectiveness: leverage perceptual knowledge

Experiment with multiple alternatives.

Iteratively improve your design.Analyze your choice of encodingsGet feedback (e.g., user testing, expert review)