visual encoding principlesdap.vsb.cz/cass2019/pres/encoding_principles.pdf · • munzner,...

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Visual Encoding Principles

VisualizationChristoph Kralj

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Overview• Marks + channels • Channel effectiveness

– Accuracy – Discriminability – Separability – Popout

• Channel characteristics – Spatial position – Colour – Size – Tilt (angle) – Shape (glyph) – Stipple (texture) – Curvature – Motion �2

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Readings• Munzner, “Visualization Analysis and Design”:

– Chapter 5 (Marks and Channels) • Colin Ware:

– Chapter 4 (Color) – Chapter 5 (Visual Attention and Information that

Pops Out) • The Visualization Handbook:

– Chapter 1 (Overview of Visualization) • Additional (background) reading

– J. Mackinlay: Automating the design of graphical presentations of relational information. ACM ToG, 5(2), 110-141, 1986

Marks + Channels

Rectangle

Width

Height

Text x-position

y-position

color

Line

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Visual Language is a Sign System

• Image perceived as a set of signs • Sender encodes information in signs • Receiver decodes information from

signs

• Jacques Bertin – French cartographer [1918-2010] – Semiology of Graphics [1967] – Theoretical principles for visual encodings

Semiology of Graphics [J. Bertin, 83]

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Image

Visual language is a sign system

Images perceived as a set of signs

Sender encodes information in signs

Receiver decodes information from signs

Semiology of Graphics, 1983

Jacques Bertin

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According to Bertin ...

Position

Size

(Grey)Value

Texture

Color

Orientation

ShapeSemiology of Graphics [J. Bertin, 67]

Points Lines AreasMarks

Cha

nnel

s

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Stolte / Hanrahan

“Polaris: A System for Query, Analysis and Visualization of Multi-dimensional Relational Databases”, Chris Stolte and Pat Hanrahan

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http://graphics.stanford.edu/papers/polaris/

• Mark: basic graphical element / geometric primitive:

• point (0D)

• line (1D)

• area (2D)

• volume (3D)

• Channel: control appearance (of a mark)

• position

• size

• shape

• orientation

• hue, saturation, lightness

• etc.

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Munzner

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Horizontal

Position

Vertical Both

Color

Shape Tilt

Size

Length Area Volume

Points Lines Areas

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Progression

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Progression

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Progression

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Progression

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Tableau Example

• Let us use now different visual encodings in a new sheet

• Create a new sheet using the following fields: Family Income Gini Coefficient, Happiness Score, GDP per capita, Internet Access Population

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Tableau Example

• Is there a correlation (visually) between GDP and Internet Access?

• Which country has a very low Gini Coeff but a high GDP?

• What else can you see?

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Tableau Example

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What vs. How Much channels• What: categorical

– shape – spatial region – colour (hue)

• How Much: ordered (ordinal, quantitative) – length (1D) – area (2D) – volume (3D) – tilt – position – colour (lightness) �18

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Mark types• tables: item = point • network: node+link • link types:

– connection: relationship btw. two nodes – containment: hierarchy

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Marks as Items/Nodes

Marks as Links

Points Lines Areas

Containment Connection

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Expressiveness + Effectiveness

• expressiveness principle: – visual encoding should express all of, and

only, the information in the dataset attributes

– lie factor

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• effectiveness principle: – importance of the attribute should match

the salience of the channel – data-ink ratio

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• effectiveness principle: – importance of the attribute should match

the salience of the channel – data-ink ratio

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Effectiveness -- Accuracy

• perceptualjudgementvs. stimulus

• Weber’s law: S = In

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Magnitude Channels: Ordered Attributes Identity Channels: Categorical Attributes

Spatial region

Color hue

Motion

Shape

Position on common scale

Position on unaligned scale

Length (1D size)

Tilt/angle

Area (2D size)

Depth (3D position)

Color luminance

Color saturation

Curvature

Volume (3D size)

Channels: Expressiveness Types and Effectiveness Ranks


Tableau Example

• Let us test such difference by comparing area and length as channels for a quantitative value

• Lets look at the Cellular Subscriptions per continent (Map Reference)

• We will use a Heat Map as area encoding and a bar chart as length encoding

• Which one is easier to read?

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Tableau Example

• On the right side is a Show Me button and there you can change easily the encoding

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Tableau Example

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Tableau Example

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Effectiveness -- Separability

• separable vs. integral channels

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Popout - Preattentive processing

• parallel (visual processing)

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Overview• Marks + channels • Channel effectiveness • Channel characteristics

– Spatial position – Color – Size – Tilt (angle) – Shape (glyph) – Stipple (texture) – Curvature – Motion �35

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Channels

• Spatial position: most effective for all data types

• Size: ‘how much’, interacts with others • Shape/Glyph: ‘what channel’ • Stipple/texture: less popular today • Curvature: less popular today • Motion: large popout effect

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Colour

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Thanks to Moritz Wustinger

Thanks to Moritz WustingerSmiley based on http://upload.wikimedia.org/wikipedia/commons/b/bd/A_Smiley.jpg

http://upload.wikimedia.org/wikipedia/commons/b/bd/A_Smiley.jpg

Color deficiency

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Model “Color blindness” • Flaw in opponent processing

– Red-green common (deuteranope, protanope) – Blue-yellow possible (tritanope -- most common) – Luminance channel almost “normal”

• 8% of all men, 0.5% of all women • Effect is 2D color vision model

– Flatten color space – Can be simulated (Brettel et. al.) – http://colorfilter.wickline.org – http://www.colblindor.com/coblis-color-

blindness-simulator/�43

Source: M. Stone

http://colorfilter.wickline.org

http://www.colblindor.com/coblis-color-blindness-simulator/

http://www.colblindor.com/coblis-color-blindness-simulator/

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Color Blindness

DeuteranopeProtanope TritanopeNo M cones No L cones No S cones

Red / green deficiencies

Blue / Yellow deficiency

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Source: M. Stone

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Color-Blindness

Normal Protanope Deuteranope Lightness�45

Source: M. Stone

Using Color

Categorical Data

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Categorical Data

• Limited distinguishability (8-14) – Best with Hue – Best choices from Ware:

Source: Ware, “Information Visualization”

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Maximum Hue Separation

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Source: H.P. Pfister

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Analogous, yet distinctSource: H.P. Pfister

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Primary Colors

• Primary color terms are remarkably consistent across cultures [Berlin & Kay, 69]

Source: Ware, “Information Visualization”

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Take-home message

• Only a small number of colors can be used effectively as categorical labels

• Keep the number of colors for categorical data to less than eight, and use quiet medium grey backgrounds

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Small Areas

Tableau Software

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© Munzner/Möller Tufte, VDQI (Vol. 1), p. 77

Large Areas

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Large Areas

�55Tufte, VDQI (Vol. 1), p. 77

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Large Areas

Cleveland & McGill, “A Color-Caused Optical Illusion on a Statistical Graph”, 1983

1 = Red is bigger 2 = Green is bigger 3 = Both look the same

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Color Size Illusion

�57Cleveland & McGill, “A Color-Caused Optical Illusion on a Statistical Graph”, 1983



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Maps

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Source: Ware, “Visual Thinking for Design”

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Take-home message

• Color in small regions is difficult to perceive, and bright colors in large areas appear bigger

• Use bright, saturated colors for small regions, and use low saturation pastel colors for large regions and backgrounds

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Tableau Example

• Let us use our map to test this! • Try to use Map Reference or Biggest

official Langauge as color for our map. • What works and doesn’t work?

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Tableau Example

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Ordinal

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Order These Colors

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Source: J. Stasko

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Order These Colors

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Source: J. Stasko

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Order These Colors

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Source: J. Stasko

Order These Colors

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Source: J. Stasko

http://colorbrewer2.org/

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Take-home message

• Lightness and saturation are effective for ordinal data because they have an implicit perceptual ordering

• Show ordinal data with a discrete set of color values that change in lightness or saturation

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Quantitative

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Rainbow Colormap

Rogowitz and Treinish, Why should engineers and scientists be worried about color?

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Rainbow Colormap

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Rogowitz and Treinish, Why should engineers and scientists be worried about color?




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Rainbow Colormap

• Hue is used to show ordinal data

• Not perceptually linear: Equal steps in the continuous range are not perceived as equal steps

• Not good for colorblind people

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Rainbow colour map

• Learned order • Visually segmented

– Solution — isoluminant rainbow – Solution — discretize colormap

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Take-home message

• Quantitative data can be shown with a discrete or continuous colormap

• Use colormaps with a limited hue palette and redundantly vary lightness and saturation, and use discrete colormaps for accuracy

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Tableau Example

• Let us use our map again to test different color scales by using Cellular Subscriptions as color

• Compare the continuous color scale to a discrete color scale

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Tableau Example

• Right click on the Color Button opens …

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Tableau Example

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Continuous Stepped

Color and Tasks

Color Aesthetics

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Why Should We Care?

• Poorly designed color is confusing – Creates visual clutter – Misdirects attention

• Poor design devalues the information – Visual sophistication – Evolution of document and web design

• Don Norman:“Attractive things work better”

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Source: M. Stone

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Relative vs absolute judgement

• Weber’s law says that everything is relative, i.e. the “intensity” depends on the background signal

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�91https://thenextweb.com/dd/2015/05/15/7-most-common-data-visualization-mistakes/#.tnw_LZw9olbK

Unframed Framed

�93http://de.wikipedia.org/wiki/Optische_Täuschung

http://de.wikipedia.org/wiki/Optische_T%C3%A4uschung

In conclusion

• Visualizations are broken down into marks (elements) and channels (parameters)

• Data is linked to these channels • Alot of care in choosing which and how

many channels to use

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Overview• Marks + channels • Channel effectiveness

– Accuracy – Discriminability – Separability – Popout

• Channel characteristics – Spatial position – Colour – Size – Tilt (angle) – Shape (glyph) – Stipple (texture) – Curvature – Motion �95

visual encoding principlesdap.vsb.cz/cass2019/pres/encoding_principles.pdf · • munzner,...

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