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© Munzner/Möller
Visual Encoding Principles
VisualizationChristoph Kralj
© Munzner/Möller
Overview• Marks + channels • Channel effectiveness
– Accuracy – Discriminability – Separability – Popout
• Channel characteristics – Spatial position – Colour – Size – Tilt (angle) – Shape (glyph) – Stipple (texture) – Curvature – Motion �2
© Munzner/Möller �3
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
�8
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Stolte / Hanrahan
“Polaris: A System for Query, Analysis and Visualization of Multi-dimensional Relational Databases”, Chris Stolte and Pat Hanrahan
�9
• 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
�15
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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
�19
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
�20
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Expressiveness + Effectiveness
• effectiveness principle: – importance of the attribute should match
the salience of the channel – data-ink ratio
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© Munzner/Möller
Expressiveness + Effectiveness
• 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
�23
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
© Munzner/Möller/Sedlmair
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?
�25
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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
�29
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Popout - Preattentive processing
• parallel (visual processing)
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Popout - Preattentive processing
• parallel (visual processing)
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Popout - Preattentive processing
• parallel (visual processing)
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© Munzner/Möller
Popout - Preattentive processing
• parallel (visual processing)
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Popout - Preattentive processing
• parallel (visual processing)
�34
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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
�37
Thanks to Moritz Wustinger
Thanks to Moritz Wustinger
Thanks to Moritz Wustinger
Thanks to Moritz WustingerSmiley based on 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
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Color Blindness
DeuteranopeProtanope TritanopeNo M cones No L cones No S cones
Red / green deficiencies
Blue / Yellow deficiency
�44
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
�49
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”
�51
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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
�52
Source: H.P. Pfister
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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
�56
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Color Size Illusion
�57Cleveland & McGill, “A Color-Caused Optical Illusion on a Statistical Graph”, 1983
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Maps
�58
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
�59
Source: H.P. Pfister
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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
138
Source: J. Stasko
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Brewer Scales
Cynthia Brewer, Color Use Guidelines for Data Representation
Nominal
Ordinal
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SequentialSource: H.P. Pfister
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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
�69
Source: H.P. Pfister
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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Color Segmentation
C. Ware, “Visual Thinking for Design”
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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
�76
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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
�79
Continuous Stepped
Color and Tasks
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Task: Find high & low anomalies
http://www.jason.oceanobs.com/html/donnees/las/2005-03_uk.html
© Munzner/Möller
Task: Find high & low anomalies
http://www.jason.oceanobs.com/html/donnees/las/2005-03_uk.html
© Munzner/Möllerhttp://www.jason.oceanobs.com/html/donnees/las/2005-03_uk.html
Task: Understand relative height
© Munzner/Möllerhttp://www.jason.oceanobs.com/html/donnees/las/2005-03_uk.html
Task: Find height 120
Color Aesthetics
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DANGER!
!Inappropriate use of colour can be disasterous to the application
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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”
�87
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
�88
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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
�89
© Munzner/Möller
Relative vs absolute judgement
• Weber’s law says that everything is relative, i.e. the “intensity” depends on the background signal
�90
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Relative vs absolute judgement
�91https://thenextweb.com/dd/2015/05/15/7-most-common-data-visualization-mistakes/#.tnw_LZw9olbK
Unframed Framed
�93http://de.wikipedia.org/wiki/Optische_Täuschung
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
�94
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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
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