05 visual mappings
TRANSCRIPT
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Weiskopf/Machiraju/Mller
Approaches to Visual
Mappings
CMPT 467/767
VisualizationTorsten Mller
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Overview
Effectiveness of mappings
Mapping to positional quantities
Mapping to shape Mapping to color
Mapping to texture
Other mappings Glyphs
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Readings
Colin Ware:
Chapter 4 (Color)
Chapter 5 (Visual Attention and Informationthat 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
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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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Effectiveness of Mappings
Mapping from (filtered) data to renderable
representation
Most important part of visualization Possible visual representations:
Position
Size
Orientation Shape
Brightness
Color (hue, saturation)
.
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Effectiveness of Mappings
Efficiency and effectiveness depends on
input data:
Nominal
Ordinal
Quantitative
Good visual design
Based on psychology and psychophysics
Psychological investigations to evaluate the
appropriateness of mapping approaches
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Basic Types of Visual
Encodings
Position
Size
(Grey)Value
Texture
Color
Orientation
Shape
Semiology of Graphics [J. Bertin, 67]
Points Lines Areas
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Mapping to Data TypesNominal Ordinal Quantitative
Position
Size
(Grey)Value
Texture
Color
Orientation
Shape!= Good~ = OK
"= Bad8
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Mapping to Data TypesNominal Ordinal Quantitative
Position ! ! !
Size ! ! !
(Grey)Value ! ! ~
Texture ! ~ "
Color ! " "
Orientation ! " "
Shape ! " "
!= Good~ = OK
"= Bad9
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Weiskopf/Machiraju/Mller[Mackinlay, Automating the Design of Graphical Presentations
of Relational Information, ACM TOG 5:2, 1986]
Mackinlays Retinal Variables
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Effectiveness of Mappings
Effectiveness of visual variables According to Mackinlay
[J. Mackinlay: Automating the design of graphical presentations of relational information. ACM
Transactions on Graphics, 5(2), 110-141, 1986]
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Stolte / Hanrahan
Polaris: A System for Query, Analysis andVisualization of Multi-dimensional Relational DatabasesChris Stolte and Pat Hanrahan
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Effectiveness of Mappings
Effectiveness according to neurophysiology
Cells in Visual Areas 1 and 2 differentially tuned
to each of the following properties: Orientation and size (with luminance)
Color (two types of signal)
Stereoscopic depth
Motion
Grapheme Describes a graphical element that is primitive in
visual terms
Makes use of early stages of human vision
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Mapping to Positional
Quantities Mapping to positional quantities:
Position
Size Orientation
Geometric mapping
Typically, very effective visual parameters
Generic diagram methods
0.100
0.325
0.550
0.775
1.000
0.05 0.288 0.525 0.763 1
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Mapping to Positional
Quantities Point diagrams
E.g. scatter plots: visual recognition of
correlations
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Mapping to Positional
Quantities Line and curve diagrams:
Effective perception of differences in
Position and
Length
5000
6250
7500
8750
10000
0 7.5 15 22.5 30
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Mapping to Positional
Quantities Bar graph:
Discrete independent variable (domain):
nominal/ordinal/quantitative
Quantitative dependent variable (data)
5000
6250
7500
8750
10000
1 2 3 4 5 6 7 8 9 10 11 12 13 14
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Mapping to Positional
Quantities Pie charts:
Quantitative data that adds up to a (fixed?)
number
0.6188
0.9944
0.3258
0.4613
0.7847
0.1292
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Mapping to Shape
Especially useful for data with direct
relationship to locations
Convey spatial structures
Examples
Isolines (contour lines)
Height fields
Function graphs (function plots)
Direct encoding of qualitative data
Typically in the form of glyphs
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Mapping to Color
Issues: What kind of data can be color-coded?
What kind of information can be efficiently
visualized?
Areas of application Provide information coding
Designate or emphasize a specific target in a crowded
display
Provide a sense of realism or virtual realism Provide warning signals or signify low probability events
Group, categorize, and chunk information
Convey emotional content
Provide an aesthetically pleasing display
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Mapping to Color
Possible problems:
Distract the user when inadequately used
Dependent on viewing and stimulus conditions
Ineffective for color deficient individuals (use
redundancy)
Results in information overload
Unintentionally conflict with culturalconventions
Cause unintended visual effects and discomfort
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Mapping to Color
Nominal data
Colors need to be
distinguished
Localization of data
Around 8 different
basis colors
co-citation analysis
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Mapping to Color
Nominal data
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Mapping to Color
Ordinal and quantitative data
Ordering of data should be represented by ordering of
colors
Psychological aspects x1< x2< ... < xn! E(c1) < E(c2) < ... < E(cn)
Color coding for scalar data
Assign to each scalar value a different color value
Assignment via transfer function T
T : scalarvalue !colorvalue
Code color values into a color lookup table
0
Scalar "(0,1)
RiGiBi
(0,1)!(0,N-1)255
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Mapping to Color
Pre-shading vs. post-shading
Pre-shading
Assign color values to original function values (e.g. at vertices of a cell)
Interpolate between color values (within a cell)
Post-shading
Interpolate between scalar values (within a cell)
Assign color values to interpolated scalar values
Linear transfer function for color coding
Specify color for fmin and for fmax (Rmin ,Gmin , Bmin) and (Rmax , Gmax , Bmax)
Linearly interpolate between them
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Mapping to Color
Different color spaces lead to different interpolation
functions
In order to visualize (enhance/suppress) specific details,
non-linear color lookup tables are needed Gray scale color table
Intuitive ordering
Rainbow color table
Less intuitive
HSV color model
Perceptual issues
Temperature color table
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Mapping to Color
Bivariate and trivariate color tables are
problematic:
No intuitive ordering
Colors hard to distinguish
Many more color tables for specific
applications
Design of good color tables depends onpsychological / perceptional issues
Often interactive specification of transfer
functions to extract important features
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Mapping to Texture
Main parameters for texture Orientation
Size
Contrast
Alternatively
[Tamura 78]: Coarseness
Roughness Contrast
Directionality
Line-likeness
Regularity
[C.
Ware,
InformationVisualization]
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Mapping to Texture
Goal:
Avoid visual "crosstalk
Orthogonal perceptual channels
Restricts range of parameters
E.g. approximately 30 degrees difference in
orientation needed to distinguish textures
Main application for textures: nominal data
Some applications for direct visualization of
orientations
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Mapping to Texture
Generate texture
Gabor func. as primitives
Parameters:
Orientation
Size
Contrast
Randomly splatter down
Gabor functions Blending yields continuous
coverage
Stochastic texture model
[C.
Ware,
InformationVis
ualization]
Visualization of a magnetic field
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Mapping to Texture
Other stochastic texture models:
LIC (Line integral convolution) for vector field
visualization
Structural models
Procedural description of texture generation
E.g. Lindenmayer systems (L-systems)
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Other Mappings
More advanced mappings possible
Examples for other visual variables Motion
Blink coding
Explicit use of 3D
Multiple attributes
Typical combination of
attributes:
Geometric position,e.g., height field
Color: saturation,
intensity, tone
Texture
Issue: Interference?
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Glyphs
Glyphs and icons
Consist of several components
Features should be easy to distinguish andcombine
Icons should be separated from each other
Mainly used for multivariate
discretedata
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Glyphs
Multi-dimensional coding Perceptual independence?
Integraldisplay dimensions Two or more attributes perceived holistically
Separabledimensions Separate judgments about each graphical
dimension
Simplistic classification, with a large
number of exceptions and asymmetries
More integral coding pairs
More separable coding pairs
[C.W
are,
InformationVisualization]
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Glyphs
Interesting graphical attributes for basic glyph design
[according to C. Ware, Information Visualization]
Visual variable Dimensionality
Spatial position of glyph 3 dimensions: X, Y, ZColor of glyph 3 dimensions: defined by color opponent theory
Shape 23? dimensions unknown
Orientation 3 dimensions: corresponding to orientation about each
of the primary axes
Surface texture 3 dimensions: orientation, size, and contrastMotion coding 23? Dimensions largely unknown, but phase may be
useful
Blink coding: The glyph
blinks on and off at some
rate
1 dimension
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Glyphs
Color icons [Levkowitz 91]
Subdivision of a basic figure (triangle,
square, ) into edges and faces
Mapping of data to faces
via color tables
Grouping by emphasizing
edges or faces
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Glyphs
Stick-figure icon [Picket & Grinstein 88]
2D figure with 4 limbs
Coding of data via Length
Thickness
Angle with vertical axis
12 attributes
Exploits the human
capability to recognize
patterns/textures
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Glyphs
Stick-figure icon
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Glyphs
Circular icon plots:
Star plots
Sun ray plots
etc
Follow a "spoked wheel" format
Values of variables are represented by
distances between the center ("hub") of the
icon and its edges
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Glyphs
Star glyphs[S. E. Fienberg: Graphical methods in statistics.
The American Statistician, 33:165-178, 1979]
A star is composed of equally spaced radii, stemming
from the center
The length of the spike is proportional to the value of
the respective attribute
The first spike/attribute
is to the right
Subsequent spikesare counter-clockwise
The ends of the rays
are connected by a line
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Glyphs
Sun ray plots
Similar to star glyphs/plots
Underlying star-shaped structure
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Glyphs
Chernoff faces/icons[H. Chernoff (1973). The use of faces to represent points in k-dimensional space
graphically. Journal of the American Statistical Association , 68 :361-368]
Each facial feature represents one variable
Human ability to
distinguish small
features in faces
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Glyphs
Chernoff faces/icons
Possible assignment in the decreasing order of importance: Area of the face
Shape of the face
Length of the nose Location of the mouth
Curve of the smile
Width of the mouth
Location, separation, angle, shape, and width of the eyes
Location of the pupil
Location, angle, and width of the eyebrows
Coding of 15 attributes
Additional variables could be encoded by making faces
asymmetric
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Glyphs
Chernoff faces/icons
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Glyphs
Face morphing [Alexa 98]