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Graphics and Graphic Information Processing
J. Bertin
Presented by Fusun Yaman
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Overview
Introduction Description of the paper My favorite sentence Contributions Notes on the references Critique What happened to this topic
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Introduction
Section from Graphics and Graphic Information Processing (1977/1981)
Problem addressed in section B Collection of objects that are described by n
characteristics How to graphically represent this information
when usually n > 3
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Terminology
Information is in Data Table Objects correspond to cases (A, B, C, D) Characteristics correspond to variables
(income,education, experience)
A B C D
Income
Education
Experience
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Terminology (continued)
Objects can be Ordered (0) , like months Reorderable (), like individuals Topographic (T), like cities
Characteristics can be Nominal, like movie titles Ordinal, like movie ratings Quantitative, like length of the movie
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“Impassable barrier”
Image has only 3 dimensions This barrier is impassable
Le n be number of variables (rows) n 3 : Use scatter plots n > 3 : Other solutions needed
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Solutions for n > 3
Constructing several scatter plots Sacrificing overall relationship
Constructing a matrix Overall relationship is discovered by
permutations
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Synoptic
Classifies graphic constructions according to two properties of Data Table If n is number of characteristics
n > 3 and n 3 Nature of objects
Ordered , reorderable, topographic
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Graphics for n 3
Matrix construction when objects are reorderable
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Graphics for n 3
Arrays of curves when objects are ordered
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Graphics for n 3
Scatter plots for both reorderable and ordered cases
Third row is represented by the size of the marker (9)
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Graphics for n 3
In topographies bi- or tri-chromatic superimposition reveals the overall relation ships
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Graphics for n > 3
Objects and characteristics are reorderable () Reorderable matrix
Objects are ordered, characteristics are reorderable
Image file (2) Array of curves when slops are meaningful (3)
Ordered objects and characteristics Collection of tables or maps (4,5) Use super imposition to discover similar groups
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Reorderable Matrix
Objects and characteristics are reorderable () Permutable in x and y Overall relationship is discovered by permutations
What if characteristics are not nominal?
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Special Cases for ()
Weighted matrix Areas become meaningful Applicable to a data table in which row and
column totals are meaningful Limited in dimension
Matrix-file When one of the dimensions is too large Constructed similar to image files Use sorting to discover correlations
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Image File
Used for ordered objects and reorderable characteristics
One card for each characteristic
Values greater than the mean of that row are darkened
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Matrix-File
Special case for permutable matrix; one of the dimensions is too big.
Large number of objects across a small number of characteristics.
Constructed similar to image files
Use sorting to discover correlations
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Matrix-File Example
Ordered by salary, origin, age
Higher salaries are paid to men, who are married, older and who have more childeren then others
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Graphics for Networks
A network portrays the relationships that exists among the elements of a single component. can also be represented in matrix form
If this component is Reorderable: network is transformable on a plane (19) Ordered: network is transformable on one dimension (20) Topography: non-transformable; ordered network (21)
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Utilization of Synoptic
Using synoptic choose the appropriate graphic construction for your data
Deviating from suggested construction leads to loss of information and requires justification
Size limitations
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My favorite Sentence
“A problem involving n rows does not correspond to n problems involving one row.”
“[Graphics] is a strict and simple system of signs, which anyone can learn to use and which leads to better understanding.”
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Contributions
Synoptic Classification scheme for 2D graphical presentation
Permutation Matrix General solution for more than 3 variables
(In the book) Identifies seven visual variables Position,size, value, orientation, color, texture and
shape
Position
Size
Value
Texture
Color
Orientation
Shape
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References
The book has no reference section! Semiology of graphics: Diagrams, networks,
maps, J. Bertin, 1967 Identifies basic elements of diagrams Describes a framework for their design
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Critique
Strength of the paper One image summerizes his
all theory on graphic construction selection
Weakness of the paper No 3D discussion Not easy to follow, lack of
examples (in the given section)
Outdated implementation techniques
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What happened to this topic?
Formed a basis for research in Information Visualization
Graphical constructions and ideas presented in this section are implemented in information visualization tools Tablelens (matrix file) Spotfire (scatter plots using seven visual variables)
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What happened to this topic?
Classification enabled auotomation studies Automating the design of graphical presentations of
relational information, Mackinlay 1987 NSF report, DeFanti (uses the term visualization)
Extension to 3D graphics Information Animation Applications in the capital
markets, Wright 1987 NSF report, DeFanti