1. Graphics and Graphic Information Processing J. Bertin Presented by Fusun Yaman

2. Overview  Introduction  Description of the paper  My favorite sentence  Contributions  Notes on the references  Critique  What happened to this topic

3. 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

4. Terminology  Information is in Data Table  Objects correspond to cases (A, B, C, D)  Characteristics correspond to variables (income,education, experience) ABCD Income Education Experience

5. 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

6. “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

7. Solutions for n > 3  Constructing several scatter plots Sacrificing overall relationship  Constructing a matrix Overall relationship is discovered by permutations

8. 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

10. Graphics for n  3  Matrix construction when objects are reorderable

11. Graphics for n  3  Arrays of curves when objects are ordered

12. Graphics for n  3  Scatter plots for both reorderable and ordered cases  Third row is represented by the size of the marker (9)

13. Graphics for n  3  In topographies bi- or tri-chromatic superimposition reveals the overall relation ships

14. 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

15. Reorderable Matrix  Objects and characteristics are reorderable (  )  Permutable in x and y  Overall relationship is discovered by permutations  What if characteristics are not nominal?

16. 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

17. Image File  Used for ordered objects and reorderable characteristics  One card for each characteristic  Values greater than the mean of that row are darkened

18. 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

19. 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

20. 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)

21. 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

22. 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.”

23. 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

24. 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

25. 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

26. 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)

27. 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