1. CSc4730/6730 Scientific Visualization
Lecture 26
Visualization Engines –
Toward Automatic Visualization Design
Ying Zhu
Georgia State University

2. Motivation
Make the construction of data visualization transparent to end users
Given a data set, computer programs automatically generate data visualizations with minimal or no user input
Users focus on problem solving rather than the technical details of creating data visualizations

3. Challenges
Given a data set, there are many possible visualization configurations
How to narrow down the possible chart types and visual variables?
How to select the most fitting charts and visual variables for visualization?

4. General approach Specify a list of data types
Specify a list of chart types and visual variables
For each chart type, specify its visual elements (e.g., axes, visual variables, etc.)
Create rules for matching data types to chart types
Create a ranking system for different chart types with regards to different data types

5. General approach Specify rules for encoding data with visual variables
Certain data types can only be mapped to certain visual variables
Create a ranking system to select the most suitable visual variable for each data type
Create rules for compositing multiple visual variables

6. General approach The process Collect data and tasks
Select a chart type based on data, tasks, the rules and chart rankings
For each data parameter, choose a visual variable based on data type, rules, and visual variable ranking
Composite the visualization

7. Rules and rankings
The most important part of a visualization engine is the rules and rankings
Rules are created to put restrictions on the mapping between data types and visual elements
Rankings are created to help select the most suitable visual element for a particular data type or task

8. Four examples
Mackinlay, J. (1986). "Automating the Design of Graphical Presentations of Relational Information." ACM Transactions on Graphics 5(2):
Casner, S. M. (1991). "A task-analytic approach to the automated design of graphic presentation." ACM Transactions on Graphics 10(2):
Salisbury, L. D. P. (2001). Automatic Visual Display Design and Creation. PhD Thesis, Department of Department of Computer Science and Engineering, University of Washington.
Mackinlay, J. D., P. Hanrahan, et al. (2007). "Show Me: Automatic Presentation for Visual Analysis." IEEE Transactions on Visualization and Computer Graphics 13(6):

9. ADP by MacKinlay
Mackinlay, J. (1986). "Automating the Design of Graphical Presentations of Relational Information." ACM Transactions on Graphics 5(2):

10. Basic idea
The fundamental assumption of the approach is that graphical presentations are sentences of graphical languages, which are similar to other formal languages in that they have precise syntactic and semantic definitions.

11. How to evaluate a visualization design?
Expressiveness
Encodes all the data in the set
Encodes only the data in the set
Effectiveness
Based on Cleveland and McGill’s ranking system
People interpret graphical presentation of quantitative information with different degrees of accuracy

12. Data types
Quantitative
Nominal
Ordinal

13. Chart types, visual variables, etc.

14. Visual variables Position Length Angle Slope Area Color density
Color hue, etc.

15. Cleveland and McGill’s ranking of visual variables

16. MacKinlay’s ranking of visual variables by data types

17. Rules for data encoding

18. Composition
Principle of Composition: Compose two designs by merging parts that encode the same information.

19. Example

20. Example

21. Automated Design of Graphic Presentations
Casner, S. M. (1991). "A task-analytic approach to the automated design of graphic presentation." ACM Transactions on Graphics 10(2):

22. Basic idea Describe the task with Logical Task Description Language
Much like a programming language
Use logical operators to describe user’s tasks
Convert the Logical Representation into Visual Representation
Replace some logical operators with equivalent perceptual operators

23. Basic idea
Use both the perceptual operator and data types as input to select chart types and visual variables
The visual mapping part is largely based on MacKinlay’s method

24. Basic idea
Casner’s main contribution is to introduce perceptual operators (tasks) as an input along with data types
The perceptual tasks are extracted from an logical description of the user problem solving process

25. Logical Task Description

26. Logical Problem Description

27. Approach Replace logical operators with perceptual operators
Consider each of the logical search and computation operators in the logical procedure
Try to locate perceptual search and computation operators that give the user the same result

28. Visual variables

29. Perceptual operators (task)

30. Perceptual operators
If more than one perceptual operator qualifies as a legal replacement for a given logical operator, then choose one based on a ranking system.

31. Ranking of perceptual operators

32. Rules for visual encoding

33. Rules for composition

34. Logical Problem Description

35. The perceptual description

36. Automatically generated visualization

37. Automated visualization design for urban planning
Salisbury, L. D. P. (2001). Automatic Visual Display Design and Creation. PhD Thesis, Department of Computer Science and Engineering, University of Washington.

38. Basic approach Specify a list of cognitive components for user tasks
Search, identify, compare, etc.
Created a ranking system for chart types based on cognitive components of tasks
Created a ranking system for visual variables
Created rules for mapping data types to chart types and visual variables

39. Basic approach The process Collect data Users specify tasks
The system selects a chart type based on data and user tasks
The system maps data parameters to visual variables based on the rules and rankings

40. Basic approach Salisbury’s method is also based on MacKinlay’s
Data types and visualization types are similar to MacKinlay’s
The visual encoding rules are largely based on MacKinlay’s method
The ranking of visual variables are largely based on MacKinlay’s

41. Basic approach Key differences:
Introduced a more detailed task component classification
Divide a task into cognitive components
Created a chart type ranking based on two user studies
Chart types are selected based on the cognitive components of a task

42. Chart types

43. Rules for chart configuration

44. Rules for encoding

45. Rules for matching data with visual variables

46. Create a chart ranking
Create a chart ranking based on two user studies
The first user study was designed to determine what types of visualizations urban planners are willing to use.
The second user study was performed to find out the usefulness of the different visualization types and to verify the correctness and utility of our design approach and strategies.

47. Tasks

48. User study

49. User study

50. User study

51. User study

52. Tasks

53. Second user study

57. Basic process
Break down the task into its constituent cognitive processing activities
Choose the base visualization type
Determine which encoding methods to use and how to map the data to the chosen encoding methods

61. Examples

62. Examples

63. “Show Me” in Tableau
Mackinlay, J. D., P. Hanrahan, et al. (2007). "Show Me: Automatic Presentation for Visual Analysis." IEEE Transactions on Visualization and Computer Graphics 13(6):

64. Show Me!

65. Data types Categorical (C) Quantitative Categorical date (Cdate)
Quantitative dependent (measure) (Qd)
Quantitative independent (dimension) (Qi)

66. Chart types
Cross-table
Bar chart
Line chart
Scatter plot
Gantt chart

67. Visual variables
Text
Bar
Line
Shape
Gantt
Color

68. Rules for data encoding and chart selection

69. Ranking of charts Chart type Data elements Rank Text tables
At least one field
1 (lowest)
Aligned bars
At least 1 Q 2
Stacked bars
At least 2 C, at least 1 Q 3
Discrete lines
At least 1 Cdate, at least 1 Q 4
Scatter plots
Between 2 and 4 Q 5
Gantt charts
At least 1 C, at least 1 Qi, 1 to 2 Q
6 (highest)

70. Small multiple views
One of the uniqueness of Tableau’s “Show Me” feature is the ability to automatically generate multiple small views
Previous automatic visualization generation tools only generate a single chart or a sequence of charts

71. Small multiple views

72. Rules for generating small multiple views
Affinity
The affinity heuristic supports the generation of effective small multiple displays by adding fields next to related fields.

73. Rules for generating small multiple views
Rule for adding multiple categorical data
The heuristic for adding categorical fields to views that have multiple fields is to consider the following shelves in order: Shape, Color, and Level of Detail.
Rule for adding multiple quantitative data
Combine measures together

74. Rules for generating small multiple views
Rules for creating multiple views when user starts from empty view

75. Discussion
MacKinlay’s work laid the foundation for most of the subsequent works on automatic visualization generation
Focus on visualization expressiveness and perceptual efficiency
Casner’s system considers tasks in addition to data types
Still focused on perceptual efficiency

76. Discussion Salisbury’s program added a few new things
Attempt to account for cognitive efficiency in addition to perceptual efficiency
Conducted domain specific user studies to create a chart ranking system
Use cognitive task efficiency to select chart types
Visual mapping process is still largely based on MacKinlay’s framework

77. Discussion Tableau’s “Show Me” feature
Introduced additional rules for creating small multiple views

78. Critique Lacks domain specific data classification
Are the chart and visual variable rankings accurate?
Not based on rigorous testing
Still using Cleveland and McGill’s old experiments

79. Critique
Assumption: if each visual variable is perceptually a good fit for the corresponding data variable, then the overall chart is effective.
If the individual parts are good, the sum of them will be good too. Is this true?

80. Critique
Charts and visual variables are selected on a individual basis
Select the visual variable that best fits a particular data type
Never evaluate the automatically generated chart as a whole

81. Is this a good visualization?

82. Is this a good visualization?

83. Is this a good visualization?

84. Is this a good visualization?

85. Is this a good visualization?

86. Critique
No extensive user study to validate the effectiveness of these automatically generated visualizations

87. References
Mackinlay, J. (1986). "Automating the Design of Graphical Presentations of Relational Information." ACM Transactions on Graphics 5(2):
Casner, S. M. (1991). "A task-analytic approach to the automated design of graphic presentation." ACM Transactions on Graphics 10(2):
Salisbury, L. D. P. (2001). Automatic Visual Display Design and Creation. PhD Thesis, Department of Computer Science and Engineering, University of Washington.
Mackinlay, J. D., P. Hanrahan, et al. (2007). "Show Me: Automatic Presentation for Visual Analysis." IEEE Transactions on Visualization and Computer Graphics 13(6):