1. CSc4730/6730 Scientific Visualization
Lecture 23
Theoretical Basis of
Information Visualization
Ying Zhu
Georgia State University

2. Outline The value of information visualization
Theoretical foundations of information visualization

3. When is visualization useful?
When a person has a specific question, data visualization is often not the best tools to use.
Here the following methods are perhaps more effective
Database query
Search
Statistical methods
Data mining

4. When is visualization useful?
Visualization is often useful when a person simply does not know what questions to ask about the data
Bottom up visual search
Visualization can help people to rapidly narrow in from a large space and find parts of the data to study more carefully.
Data visualization is most useful for exploratory tasks.

5. When is visualization useful?
Exploratory tasks:
When there is a good underlying structure
When users are unfamiliar with a data set
When users have limited understanding of how a system is organized
When users have difficulty verbalizing the underlying information need
When information is easier to recognize than describe

6. Measuring the effectiveness
However, it’s not easy to measure the effectiveness of activities such as exploration, browsing, gaining insight, etc.
Therefore, it’s a challenge to for identifying and measuring the value of data visualization.

7. Benefits of data visualization
Cognitive benefits
Data visualization can serve as a external memory for human cognitive process
External cognitive aids
visualization can support more
Improve task performance by allowing substitution of rapid perceptual inferences for difficult logical inferences and by reducing the search for information

8. Cognitive benefits
It is vital to match the representation used in a visualization to the task it is addressing

9. Definition of “data visualization”
Data visualization is the use of computer-supported, interactive visual representations of data to amplify cognition.

10. How can visualization amplify cognition?
Increasing memory and processing resources available
Reducing search for information
Enhancing the recognition of patterns
Enabling perceptual inference operations
Using perceptual attention mechanisms for monitoring
Encoding info in a manipulable medium

11. Perceptual benefits
Preattentive processing theory: some visual features can be perceived very rapidly and accurately by our low-level visual system.

12. Perceptual benefits Gestalt theory
Law of closure
Law of similarity
Law of proximity
Law of symmetry
Law of continuity
Law of common fate

13. Perceptual benefits
Information Visualization experts design visual representations that try to follow these principles.
Radial graph: law of proximity
Tree map: law of closure

14. Data visualization vs. statistics and data mining
What data visualization can do that statistics and data mining cannot?

15. Visualization vs. statistics
Descriptive statistics
Classical statistics
Bayesian statistics
Exploratory data analysis
Information Visualization is sometimes considered as a descendant and expansion of Exploratory Data Analysis.

16. Visualization vs. statistics
Exploratory Data Analysis performs an analysis using visual methods to acquire insights of what the data looks like, usually to find a model.
It uses visual exploration methods to get the insights.

17. Visualization vs. statistics
So why is visualization useful before the modeling?
Because, there are cases when we have no clear idea on the nature of the data and have no model.
In some cases, visualization is much more effective at showing the differences between these datasets than statistics.

18. Visualization vs. data mining
The goal of data mining is to automatically find interesting facts in large datasets.
In many cases, human vision system is far better than automatic data mining methods to spot interesting patterns.
See Fekete, et al. (Figure 7)

19. Data visualization vs. statistics and data mining
When a model is known in advance or expected, using statistics is the right method.
When a dataset becomes too large to be visualized directly, automating some analysis is required.

20. Data visualization vs. statistics and data mining
When exploring a dataset in search of insights, information visualization should be used, possibly in conjunction with data mining techniques if the dataset is too large.

21. Theories for visualization
Three theories for data visualization
Predictive Data-Centered Theory
Information Theory
Formal models
Visualization Exploration Model
Visualization Transform Design Model

22. Predictive Data-Centered Theory
Basic ideas
Develop a generic framework for categorizing data types and structures
Develop a general typology of patterns
Derive possible patterns from given data types and structures
For a given data set, develop/customize data visualization tools for detecting the type of patterns specific to that data set

23. Predictive Data-Centered Theory
Derive possible patterns from given data types and structures
Develop a pattern-by-data typology

24. Information theory
Measuring information content in a data visualization design
Quantify the volume of information and then measure how much of this volume a visualization technique is capable of effectively conveying.
However, there are too many possibilities to consider them all
Perhaps we need a different approach

25. Measuring information loss
Perhaps it is easier to measure loss of information (entropy) during the visualization process than the total information content of a dataset

26. Measuring information loss
Examples:
Measure the distance between data points in multidimentional data sets during dimension reduction
Measure information lost during aggregation, sampling, clustering, etc.
Measure information lost during Focus + Context techniques
Hardware and perceptual limitations

27. Measuring information content
Counting the number of data points
Tufte’s data-ink ratio
Counting the number of features or patterns in a given time period
Counting the number of clusters and outliers found by users
Counting insights discovered

28. Case study

29. Case study Parallel coordinates Information lost due to occlusion
Only show pairwise relationships: (N-1) out of N*(N-1)/2 possible relations

30. Formal models Visual exploration model
A description of the visual information search process and how it affects human cognition
second, a model of how the visualization session evolves due to human interaction.
An exploration model describes and predicts a human's interaction with a visualization system based upon its design.

31. Visual exploration model
An understanding of how humans process visual cues in order to make exploration decisions can inform visualization design.

32. Visual Transform Model
A visualization transform is the function that computes the depicted result from visualization parameters
This model describes the components that compose the design and provide initial design guidance

33. Visual Transform Model
Benefits of a visualization transform model
Providing guidance on suitable, less suitable, and unsuitable visual component choices.
Visualization pedagogy will improve due to a validated foundation for techniques.
Further, formal models will lead to objective metrics for evaluating a transform's effectiveness.

34. Summary Data visualization needs a solid theoretical foundation
Much work needs to be done to further develop theories and principles of data visualization

35. References
J-D Fekete, et al. “The Value of Information Visualization,” in A. Kerren et al. (Eds.): Information Visualization, Lecture Notes in Computer Science, Vol. 4950, pp. 1-18, 2008
H. C. Purchase, et al. “Theoretical Foundations of Information Visualization,” in A. Kerren et al. (Eds.): Information Visualization, Lecture Notes in Computer Science, Vol. 4950, pp , 2008