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

Outline The value of information visualization Theoretical foundations of information visualization

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

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.

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

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.

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

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

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

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

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

Perceptual benefits Gestalt theory Law of closure Law of similarity Law of proximity Law of symmetry Law of continuity Law of common fate http://en.wikipedia.org/wiki/Gestalt_psychology

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

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

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.

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.

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.

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)

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.

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.

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

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

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

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

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

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

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

Case study

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

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.

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

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

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.

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

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. 46-64, 2008