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Table of Contents
Front Cover
Eye Tracking and Visual Analytics
Contents
Preface
List of Figures
List of Tables
List of Abbreviations
1 Introduction
1.1 Tasks, Hypotheses, and Human Observers
1.2 Synergy Effects
1.3 Dynamic Visual Analytics
2 Visualization
2.1 Motivating Examples
2.2 Historical Background
2.2.1 Early Forms of Visualizations
2.2.2 The Age of Cartographic Maps
2.2.3 Visualization During Industrialization
2.2.4 After the Invention of the Computer
2.2.5 Visualization Today
2.3 Data Types and Visual Encodings
2.3.1 Primitive Data
2.3.2 Complex Data
2.3.3 Mixture of Data
2.3.4 Dynamic Data
2.3.5 Metadata
2.4 Interaction Techniques
2.4.1 Interaction Categories
2.4.2 Physical Devices
2.4.3 Users-in-the-Loop
2.5 Design Principles
2.5.1 Visual Enhancements and Decorations
2.5.2 Visual Structuring and Organization
2.5.3 General Design Flaws
2.5.4 Gestalt Laws
2.5.5 Optical Illusions
3 Visual Analytics
3.1 Key Concepts
3.1.1 Origin and First Stages
3.1.2 Data Handling and Management
3.1.3 System Ingredients Around the Data
3.1.4 Involved Research Fields and Future Perspectives
3.2 Visual Analytics Pipeline
3.2.1 Data Basis and Runtimes
3.2.2 Patterns, Correlations, and Rules
3.2.3 Tasks and Hypotheses
3.2.4 Refinements and Adaptations
3.2.5 Insights and Knowledge
3.3 Challenges of Algorithmic Concepts
3.3.1 Algorithm Classes
3.3.2 Parameter Specifications
3.3.3 Algorithmic Runtime Complexities
3.3.4 Performance Evaluation
3.3.5 Insights into the Running Algorithm
3.4 Applications
3.4.1 Dynamic Graphs
3.4.2 Digital and Computational Pathology
3.4.3 Malware Analysis
3.4.4 Video Data Analysis
3.4.5 Eye Movement Data
4 User Evaluation
4.1 Study Types
4.1.1 Pilot vs. Real Study.
4.1.2 Quantitative vs. Qualitative
4.1.3 Controlled vs. Uncontrolled
4.1.4 Expert vs. Non-Expert
4.1.5 Short-term vs. Longitudinal
4.1.6 Limited-number Population vs. Crowdsourcing
4.1.7 Field vs. Lab
4.1.8 With vs. Without Eye Tracking
4.2 Human Users
4.2.1 Level of Expertise
4.2.2 Age Groups
4.2.3 Cultural Differences
4.2.4 Vision Deficiencies
4.2.5 Ethical Guidelines
4.3 Study Design and Ingredients
4.3.1 Hypotheses and Research Questions
4.3.2 Visual Stimuli
4.3.3 Tasks
4.3.4 Independent and Dependent Variables
4.3.5 Experimenter
4.4 Statistical Evaluation and Visual Results
4.4.1 Data Preparation and Descriptive Statistics
4.4.2 Statistical Tests and Inferential Statistics
4.4.3 Visual Representation of the Study Results
4.5 Example User Studies Without Eye Tracking
4.5.1 Hierarchy Visualization Studies
4.5.2 Graph Visualization Studies
4.5.3 Interaction Technique Studies
4.5.4 Visual Analytics Studies
5 Eye Tracking
5.1 The Eye
5.1.1 Eye Anatomy
5.1.2 Eye Movement and Smooth Pursuit
5.1.3 Disorders and Diseases Influencing Eye Tracking
5.1.4 Corrected-to-Normal Vision
5.2 Eye Tracking History
5.2.1 The Early Days
5.2.2 Progress in the Field
5.2.3 Eye Tracking Today
5.2.4 Companies, Technologies, and Devices
5.2.5 Application Fields
5.3 Eye Tracking Data Properties
5.3.1 Visual Stimuli
5.3.2 Gaze Points, Fixations, Saccades, and Scanpaths
5.3.3 Areas of Interest (AOIs) and Transitions
5.3.4 Physiological and Additional Measures
5.3.5 Derived Metrics
5.4 Examples of Eye Tracking Studies
5.4.1 Eye Tracking for Static Visualizations
5.4.2 Eye Tracking for Interaction Techniques
5.4.3 Eye Tracking for Text/Label/Code Reading
5.4.4 Eye Tracking for User Interfaces.
5.4.5 Eye Tracking for Visual Analytics
6 Eye Tracking Data Analytics
6.1 Data Preparation
6.1.1 Data Collection and Acquisition
6.1.2 Organization and Relevance
6.1.3 Data Annotation and Anonymization
6.1.4 Data Interpretation
6.1.5 Data Linking
6.2 Data Storage, Adaptation, and Transformation
6.2.1 Data Storage
6.2.2 Validation, Verification, and Cleaning
6.2.3 Data Enhancement and Enrichment
6.2.4 Data Transformation
6.3 Algorithmic Analyses
6.3.1 Ordering and Sorting
6.3.2 Data Clustering
6.3.3 Summarization, Classing, and Classification
6.3.4 Normalization and Aggregation
6.3.5 Projection and Dimensionality Reduction
6.3.6 Correlation and Trend Analysis
6.3.7 Pairwise or Multiple Sequence Alignment
6.3.8 Artificial Intelligence-Related Approaches
6.4 Visualization Techniques and Visual Analytics
6.4.1 Statistical Plots
6.4.2 Point-based Visualization Techniques
6.4.3 AOI-based Visualization Techniques
6.4.4 Eye Tracking Visual Analytics
7 Open Challenges, Problems, and Difficulties
7.1 Eye Tracking Challenges
7.2 Eye Tracking Visual Analytics Challenges
References
Index
About the Author
Back Cover.
Eye Tracking and Visual Analytics
Contents
Preface
List of Figures
List of Tables
List of Abbreviations
1 Introduction
1.1 Tasks, Hypotheses, and Human Observers
1.2 Synergy Effects
1.3 Dynamic Visual Analytics
2 Visualization
2.1 Motivating Examples
2.2 Historical Background
2.2.1 Early Forms of Visualizations
2.2.2 The Age of Cartographic Maps
2.2.3 Visualization During Industrialization
2.2.4 After the Invention of the Computer
2.2.5 Visualization Today
2.3 Data Types and Visual Encodings
2.3.1 Primitive Data
2.3.2 Complex Data
2.3.3 Mixture of Data
2.3.4 Dynamic Data
2.3.5 Metadata
2.4 Interaction Techniques
2.4.1 Interaction Categories
2.4.2 Physical Devices
2.4.3 Users-in-the-Loop
2.5 Design Principles
2.5.1 Visual Enhancements and Decorations
2.5.2 Visual Structuring and Organization
2.5.3 General Design Flaws
2.5.4 Gestalt Laws
2.5.5 Optical Illusions
3 Visual Analytics
3.1 Key Concepts
3.1.1 Origin and First Stages
3.1.2 Data Handling and Management
3.1.3 System Ingredients Around the Data
3.1.4 Involved Research Fields and Future Perspectives
3.2 Visual Analytics Pipeline
3.2.1 Data Basis and Runtimes
3.2.2 Patterns, Correlations, and Rules
3.2.3 Tasks and Hypotheses
3.2.4 Refinements and Adaptations
3.2.5 Insights and Knowledge
3.3 Challenges of Algorithmic Concepts
3.3.1 Algorithm Classes
3.3.2 Parameter Specifications
3.3.3 Algorithmic Runtime Complexities
3.3.4 Performance Evaluation
3.3.5 Insights into the Running Algorithm
3.4 Applications
3.4.1 Dynamic Graphs
3.4.2 Digital and Computational Pathology
3.4.3 Malware Analysis
3.4.4 Video Data Analysis
3.4.5 Eye Movement Data
4 User Evaluation
4.1 Study Types
4.1.1 Pilot vs. Real Study.
4.1.2 Quantitative vs. Qualitative
4.1.3 Controlled vs. Uncontrolled
4.1.4 Expert vs. Non-Expert
4.1.5 Short-term vs. Longitudinal
4.1.6 Limited-number Population vs. Crowdsourcing
4.1.7 Field vs. Lab
4.1.8 With vs. Without Eye Tracking
4.2 Human Users
4.2.1 Level of Expertise
4.2.2 Age Groups
4.2.3 Cultural Differences
4.2.4 Vision Deficiencies
4.2.5 Ethical Guidelines
4.3 Study Design and Ingredients
4.3.1 Hypotheses and Research Questions
4.3.2 Visual Stimuli
4.3.3 Tasks
4.3.4 Independent and Dependent Variables
4.3.5 Experimenter
4.4 Statistical Evaluation and Visual Results
4.4.1 Data Preparation and Descriptive Statistics
4.4.2 Statistical Tests and Inferential Statistics
4.4.3 Visual Representation of the Study Results
4.5 Example User Studies Without Eye Tracking
4.5.1 Hierarchy Visualization Studies
4.5.2 Graph Visualization Studies
4.5.3 Interaction Technique Studies
4.5.4 Visual Analytics Studies
5 Eye Tracking
5.1 The Eye
5.1.1 Eye Anatomy
5.1.2 Eye Movement and Smooth Pursuit
5.1.3 Disorders and Diseases Influencing Eye Tracking
5.1.4 Corrected-to-Normal Vision
5.2 Eye Tracking History
5.2.1 The Early Days
5.2.2 Progress in the Field
5.2.3 Eye Tracking Today
5.2.4 Companies, Technologies, and Devices
5.2.5 Application Fields
5.3 Eye Tracking Data Properties
5.3.1 Visual Stimuli
5.3.2 Gaze Points, Fixations, Saccades, and Scanpaths
5.3.3 Areas of Interest (AOIs) and Transitions
5.3.4 Physiological and Additional Measures
5.3.5 Derived Metrics
5.4 Examples of Eye Tracking Studies
5.4.1 Eye Tracking for Static Visualizations
5.4.2 Eye Tracking for Interaction Techniques
5.4.3 Eye Tracking for Text/Label/Code Reading
5.4.4 Eye Tracking for User Interfaces.
5.4.5 Eye Tracking for Visual Analytics
6 Eye Tracking Data Analytics
6.1 Data Preparation
6.1.1 Data Collection and Acquisition
6.1.2 Organization and Relevance
6.1.3 Data Annotation and Anonymization
6.1.4 Data Interpretation
6.1.5 Data Linking
6.2 Data Storage, Adaptation, and Transformation
6.2.1 Data Storage
6.2.2 Validation, Verification, and Cleaning
6.2.3 Data Enhancement and Enrichment
6.2.4 Data Transformation
6.3 Algorithmic Analyses
6.3.1 Ordering and Sorting
6.3.2 Data Clustering
6.3.3 Summarization, Classing, and Classification
6.3.4 Normalization and Aggregation
6.3.5 Projection and Dimensionality Reduction
6.3.6 Correlation and Trend Analysis
6.3.7 Pairwise or Multiple Sequence Alignment
6.3.8 Artificial Intelligence-Related Approaches
6.4 Visualization Techniques and Visual Analytics
6.4.1 Statistical Plots
6.4.2 Point-based Visualization Techniques
6.4.3 AOI-based Visualization Techniques
6.4.4 Eye Tracking Visual Analytics
7 Open Challenges, Problems, and Difficulties
7.1 Eye Tracking Challenges
7.2 Eye Tracking Visual Analytics Challenges
References
Index
About the Author
Back Cover.