Eye Tracking and Visual Analytics.

Eye Tracking and Visual Analytics.

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Bibliographic Details
:
Burch, Michael.
Place / Publishing House:Aalborg : : River Publishers,, 2021.
Ã2021.
Year of Publication:2021
Edition:1st ed.
Language:English
Online Access:
Physical Description:1 online resource (382 pages)
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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.

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