Eye Tracking and Visual Analytics

Description

Visualization and visual analytics are powerful concepts for exploring data from various application domains. The endless number of possible parameters and the many ways to combine visual variables as well as algorithms and interaction techniques create lots of possibilities for building such techniques and tools. The major goal of those tools is to include the human users with their tasks at hand, their hypotheses, and research questions to provide ways to find solutions to their problems or at least to hint them in a certain direction to come closer to a problem solution. However, due to the sheer number of design variations, it is unclear which technique is suitable for those tasks at hand, requiring some kind of user evaluation to figure out how the human users perform while solving their tasks. The technology of eye tracking has existed for a long time; however, it has only recently been applied to visualization and visual analytics as a means to provide insights to the users' visual attention behavior. This generates another kind of dataset that has a spatio-temporal nature and hence demands for advanced data science and visual analytics concepts to find insights into the recorded eye movement data, either as a post process or even in real-time. This book describes aspects from the interdisciplinary field of visual analytics, but also discusses more general approaches from the field of visualization as well as algorithms and data handling. A major part of the book covers research on those aspects under the light and perspective of eye tracking, building synergy effects between both fields -- eye tracking and visual analytics -- in both directions, i.e. eye tracking applied to visual analytics and visual analytics applied to eye tracking data. Technical topics discussed in the book include: Visualization;Visual Analytics;User Evaluation;Eye Tracking;Eye Tracking Data Analytics;Eye Tracking and Visual Analytics includes more than 500 references from the fields of visualization, visual analytics, user evaluation, eye tracking, and data science, all fields which have their roots in computer science. Eye Tracking and Visual Analytics is written for researchers in both academia and industry, particularly newcomers starting their PhD, but also for PostDocs and professionals with a longer research history in one or more of the covered research fields. Moreover, it can be used to get an overview about one or more of the involved fields and to understand the interface and synergy effects between all of those fields. The book might even be used for teaching lectures in the fields of information visualization, visual analytics, and/or eye tracking. 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