Eye Tracking and Visual Analytics:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Aalborg
River Publishers
2021
|
| Ausgabe: | 1st ed |
| Schlagworte: | |
| Online-Zugang: | KUBA1 |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 Online-Ressource (382 pages) |
| ISBN: | 9788770224321 |
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| 245 | 1 | 0 | |a Eye Tracking and Visual Analytics |
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| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 650 | 4 | |a Visual analytics | |
| 650 | 4 | |a Information visualization | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |a Burch, Michael |t Eye Tracking and Visual Analytics |d Aalborg : River Publishers,c2021 |
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Datensatz im Suchindex
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| 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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| illustrated | Not Illustrated |
| index_date | 2024-07-03T23:28:34Z |
| indexdate | 2024-07-10T10:10:43Z |
| institution | BVB |
| isbn | 9788770224321 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034906251 |
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| physical | 1 Online-Ressource (382 pages) |
| psigel | ZDB-30-PAD KUBA1-ZDB-30-PAD-2023 ZDB-30-PAD KHI |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | River Publishers |
| record_format | marc |
| spelling | Burch, Michael Verfasser aut Eye Tracking and Visual Analytics 1st ed Aalborg River Publishers 2021 ©2021 1 Online-Ressource (382 pages) txt rdacontent c rdamedia cr rdacarrier Description based on publisher supplied metadata and other sources 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 Visual analytics Information visualization Erscheint auch als Druck-Ausgabe Burch, Michael Eye Tracking and Visual Analytics Aalborg : River Publishers,c2021 |
| spellingShingle | Burch, Michael Eye Tracking and Visual Analytics 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 Visual analytics Information visualization |
| title | Eye Tracking and Visual Analytics |
| title_auth | Eye Tracking and Visual Analytics |
| title_exact_search | Eye Tracking and Visual Analytics |
| title_exact_search_txtP | Eye Tracking and Visual Analytics |
| title_full | Eye Tracking and Visual Analytics |
| title_fullStr | Eye Tracking and Visual Analytics |
| title_full_unstemmed | Eye Tracking and Visual Analytics |
| title_short | Eye Tracking and Visual Analytics |
| title_sort | eye tracking and visual analytics |
| topic | Visual analytics Information visualization |
| topic_facet | Visual analytics Information visualization |
| work_keys_str_mv | AT burchmichael eyetrackingandvisualanalytics |