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El. knyga: Eye Tracking and Visual Analytics

  • Formatas: 300 pages
  • Išleidimo metai: 01-Sep-2022
  • Leidėjas: River Publishers
  • Kalba: eng
  • ISBN-13: 9781000796728
Kitos knygos pagal šią temą:
  • Formatas: 300 pages
  • Išleidimo metai: 01-Sep-2022
  • Leidėjas: River Publishers
  • Kalba: eng
  • ISBN-13: 9781000796728
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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.

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.



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.

Preface xi
List of Figures
xiii
List of Tables
xxxi
List of Abbreviations
xxxiii
1 Introduction
1(16)
1.1 Tasks, Hypotheses, and Human Observers
3(4)
1.2 Synergy Effects
7(4)
1.3 Dynamic Visual Analytics
11(6)
2 Visualization
17(58)
2.1 Motivating Examples
19(8)
2.2 Historical Background
27(11)
2.2.1 Early Forms of Visualizations
28(2)
2.2.2 The Age of Cartographic Maps
30(2)
2.2.3 Visualization During Industrialization
32(2)
2.2.4 After the Invention of the Computer
34(2)
2.2.5 Visualization Today
36(2)
2.3 Data Types and Visual Encodings
38(15)
2.3.1 Primitive Data
39(3)
2.3.2 Complex Data
42(6)
2.3.3 Mixture of Data
48(2)
2.3.4 Dynamic Data
50(2)
2.3.5 Metadata
52(1)
2.4 Interaction Techniques
53(9)
2.4.1 Interaction Categories
54(4)
2.4.2 Physical Devices
58(3)
2.4.3 Users-in-the-Loop
61(1)
2.5 Design Principles
62(13)
2.5.1 Visual Enhancements and Decorations
63(2)
2.5.2 Visual Structuring and Organization
65(1)
2.5.3 General Design Flaws
66(2)
2.5.4 Gestalt Laws
68(3)
2.5.5 Optical Illusions
71(4)
3 Visual Analytics
75(50)
3.1 Key Concepts
77(14)
3.1.1 Origin and First Stages
78(1)
3.1.2 Data Handling and Management
79(7)
3.1.3 System Ingredients Around the Data
86(2)
3.1.4 Involved Research Fields and Future Perspectives
88(3)
3.2 Visual Analytics Pipeline
91(14)
3.2.1 Data Basis and Runtimes
91(2)
3.2.2 Patterns, Correlations, and Rules
93(4)
3.2.3 Tasks and Hypotheses
97(5)
3.2.4 Refinements and Adaptations
102(2)
3.2.5 Insights and Knowledge
104(1)
3.3 Challenges of Algorithmic Concepts
105(11)
3.3.1 Algorithm Classes
106(4)
3.3.2 Parameter Specifications
110(1)
3.3.3 Algorithmic Runtime Complexities
111(1)
3.3.4 Performance Evaluation
112(2)
3.3.5 Insights into the Running Algorithm
114(2)
3.4 Applications
116(9)
3.4.1 Dynamic Graphs
117(1)
3.4.2 Digital and Computational Pathology
118(1)
3.4.3 Malware Analysis
119(1)
3.4.4 Video Data Analysis
120(2)
3.4.5 Eye Movement Data
122(3)
4 User Evaluation
125(50)
4.1 Study Types
127(11)
4.1.1 Pilot vs. Real Study
128(1)
4.1.2 Quantitative vs. Qualitative
129(1)
4.1.3 Controlled vs. Uncontrolled
130(2)
4.1.4 Expert vs. Non-Expert
132(2)
4.1.5 Short-term vs. Longitudinal
134(1)
4.1.6 Limited-number Population vs. Crowdsourcing
135(1)
4.1.7 Field vs. Lab
136(2)
4.1.8 With vs. Without Eye Tracking
138(1)
4.2 Human Users
138(9)
4.2.1 Level of Expertise
139(2)
4.2.2 Age Groups
141(1)
4.2.3 Cultural Differences
142(2)
4.2.4 Vision Deficiencies
144(1)
4.2.5 Ethical Guidelines
145(2)
4.3 Study Design and Ingredients
147(11)
4.3.1 Hypotheses and Research Questions
148(1)
4.3.2 Visual Stimuli
149(2)
4.3.3 Tasks
151(2)
4.3.4 Independent and Dependent Variables
153(4)
4.3.5 Experimenter
157(1)
4.4 Statistical Evaluation and Visual Results
158(9)
4.4.1 Data Preparation and Descriptive Statistics
160(1)
4.4.2 Statistical Tests and Inferential Statistics
161(2)
4.4.3 Visual Representation of the Study Results
163(4)
4.5 Example User Studies Without Eye Tracking
167(8)
4.5.1 Hierarchy Visualization Studies
168(1)
4.5.2 Graph Visualization Studies
169(2)
4.5.3 Interaction Technique Studies
171(1)
4.5.4 Visual Analytics Studies
172(3)
5 Eye Tracking
175(54)
5.1 The Eye
177(8)
5.1.1 Eye Anatomy
178(1)
5.1.2 Eye Movement and Smooth Pursuit
179(2)
5.1.3 Disorders and Diseases Influencing Eye Tracking
181(2)
5.1.4 Corrected-to-Normal Vision
183(2)
5.2 Eye Tracking History
185(12)
5.2.1 The Early Days
186(2)
5.2.2 Progress in the Field
188(2)
5.2.3 Eye Tracking Today
190(2)
5.2.4 Companies, Technologies, and Devices
192(1)
5.2.5 Application Fields
192(5)
5.3 Eye Tracking Data Properties
197(12)
5.3.1 Visual Stimuli
199(3)
5.3.2 Gaze Points, Fixations, Saccades, and Scanpaths
202(2)
5.3.3 Areas of Interest (AOIs) and Transitions
204(2)
5.3.4 Physiological and Additional Measures
206(2)
5.3.5 Derived Metrics
208(1)
5.4 Examples of Eye Tracking Studies
209(20)
5.4.1 Eye Tracking for Static Visualizations
210(5)
5.4.2 Eye Tracking for Interaction Techniques
215(3)
5.4.3 Eye Tracking for Text/Label/Code Reading
218(3)
5.4.4 Eye Tracking for User Interfaces
221(2)
5.4.5 Eye Tracking for Visual Analytics
223(6)
6 Eye Tracking Data Analytics
229(38)
6.1 Data Preparation
230(7)
6.1.1 Data Collection and Acquisition
231(1)
6.1.2 Organization and Relevance
232(2)
6.1.3 Data Annotation and Anonymization
234(1)
6.1.4 Data Interpretation
235(1)
6.1.5 Data Linking
236(1)
6.2 Data Storage, Adaptation, and Transformation
237(6)
6.2.1 Datastorage
238(2)
6.2.2 Validation, Verification, and Cleaning
240(1)
6.2.3 Data Enhancement and Enrichment
241(1)
6.2.4 Data Transformation
242(1)
6.3 Algorithmic Analyses
243(11)
6.3.1 Ordering and Sorting
244(1)
6.3.2 Data Clustering
245(2)
6.3.3 Summarization, Classing, and Classification
247(1)
6.3.4 Normalization and Aggregation
248(1)
6.3.5 Projection and Dimensionality Reduction
249(1)
6.3.6 Correlation and Trend Analysis
250(2)
6.3.7 Pairwise or Multiple Sequence Alignment
252(1)
6.3.8 Artificial Intelligence-Related Approaches
253(1)
6.4 Visualization Techniques and Visual Analytics
254(13)
6.4.1 Statistical Plots
256(1)
6.4.2 Point-based Visualization Techniques
257(4)
6.4.3 AOI-based Visualization Techniques
261(2)
6.4.4 Eye Tracking Visual Analytics
263(4)
7 Open Challenges, Problems, and Difficulties
267(6)
7.1 Eye Tracking Challenges
267(2)
7.2 Eye Tracking Visual Analytics Challenges
269(4)
References 273(62)
Index 335(12)
About the Author 347
Michael Burch studied computer science and mathematics at the Saarland University in Saarbrücken, Germany. He received his PhD from the University of Trier in 2010 in the fields of information visualization and visual analytics. After 8 years of having been a PostDoc in the Visualization Research Center (VISUS) in Stuttgart, he moved to the Eindhoven University of Technology (TU/e) as an assistant professor for visual analytics. Michael Burch is in many international program committees and published more than 160 conference papers and journal articles in the field of visualization. His main interests are in information visualization, visual analytics, eye tracking, data science, and software engineering. Currently, he works as an associate professor at the University of Applied Sciences in Chur, Switzerland in the center for data analytics, visualization, and simulation (DAViS).