Interactive Data Visualization Foundations, Techniques, and Applications, Second Edition

An Updated Guide to the Visualization of Data for Designers, Users, and Researchers Interactive Data Visualization: Foundations, Techniques, and Applications, Second Edition provides all the theory, details, and tools necessary to build visualizations and systems involving the visualization of data. In color throughout, it explains basic terminology and concepts, algorithmic and software engineering issues, and commonly used techniques and high-level algorithms. Full source code is provided for completing implementations. New to the Second Edition New related readings, exercises, and programming projects Better quality figures and numerous new figures New chapter on techniques for time-oriented data This popular book continues to explore the fundamental components of the visualization process, from the data to the human viewer. For developers, the book offers guidance on designing effective visualizations using methods derived from human perception, graphical design, art, and usability analysis. For practitioners, it shows how various public and commercial visualization systems are used to solve specific problems in diverse domains. For researchers, the text describes emerging technology and hot topics in development at academic and industrial centers today. Each chapter presents several types of exercises, including review questions and problems that motivate readers to build on the material covered and design alternate approaches to solving a problem. In addition, programming projects encourage readers to perform a range of tasks, from the simple implementation of algorithms to the extension of algorithms and programming techniques. Web ResourceA supplementary website includes downloadable software tools and example data sets, enabling hands-on experience with the techniques covered in the text. The site also offers links to useful data repositories and data file formats, an up-to-date listing of software packages and vendors, and instructional tools, such as reading lists, lecture slides, and demonstration programs.

Introduction What Is Visualization? History of Visualization Relationship between Visualization and Other Fields The Visualization Process Pseudocode Conventions The Scatterplot The Role of the User Related Readings Exercises Projects Data Foundations Types of Data Structure within and between Records Data Preprocessing Data Sets Used in This Book Related Readings Exercises Projects Human Perception and Information Processing What Is Perception? Physiology Perceptual Processing Perception in Visualization Metrics Related Readings Exercises Projects Visualization Foundations The Visualization Process in Detail Semiology of Graphical Symbols The Eight Visual Variables Historical Perspective Taxonomies Related Readings Exercises Projects Visualization Techniques for Spatial Data One-Dimensional Data Two-Dimensional Data Three-Dimensional Data Dynamic Data Combining Techniques Summary Related Readings Exercises Projects Visualization Techniques for Geospatial Data Visualizing Spatial Data Visualization of Point Data Visualization of Line Data Visualization of Area Data Other Issues in Geospatial Data Visualization Related Readings Exercises Projects Visualization Techniques for Time-Oriented Data Introduction Definitions: Characterizing Time-Oriented Data Visualizing Time-Oriented Data TimeBench: A Data Model and Software Library for Visual Analytics of Time-Oriented Data Summary Related Readings Exercises Projects Visualization Techniques for Multivariate Data Point-Based Techniques Line-Based Techniques Region-Based Techniques Combinations of Techniques Related Readings Exercises Projects Visualization Techniques for Trees, Graphs, and Networks Displaying Hierarchical Structures Displaying Arbitrary Graphs/Networks Other Issues Related Readings Exercises Projects Text and Document Visualization Introduction Levels of Text Representations The Vector Space Model Single Document Visualizations Document Collection Visualizations Extended Text Visualizations Summary Related Readings Exercises Projects Interaction Concepts Interaction Operators Interaction Operands and Spaces A Unified Framework Summary Related Readings Exercises Projects Interaction Techniques Screen Space Object Space (D Surfaces) Data Space (Multivariate Data Values) Attribute Space (Properties of Graphical Entities) Data Structure Space (Components of Data Organization) Visualization Structure Space (Components of theData Visualization) Animating Transformations Interaction Control Related Readings Exercises Projects Designing Effective Visualizations Steps in Designing Visualizations Problems in Designing Effective Visualizations Summary Related Readings Exercises Projects Comparing and Evaluating Visualization Techniques User Tasks User Characteristics Data Characteristics Visualization Characteristics Structures for Evaluating Visualizations Benchmarking Procedures An Example of Visualization Benchmarking Related Readings Exercises Projects Visualization Systems Systems Based on Data Type Systems Based on Analysis Type Text Analysis and Visualization Modern Integrated Visualization Systems Toolkits Related Readings Exercises Projects Research Directions in VisualizationIssues of Data Issues of Cognition, Perception, and Reasoning Issues of System Design Issues of Evaluation Issues of Hardware Issues of Applications Related Readings Exercises Projects Appendix A: History of Computer Graphics and Visualization Appendix B: Example Data Sets Appendix C: Sample Programs Bibliography Index

Matthew O. Ward was a professor in the Department of Computer Science at Worcester Polytechnic Institute. Dr. Ward’s research focused on computer graphics, animation, image processing, computer vision, and data and information visualization. Georges Grinstein is a professor in the Department of Computer Science and director of both the Institute for Visualization and Perception Research and the Center for Biomolecular and Medical Informatics at the University of Massachusetts Lowell. Dr. Grinstein’s research encompasses visual analytics, human computing, perceptual computing, information computing, and visualization systems engineering. Daniel Keim is a professor in the Department of Computer and Information Science and head of the Data Analysis and Visualization group at the University of Konstanz. Dr. Keim’s research interests include databases, data mining, information visualization, and visual analytics.