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.
A 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.
Table of Contents
What Is Visualization?
History of Visualization
Relationship between Visualization and Other Fields
The Visualization Process
The Role of the User
Types of Data
Structure within and between Records
Data Sets Used in This Book
Human Perception and Information Processing
What Is Perception?
Perception in Visualization
The Visualization Process in Detail
Semiology of Graphical Symbols
The Eight Visual Variables
Visualization Techniques for Spatial Data
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
Visualization Techniques for Time-Oriented Data
Definitions: Characterizing Time-Oriented Data
Visualizing Time-Oriented Data
TimeBench: A Data Model and Software Library for Visual Analytics of Time-Oriented Data
Visualization Techniques for Multivariate Data
Combinations of Techniques
Visualization Techniques for Trees, Graphs, and Networks
Displaying Hierarchical Structures
Displaying Arbitrary Graphs/Networks
Text and Document Visualization
Levels of Text Representations
The Vector Space Model
Single Document Visualizations
Document Collection Visualizations
Extended Text Visualizations
Interaction Operands and Spaces
A Unified Framework
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 the
Designing Effective Visualizations
Steps in Designing Visualizations
Problems in Designing Effective Visualizations
Comparing and Evaluating Visualization Techniques
Structures for Evaluating Visualizations
An Example of Visualization Benchmarking
Systems Based on Data Type
Systems Based on Analysis Type
Text Analysis and Visualization
Modern Integrated Visualization Systems
Research Directions in Visualization
Issues of Data
Issues of Cognition, Perception, and Reasoning
Issues of System Design
Issues of Evaluation
Issues of Hardware
Issues of Applications
Appendix A: History of Computer Graphics and Visualization
Appendix B: Example Data Sets
Appendix C: Sample Programs
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.
Praise for the First Edition:
A 2010 CHOICE Outstanding Academic Title
"College-level collections strong in concepts and theory surrounding data visualization will find Interactive Data Visualization: Foundations, Techniques, and Applications to be a powerful addition, covering all the details and tools needed for building visualizations around data. From math and statistical graphs to cartography and scientific displays, this offers plenty of details for creating visual displays of data, offering color illustrations throughout and plenty of refinement details."
—The Midwest Book Review, August 2011
"With chapters on elaborating on the importance of visualization, understanding the data without it, the relation to the human eyes and mind, what technology has brought in the avenues of displaying and interacting data, no concept is really left untouched. Enhanced with example data, samples, a history of computer graphics, and more, Interactive Data Visualization is a solid text to have on hand for any community or college library collection."
—James A. Cox, The Midwest Book Review, August 2010