Visualization Analysis and Design

Tamar Munzner

Publisher: CRC Press Publication Date: 2015 Number of Pages: 404 Format: Hardcover Series: A K Peters Visualization Series Price: 79.95 ISBN: 9781466508910 Category: Monograph ●MAA Review ●Table of Contents [Reviewed by William J. Satzer , on 03/17/2015 ] This is a book about visualization, and specifically the presentation of data in visual form. One can think of visualization as a kind of transformation that takes information in a form suitable for computation into forms more compatible with human perception and cognition and more easily communicated. The current book is an introduction to the subject. It is aimed at graduate students and advanced undergraduates in computer science, but it would be accessible to a much broader audience. Visualization is a young field with only a few primary texts available. What distinguishes this one is the way it synthesizes past work to develop a comprehensive framework for design and analysis. Edward Tufte’s books, especially Envisioning Information, were eye-opening to many of us involved in preparing data for analysis, talks, reports and papers. But Tufte concentrated on static design presentations and there are now many new possibilities for interactive and dynamic graphical data analysis and presentation. The author builds a framework for understanding the key elements of visualization and develops a synthesis of current best practices. She breaks down the framework for analysis into three steps: what, why and how. “What” addresses the kind of data that is to be visualized; “why” identifies the purposes for creating the visualization; “how” describes the method by which it is carried out. In this book, the “what” includes three generic data types: tables, spatial data, and networks. The table category includes scatterplots, bar charts, dot and line charts, heat maps and pie charts. Maps, vector, tensor and flow fields are spatial data. Networks include tree structures and adjacency matrix views. The first part of the book addresses the what, why and how questions in detail and then considers the validation question: how well does a particular visualization actually work? This leads naturally into a discussion of visual channels; these are the means of controlling the basic graphical elements in an image. Channels include spatial position, length, angle, area, depth, color, curvature, volume, shape, and motion. Matching the channel to the human perceptual system is a key to effective visualization design. As a simple example, think of pie charts and bar charts. Pie charts require both angle and area judgments, which are perceptually harder and less accurate than bar charts that require only judgments of length on a common scale. One of the most useful chapters to a casual reader is on rules of thumb for visualization. These include: don’t use 3D when 2D will do, and don’t use 2D when a one-dimensional list will do. Using our eyes to switch between two views that are visible simultaneously has a much lower cognitive load than relying on memory to compare a current view with one seen before… There are about half a dozen similar rules presented in the book. They are perceptive and clearly based on years of experience. The book is filled with examples from the universe of visualization. Virtually all the possibilities for visualization design are illustrated with specific examples. It’s also worth pointing out what’s not in the book. The author intentionally takes a top-down look at visualization. This does not extend all the way to the level of algorithms. As the author notes, the book is already pretty long and would need to double in size to incorporate an adequate treatment of algorithms. On a more fundamental level, the book is about visualization of data, not about visualization in the broader sense that mathematicians sometimes use. So there is no visualization of the hypercube here, no eversion of the sphere, not anything like that. This is an attractive book, one that’s likely to be a fundamental source for the field. It’s worth a look for anyone with even a passing interest.

Bill Satzer (wjsatzer@mmm.com) is a senior intellectual property scientist at 3M Company, having previously been a lab manager at 3M for composites and electromagnetic materials. His training is in dynamical systems and particularly celestial mechanics; his current interests are broadly in applied mathematics and the teaching of mathematics. What's Vis, and Why Do It? The Big Picture Why Have A Human in the Loop? Why Have A Computer in the Loop? Why Use An External Representation? Why Depend on Vision? Why Show The Data In Detail? Why Use Interactivity? Why Is the Vis Idiom Design Space Huge? Why Focus on Tasks? Why Focus on Effectiveness? Why Are Most Designs Ineffective? Why Is Validation Difficult? Why Are There Resource Limitations? Why Analyze? What: Data Abstraction The Big Picture Why Do Data Semantics and Types Matter? Data Types Dataset Types Attribute Types Semantics Why: Task Abstraction The Big Picture Why Analyze Tasks Abstractly? Who: Designer or User Actions Targets How: A Preview Analyzing and Deriving: Examples Analysis: Four Levels for Validation The Big Picture Why Validate? Four Levels of Design Angles of Attack Threats and Validation Approaches Validation Examples Marks and Channels The Big Picture Why Marks and Channels? Defining Marks and Channels Using Marks and Channels Channel Effectiveness Relative vs. Absolute Judgments Rules of Thumb The Big Picture Why and When to Follow Rules of Thumb? No Unjustified 3D No Unjustified 2D Eyes Beat Memory Resolution over Immersion Overview First, Zoom and Filter, Details on Demand Responsiveness Is Required Get It Right in Black and White Function First, Form Next Arrange Tables The Big Picture Why Arrange? Classifying Arrangements by Keys and Values Express: Quantitative Values Separate, Order, and Align: Categorical Regions Spatial Axis Orientation Spatial Layout Density Arrange Spatial Data The Big Picture Why Use Given? Geometry Scalar Fields: 1 Value Vector Fields: Multiple Values Tensor Fields: Many Values Arrange Networks and Trees The Big Picture Connection: Link Marks Matrix Views Costs and Benefits: Connection vs. Matrix Containment: Hierarchy Map Color and Other Channels The Big Picture Color Theory Colormaps Other Channels Manipulate View The Big Picture Why Change? Change View over Time Select Elements Navigate: Changing Viewpoint Navigate: Reducing Attributes Facet into Multiple Views The Big Picture Why Facet? Juxtapose and Coordinate Views Partition into Views Superimpose Layers Reduce Items and Attributes The Big Picture Why Reduce? Filter Aggregate Embed: Focus+Context The Big Picture Why Embed? Elide Superimpose Distort Costs and Benefits: Distortion Analysis Case Studies Graph-Theoretic Scagnostics VisDB Hierarchical Clustering Explorer PivotGraph InterRing Constellation Bibliography Further Reading appears at the end of each chapter. Tags: Data Analysis Visualization ●Log in to post comments

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Visualization Analysis and Design

Tamar Munzner

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