Visualization Analysis and Design - Munzner, Tamara

. .

Résumé :
This book's unified approach encompasses information visualization techniques for abstract data, scientific visualization techniques for spatial data, and visual analytics techniques for interweaving data transformation and analysis with interactive visual exploration. Suitable for both beginners and more experienced designers, the book does not assume any experience with programming, mathematics, human-computer interaction, or graphic design.

Sommaire:
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.