Munzner Visualization Analysis and Design

Erscheinungsjahr 2014
ISBN: 978-1-4665-0893-4
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 428 Seiten

Reihe: AK Peters Visualization Series

Learn How to Design Effective Visualization Systems

Visualization Analysis and Design provides a systematic, comprehensive framework for thinking about visualization in terms of principles and design choices. The book features a unified approach encompassing 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. It emphasizes the careful validation of effectiveness and the consideration of function before form.

The book breaks down visualization design according to three questions: what data users need to see, why users need to carry out their tasks, and how the visual representations proposed can be constructed and manipulated. It walks readers through the use of space and color to visually encode data in a view, the trade-offs between changing a single view and using multiple linked views, and the ways to reduce the amount of data shown in each view. The book concludes with six case studies analyzed in detail with the full framework.

The book is suitable for a broad set of readers, from beginners to more experienced visualization designers. It does not assume any previous experience in programming, mathematics, human–computer interaction, or graphic design and can be used in an introductory visualization course at the graduate or undergraduate level.

Zielgruppe

Computer scientists and researchers who work with visualization techniques and systems; graduate students in visualization.

Autoren/Hrsg.

Munzner, Tamara

Fachgebiete

• Mathematik | Informatik EDV | Informatik Professionelle Anwendung

Inhaltsverzeichnis

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.