Data representations, transformations, and statistics for visual reasoning
By: Ross Maciejewski
Series: Synthesis lectures on visualization -- # 2
Publisher: Morgan & Claypool
Published: 2011
Place of publication: San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA)
Language: eng
Format: [electronic resource] /
ISBN-13: 9781608456260; 9781608456253
Subjects: Information visualization; Visual analytics
Other titles: Synthesis digital library of engineering and computer science.
Description: Analytical reasoning techniques are methods by which users explore their data to obtain insight and knowledge that can directly support situational awareness and decision making. Recently, the analytical reasoning process has been augmented through the use of interactive visual representations and tools which utilize cognitive, design and perceptual principles. These tools are commonly referred to as visual analytics tools, and the underlying methods and principles have roots in a variety of disciplines. This chapter provides an introduction to young researchers as an overview of common visual representations and statistical analysis methods utilized in a variety of visual analytics systems. The application and design of visualization and analytical algorithms are subject to design decisions, parameter choices, and many conflicting requirements. As such, this chapter attempts to provide an initial set of guidelines for the creation of the visual representation, including pitfalls and areas where the graphics can be enhanced through interactive exploration. Basic analytical methods are explored as a means of enhancing the visual analysis process, moving from visual analysis to visual analytics.
Notes: Part of: Synthesis digital library of engineering and computer science.

Series from website.

Includes bibliographical references (p. 63-74).

Abstract freely available; full-text restricted to subscribers or individual document purchasers.

Also available in print.

Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader.
Table of contents: title: 1. Datatypes; title: Data types; title: Nominal data; title: Ordinal data; title: Interval data; title: Ratio data; title: 2. Color schemes; title: Design principles for color schemes; title: Univariate color schemes; title: Qualitative color scales; title: Sequential color scales; title: Divergent color scales; title: Multivariate color schemes; title: Choosing a color scheme; title: 3. Data preconditioning; title: 4. Visual representations and analysis; title: 4.1. Histograms; title: Determining bin widths; title: Increasing the dimensionality of a histogram; title: 4.2. Kernel density estimation; title: 4.3. Multivariate visualization techniques; title: Scatterplots and scatterplot matrices; title: Parallel coordinate plots; title: Parallel sets; title: Abstract multivariate visualizations; title: 4.4. Multivariate analysis; title: Principal component analysis; title: K-means clustering; title: Multi-dimensional scaling; title: Self-organizing maps; title: 4.5. Time series visualization; title: Line graphs; title: Cyclical time; title: Calendar view; title: Multivariate temporal exploration; title: Animation; title: 4.6. Temporal modeling and anomaly detection; title: Control charts; title: Time series modeling; title: 4.7. Geographic visualization; title: Choropleth maps; title: Dasymetric maps; title: Isopleth maps; title: Class interval selection; title: Interactive maps; title: Animating maps; title: 4.8. Spatial anomaly detection; title: Spatial autocorrelation; title: Local indicators of spatial association; title: AMOEBA clustering; title: Spatial scan statistics; title: 5. Summary; title: Bibliography; title: Author's biography.