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Data representations, transformations, and statistics for visual reasoning 1 edition

Data representations, transformations, and statistics for visual reaso ...
Ross Maciejewski

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Data representations, transformations, and statistics for visual reasoning
Ross Maciejewski

Published 2011 by Morgan & Claypool in San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) .
Written in English.

About the Book

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.

Table of Contents

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

Edition 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.

Series
Synthesis lectures on visualization -- # 2
Other Titles
Synthesis digital library of engineering and computer science.

Classifications

Dewey Decimal Class
006.6
Library of Congress
TK7882.I6 M233 2011

The Physical Object

Format
[electronic resource] /

ID Numbers

Open Library
OL25564309M
Internet Archive
datarepresentati00maci
ISBN 13
9781608456260, 9781608456253

History Created July 29, 2014 · 1 revision Download catalog record: RDF / JSON

July 29, 2014 Created by ImportBot import new book