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: Data Types / Color Schemes / Data Preconditioning / Visual Representations and Analysis / Summary
Author(s): Ross Maciejewski
Series: Synthesis Lectures on Visualization
Edition: 1
Publisher: Morgan & Claypool Publishers
Year: 2011
Language: English
Pages: 87
Tags: Математика;Теория вероятностей и математическая статистика;
Acknowledgments......Page 11
Data Types......Page 13
Ordinal Data......Page 15
Ratio Data......Page 16
Design Principles for Color Schemes......Page 17
Sequential Color Scales......Page 18
Mutlivariate Color Schemes......Page 19
Choosing a Color Scheme......Page 20
Data Preconditioning......Page 23
Determining Bin Widths......Page 29
Increasing the Dimensionality of a Histogram......Page 32
Kernel Density Estimation......Page 34
Multivariate Visualization Techniques......Page 37
Scatterplots and Scatterplot Matrices......Page 38
Parallel Coordinate Plots......Page 41
Abstract Multivariate Visualizations......Page 42
Principal Component Analysis......Page 44
K-Means Clustering......Page 46
Multi-dimensional Scaling......Page 48
Self-Organizing Maps......Page 49
Line Graphs......Page 51
Calendar View......Page 52
Multivariate Temporal Exploration......Page 53
Animation......Page 54
Control Charts......Page 55
Time Series Modeling......Page 57
Geographic Visualization......Page 61
Dasymetric Maps......Page 62
Isopleth Maps......Page 63
Class Interval Selection......Page 64
Animating Maps......Page 65
Spatial Autocorrelation......Page 66
Local Indicators of Spatial Association......Page 67
AMOEBA Clustering......Page 68
Spatial Scan Statistics......Page 69
Summary......Page 73
Bibliography......Page 75
Author's Biography......Page 87
