Data Representations, Transformations, and Statistics for Visual Reasoning (Synthesis Lectures on Visualization)

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

Ross Maciejewski received his PhD in 2009 from Purdue University for his thesis "Exploring Multivariate Data through the Application of Visual Analytics." Currently, he is a visiting assistant professor at Purdue University working as a member of the visual analytics for command, control, and interoperability environments Department of Homeland Security Center of Excellence. His research interests include visual analytics, illustrative visualization, volume rendering, non-photorealistic rendering and geovisualization.