Data Representations, Transformations, and Statistics for Visual Reasoning
eBook - PDF

Data Representations, Transformations, and Statistics for Visual Reasoning

  1. English
  2. PDF
  3. Available on iOS & Android
eBook - PDF

Data Representations, Transformations, and Statistics for Visual Reasoning

Book details
Table of contents
Citations

About This 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: Data Types / Color Schemes / Data Preconditioning / Visual Representations and Analysis / Summary

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Yes, you can access Data Representations, Transformations, and Statistics for Visual Reasoning by Ross Maciejewski in PDF and/or ePUB format, as well as other popular books in Matemáticas & Matemáticas general. We have over one million books available in our catalogue for you to explore.

Information

Publisher
Springer
Year
2022
ISBN
9783031025990

Table of contents

  1. Cover
  2. Copyright Page
  3. Title Page
  4. Contents
  5. Acknowledgments
  6. 1. Data Types
  7. 2. Color Schemes
  8. 3. Data Preconditioning
  9. 4. Visual Representations and Analysis
  10. 5. Summary
  11. Bibliography
  12. Author’s Biography