Designing a complete visualization system involves many subtle decisions. When designing a complex, real-world visualization system, such decisions involve many types of constraints, such as performance, platform (in)dependence, available programming languages and styles, user-interface toolkits, input/output data format constraints, integration with third-party code, and more. Focusing on those techniques and methods with the broadest applicability across fields, the second edition of Data Visualization: Principles and Practice provides a streamlined introduction to various visualization techniques. The book illustrates a wide variety of applications of data visualizations, illustrating the range of problems that can be tackled by such methods, and emphasizes the strong connections between visualization and related disciplines such as imaging and computer graphics. It covers a wide range of sub-topics in data visualization: data representation; visualization of scalar, vector, tensor, and volumetric data; image processing and domain modeling techniques; and information visualization. See What’s New in the Second Edition: Additional visualization algorithms and techniques New examples of combined techniques for diffusion tensor imaging (DTI) visualization, illustrative fiber track rendering, and fiber bundling techniques Additional techniques for point-cloud reconstruction Additional advanced image segmentation algorithms Several important software systems and libraries Algorithmic and software design issues are illustrated throughout by (pseudo)code fragments written in the C++ programming language. Exercises covering the topics discussed in the book, as well as datasets and source code, are also provided as additional online resources.
This practical book takes you through many commonly encountered visualization problems, and it provides guidelines on how to turn large datasets into clear and compelling figures.
An accessible primer on how to create effective graphics from data This book provides students and researchers a hands-on introduction to the principles and practice of data visualization.
In nine appealing chapters, the book: examines the role of data graphics in decision-making, sharing information, sparking discussions, and inspiring future research; scrutinizes data graphics, deliberates on the messages they convey, and ...
Numbers in each cell represent the strength of that relationship, often as a Pearson's correlation coefficient (see Box on page 254). The correlation matrix graph uses the same layout but instead of numbers it uses shapes—often ...
An accessible primer on how to create effective graphics from data This book provides students and researchers a hands-on introduction to the principles and practice of data visualization.
Delivered in Evergreen’s humorous and approachable style, the book covers the spectrum of graph types available beyond the default options, how to determine which one most appropriately fits specific data stories, and easy steps for ...
Visualize This is a guide on how to visualize and tell stories with data, providing practical design tips complemented with step-by-step tutorials.
This book is for anyone who has responsibility for, or is interested in trying to find innovative and effective ways to visually analyze and communicate data.
“StructureBased Brushes: A Mechanism for Navigating Hierarchically Organized Data and Information Spaces.” IEEE Trans. Visualization and Computer Graphics 6:2 (2000), 150–159. [139] Anton Fuhrmann and Eduard Gröller.
Author Scott Murray teaches you the fundamental concepts and methods of D3, a JavaScript library that lets you express data visually in a web browser.