Data Visualization
Alexandru Cristian Telea
Abstract
Alexandru Cristian Telea
Abstract
The goal of data visualization is to use images to improve our understanding of a dataset, drawing on techniques from mathematics, computer science, cognitive and perception science, and physics. In this introductory text, the author provides a compact introduction to the field that allows readers to learn about visualization techniques. The material focuses on those techniques and methods that have a broad applicability in visualization applications, occur in most practical problems in various guises, and do not demand a specialized background to be understood. However, the author has also included a number of less mainstream visualization techniques. With these methods, the book gives the reader an idea of the large variety of applications of data visualizations, illustrates the wide 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.
OpenAlex reports 9 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
The goal of data visualization is to use images to improve our understanding of a dataset, drawing on techniques from mathematics, computer science, cognitive and perception science, and physics. In this introductory text, the author provides a compact introduction to the field that allows readers to learn about visualization techniques. The material focuses on those techniques and methods that have a broad applicability in visualization applications, occur in most practical problems in various guises, and do not demand a specialized background to be understood. However, the author has also included a number of less mainstream visualization techniques. With these methods, the book gives the reader an idea of the large variety of applications of data visualizations, illustrates the wide 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.
Key concepts: Visualization, Computer science, Scientific visualization, Variety (cybernetics), Data science, Field (mathematics), Creative visualization, Computer graphics