Introduction to Scientific Visualization

Scientific visualization is recognised as important for understanding data, whether measured, sensed remotely or calculated. __Introduction to Scientific Visualization__ is aimed at readers who are new to the subject, either students taking an advanced option at undergraduate level or postgraduates wishing to visualize some specific data. An introductory chapter on the philosophy of the subject sets the scene, showing why visualization works and what to aim for when presenting scientific data visually. Then, extending and applying Brodlie's taxonomy to underpin its core chapters, the book shows how to classify data, choose a technique that's appropriate for its visualization, and alerts readers to some of the pitfalls before they can invalidate their conclusions. The book concludes with an annotated bibliography and suggestions for further reading. Above all, this is a 'how to do it' book for scientific visualization, with: * Discussion of the different types of software architecture for visualization * Introduction to the theory of colour perception and the role of colour in conveying information about data * Comprehensive illustrations throughout * Exercises and problems to encourage further learning * Virtual laboratories available from Springer’s website for those readers with access to the IRIS Explorer package Dr. Helen Wright is a Senior Lecturer in the Computer Science department at the University of Hull. She has many years experience teaching scientific visualization to undergraduates and postgraduates, and the material used in this book has been tested through successive taught courses.

scientific Visualization Is Recognised As Important For Understanding Data, Whether Measured, Sensed Remotely Or Calculated. Introduction To Scientific Visualization Is Aimed At Readers Who Are New To The Subject, Either Students Taking An Advanced Option At Undergraduate Level Or Postgraduates Wishing To Visualize Some Specific Data.

an Introductory Chapter On The Philosophy Of The Subject Sets The Scene, Showing Why Visualization Works And What To Aim For When Presenting Scientific Data Visually. Then, Extending And Applying Brodlie's Taxonomy To Underpin Its Core Chapters, The Book Shows How To Classify Data, Choose A Technique That's Appropriate For Its Visualization, And Alerts Readers To Some Of The Pitfalls Before They Can Invalidate Their Conclusions. The Book Concludes With An Annotated Bibliography And Suggestions For Further Reading.

above All, This Is A 'how To Do It' Book For Scientific Visualization, With:

• Discussion Of The Different Types Of Software Architecture For Visualization

• Introduction To The Theory Of Colour Perception And The Role Of Colour In Conveying Information About Data

• Comprehensive Illustrations Throughout

• Exercises And Problems To Encourage Further Learning

• Virtual Laboratories Available From Springer’s Website For Those Readers With Access To The Iris Explorer Package

dr. Helen Wright Is A Senior Lecturer In The Computer Science Department At The University Of Hull. She Has Many Years Experience Teaching Scientific Visualization To Undergraduates And Postgraduates, And The Material Used In This Book Has Been Tested Through Successive Taught Courses.

AboutThisBook This book was?rst suggested to Springer in 2004, though its origins go back to changes made two years earlier to the structure of the University of Hull's Computer Science programme. At the same time, my own visualization - search was leading towards a systematic view of data and techniques that I felt could be educationally valuable. In 2003 I thus sat down with some trepidation to write a visualization course founded on research in the area but nonetheless accessible to students. This course could, however, involve no mathematics beyond GCSE, in line with university admissions practices of that time. Writing the course involved generating many new illustrations, in the form of both line drawings and visualization screenshots and, wi- ing to get maximum mileage out of this e?ort, the idea of writing a book to accompany the course came about. At the University of Hull, our practical visualization teaching is based on IRIS Explorer, an application builder-type package from NAG Ltd. Ori- nally this book was to have been both an introduction to visualization and a handbook for beginners in IRIS Explorer, with ‘virtual laboratories'running throughout it to illustrate certain points. Following comments from revi- ers, however, its emphasis has changed. Scientific visualization is recognized as important for understanding data, whether measured, sensed remotely, or calculated. Introduction to Scientific Visualization is aimed at readers who are new to the subject, either students taking an advanced option at undergraduate level or postgraduates wishing to visualize some specific data. An introductory chapter on the philosophy of the subject sets the scene, showing why visualization works and what to aim for when presenting scientific data visually. Then, extending and applying Brodlie's taxonomy to underpin its core chapters, the book shows how to classify data, choose a technique that's appropriate for its visualization, and alerts readers to some of the pitfalls before they can invalidate their conclusions. The book concludes with an annotated bibliography and suggestions for further reading This Is A ‘how To’ Book For Scientific Visualization. The Book Does Not Treat The Subject As A Subset Of Information Visualisation, But Rather As A Subject In Its Own Right. An Introduction On The Philosophy Of The Subject Sets The Scene And The Theory Of Colour Perception Is Introduced. Next, Using Brodlie’s Taxonomy To Underpin Its Core Chapters, It Is Shown How To Classify Data. Worked Examples Are Given Throughout The Text And There Are Practical ‘sidebars’ For Readers With Access To The Iris Explorer Software Who Can Try Out The Demonstrations On An Accompanying Website. The Book Concludes With A ‘taster’ Of Ongoing Research.