Graphics of Large Datasets: Visualizing a Million (Statistics and Computing)

This book shows how to look at ways of visualizing large datasets, whether large in numbers of cases, or large in numbers of variables, or large in both. All ideas are illustrated with displays from analyses of real datasets and the importance of interpreting displays effectively is emphasized. Graphics should be drawn to convey information and the book includes many insightful examples. New approaches to graphics are needed to visualize the information in large datasets and most of the innovations described in this book are developments of standard graphics. The book is accessible to readers with some experience of drawing statistical graphics. Graphics are great for exploring data, but how can they be used for looking at the large datasets that are commonplace to-day? This book shows how to look at ways of visualizing large datasets, whether large in numbers of cases or large in numbers of variables or large in both. Data visualization is useful for data cleaning, exploring data, identifying trends and clusters, spotting local patterns, evaluating modeling output, and presenting results. It is essential for exploratory data analysis and data mining. Data analysts, statisticians, computer scientists-indeed anyone who has to explore a large dataset of their own-should benefit from reading this book. New approaches to graphics are needed to visualize the information in large datasets and most of the innovations described in this book are developments of standard graphics. There are considerable advantages in extending displays which are well-known and well-tried, both in understanding how best to make use of them in your work and in presenting results to others. It should also make the book readily accessible for readers who already have a little experience of drawing statistical graphics. All ideas are illustrated with displays from analyses of real datasets and the authors emphasize the importance of interpreting displays effectively. Graphics should be drawn to convey information and the book includes many insightful examples. From the reviews:'Anyone interested in modern techniques for visualizing data will be well rewarded by reading this book. There is a wealth of important plotting types and techniques.'Paul Murrell for the Journal of Statistical Software, December 2006'This fascinating book looks at the question of visualizing large datasets from many different perspectives. Different authors are responsible for different chapters and this approach works well in giving the reader alternative viewpoints of the same problem. Interestingly the authors havecleverly chosen a definition of'large dataset'. Essentially they focus on datasets with the order of a million cases. As the authors point out there are now many examples of much larger datasets but by limiting to ones that can be loaded in their entirety in standard statistical software they end up with a book that has great utility to the practitioner rather than just the theorist. Another very attractive feature of the book is the many colour plates, showing clearly what can now routinely be seen on the computer screen. The interactive nature of data analysis with large datasets is hard to reproduce in a book but the authors make an excellent attempt to do just this.'P. Marriott for the Short Book Reviews of the ISI Graphics Are Great For Exploring Data, But How Can They Be Used For Looking At The Large Datasets That Are Commonplace To-day? This Book Shows How To Look At Ways Of Visualizing Large Datasets, Whether Large In Numbers Of Cases Or Large In Numbers Of Variables Or Large In Both. Data Visualization Is Useful For Data Cleaning, Exploring Data, Identifying Trends And Clusters, Spotting Local Patterns, Evaluating Modeling Output, And Presenting Results. It Is Essential For Exploratory Data Analysis And Data Mining. Data Analysts, Statisticians, Computer Scientists-indeed Anyone Who Has To Explore A Large Dataset Of Their Own-should Benefit From Reading This Book. New Approaches To Graphics Are Needed To Visualize The Information In Large Datasets And Most Of The Innovations Described In This Book Are Developments Of Standard Graphics. There Are Considerable Advantages In Extending Displays Which Are Well-known And Well-tried, Both In Understanding How Best To Make Use Of Them In Your Work And In Presenting Results To Others. It Should Also Make The Book Readily Accessible For Readers Who Already Have A Little Experience Of Drawing Statistical Graphics. All Ideas Are Illustrated With Displays From Analyses Of Real Datasets And The Authors Emphasize The Importance Of Interpreting Displays Effectively. Graphics Should Be Drawn To Convey Information And The Book Includes Many Insightful Examples. Antony Unwin Holds The Chair Of Computer Oriented Statistics And Data Analysis At The University Of Augsburg. He Has Been Involved In Developing Visualization Software For Twenty Years. Martin Theus Is A Senior Researcher At The University Of Augsburg, Has Worked In Industry And Research In Both Germany And The Usa, And Is The Author Of The Visualization Software Mondrian. Heike Hofmann Is Assistant Professor Of Statistics At Iowa State University. She Wrote The Software Manet And Has Also Cooperated In The Development Of The Ggobi Software. Introduction.- Statistical Graphics.- Scaling Up Graphics.- Interacting With Graphics.- Multivariate Categorical Data--mosaic Plots.- Rotating Plots.- Multivariate Continuous Data--parallel Coordinates.- Networks.- Trees.- Transactions.- Graphics Of A Large Dataset. Antony Unwin, Martin Theus, Heike Hofmann. Thereexistawiderangeofapplicationswhereasigni?cantfractionofthe- mentum and energy present in a physical problem is carried by the transport of particles. Depending on the speci?capplication, the particles involved may be photons, neutrons, neutrinos, or charged particles. Regardless of which phenomena is being described, at the heart of each application is the fact that a Boltzmann like transport equation has to be solved. The complexity, and hence expense, involved in solving the transport problem can be understood by realizing that the general solution to the 3D Boltzmann transport equation is in fact really seven dimensional: 3 spatial coordinates, 2 angles, 1 time, and 1 for speed or energy. Low-order appro- mations to the transport equation are frequently used due in part to physical justi?cation but many in cases, simply because a solution to the full tra- port problem is too computationally expensive. An example is the di?usion equation, which e?ectively drops the two angles in phase space by assuming that a linear representation in angle is adequate. Another approximation is the grey approximation, which drops the energy variable by averaging over it. If the grey approximation is applied to the di?usion equation, the expense of solving what amounts to the simplest possible description of transport is roughly equal to the cost of implicit computational ?uid dynamics. It is clear therefore, that for those application areas needing some form of transport, fast, accurate and robust transport algorithms can lead to an increase in overall code performance and a decrease in time to solution. "Graphics are great for exploring data, but how can they be used for looking at the large datasets that are commonplace today? This book shows how to look at ways of visualizing large datasets, whether large in numbers of cases or large in numbers of variables or large in both. Data visualization is useful for data cleaning, exploring data, identifying trends and clusters, spotting local patterns, evaluating modeling output, and presenting results. It is essential for exploratory data analysis and data mining. Data analysts, statisticians, computer scientists - indeed anyone who has to explore a large dataset of their own - should benefit from reading this book."--Jacket Based on a series of lectures given in the Granlibakken workshop was devoted to providing a forum, where computational transport researchers could communicate their methods with their results. This book presents computational transport in mathematics, astrophysics, high energy density physics, atmospheric physics, oceanography, and plant canopies