Available:*
Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
|---|---|---|---|---|---|
Searching... | 30000010337911 | QA76.9.I52 B67 2014 | Open Access Book | Book | Searching... |
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Summary
Summary
A guide to the basics of information visualization that teaches nonprogrammers how to use advanced data mining and visualization techniques to design insightful visualizations. In the age of Big Data, the tools of information visualization offer us a macroscope to help us make sense of the avalanche of data available on every subject. This book offers a gentle introduction to the design of insightful information visualizations. It is the only book on the subject that teaches nonprogrammers how to use open code and open data to design insightful visualizations. Readers will learn to apply advanced data mining and visualization techniques to make sense of temporal, geospatial, topical, and network data.
The book, developed for use in an information visualization MOOC, covers data analysis algorithms that enable extraction of patterns and trends in data, with chapters devoted to "when" (temporal data), "where" (geospatial data), "what" (topical data), and "with whom" (networks and trees); and to systems that drive research and development. Examples of projects undertaken for clients include an interactive visualization of the success of game player activity in World of Warcraft ; a visualization of 311 number adoption that shows the diffusion of non-emergency calls in the United States; a return on investment study for two decades of HIV/AIDS research funding by NIAID; and a map showing the impact of the HiveNYC Learning Network.
Visual Insights will be an essential resource on basic information visualization techniques for scholars in many fields, students, designers, or anyone who works with data.
Author Notes
Katy Brner is the Victor H. Yngve Professor of Information Science at the School of Informatics and Computing, Indiana University, where she directs the Cyberinfrastructure for Network Science Center (CNS). Her research focuses on the development of data analysis and visualization techniques for information access, understanding, and management. She is the author of Atlas of Science: Visualizing What We Know (MIT Press).
David E. Polley (also known as "Ted") is on the research staff at CNS. He is interested in how emerging technologies and instruction can be used in library settings to improve information literacy and enrich the lives of both students and the general public.
Reviews 1
Choice Review
This book, a text for a massively open online course (MOOC) on information visualization, focuses on developing a variety of different types of visualizations to make sense of data. It is organized around a framework for visualization and emphasizes workflows. The chapters explore charts, graphs, tables, maps, and network graphs, and how they can be created and analyzed to bring meaning to large and small data sets. The book includes hands-on activities and case studies for each chapter, built on the freeware visualization package Sci2. A companion website provides additional resources and data. The presentation of the material is clear and logical, and the book is well written and well illustrated. The material is accessible to informed laypersons and is useful to students and practitioners. Borner and Polley (both, Indiana Univ.) use their considerable experience in the field of visualization to provide a broad set of compelling examples and case studies. Though it is not comprehensive, the book highlights key topics in information visualization in a very readable fashion. Readers may also be interested in Börner's Atlas of Science: Visualizing What We Know (CH, Mar'11, 48-3824). Summing Up: Highly recommended. All levels/libraries. --Robert A. Kolvoord, James Madison University
Table of Contents
| Note from the Authors | p. viii |
| Preface | p. ix |
| Acknowledgments | p. xi |
| Chapter 1 Visualization Framework and Workflow Design | p. 1 |
| Chapter 2 "WHEN": Temporal Data | p. 37 |
| Chapter 3 "WHERE": Geospatial Data | p. 75 |
| Chapter 4 "WHAT": Topical Data | p. 113 |
| Chapter 5 "WITH WHOM": Tree Data | p. 143 |
| Chapter 6 "WITH WHOM": Network Data | p. 169 |
| Chapter 7 Dynamic Visualizations and Deployment | p. 215 |
| Chapter 8 Case Studies | p. 235 |
| Understanding the Diffusion of Non-Emergency Call Systems | p. 236 |
| Examining the Success of World of Warcraft Game Player Activity | p. 242 |
| Using Point of View Cameras to Study Student-Teacher Interactions | p. 248 |
| Phylet: An Interactive Tree of Life Visualization | p. 254 |
| Isis: Mapping the Geospatial and Topical Distribution of the History of Science Journal | p. 260 |
| Visualizing the Impact of the Hive NYC Learning Network | p. 266 |
| Chapter 9 Discussion and Outlook | p. 273 |
| Appendix | p. 284 |
| Image Credits | p. 292 |
| Index | p. 294 |
