1. ^aComputer Science Department, Charlotte Visualization Center, University of North Carolina at Charlotte, Charlotte NC 28223, USA
2. ^bSchool of Interactive Arts & Technology, Simon Fraser University, Surrey, BC, Canada V3T 0A3
3. ^cComputer Science Department, Rutgers University, Piscataway, NT 08854, USA

Correspondence: William Ribarsky, E-mail: ribarsky@uncc.edu

Received 27 May 2009; Revised 9 July 2009; Accepted 9 July 2009.

Abstract

There has been progress in the science of analytical reasoning and in meeting the recommendations for future research that were laid out when the field of visual analytics was established. Researchers have also developed a group of visual analytics tools and methods that embody visual analytics principles and attack important and challenging real-world problems. However, these efforts are only the beginning and much study remains to be done. This article examines the state of the art in visual analytics methods and reasoning and gives examples of current tools and capabilities. It shows that the science of visual analytics needs interdisciplinary efforts, indicates some of the disciplines that should be involved and presents an approach to how they might work together. Finally, the article describes some gaps, opportunities and future directions in developing new theories and models that can be enacted in methods and design principles and applied to significant and complex practical problems and data.