Supporting Knowledge Transfer through Decomposable Reasoning Artifacts
HICSS '07 Proceedings of the 40th Annual Hawaii International Conference on System Sciences
Information Visualization - Special issue on visual analytics science and technology
Contraflow Transportation Network Reconfiguration for Evacuation Route Planning
IEEE Transactions on Knowledge and Data Engineering
Jigsaw: supporting investigative analysis through interactive visualization
Information Visualization
Defining Insight for Visual Analytics
IEEE Computer Graphics and Applications
WireVis: Visualization of Categorical, Time-Varying Data From Financial Transactions
VAST '07 Proceedings of the 2007 IEEE Symposium on Visual Analytics Science and Technology
Design Considerations for Collaborative Visual Analytics
VAST '07 Proceedings of the 2007 IEEE Symposium on Visual Analytics Science and Technology
Recovering Reasoning Processes from User Interactions
IEEE Computer Graphics and Applications
Information Visualization
Data transformations and representations for computation and visualization
Information Visualization
Collaborative brushing and linking for co-located visual analytics of document collections
EuroVis'09 Proceedings of the 11th Eurographics / IEEE - VGTC conference on Visualization
Combining automated and interactive visual analysis of biomechanical motion data
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part II
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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 analyties 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.