Beyond Uncertainty: Examining Meta-Information Visualization Techniques
J. Pfautz, A. Bisantz, E. Roth, A. Fouse, and A. Nunes
SIGGRAPH, Boston, MA (July/August, 2006)
The rendering of timely and accurate decisions in safety-critical systems requires that the human operator integrate information from various sources. The integration of this information in turn depends on the skill and experience level of the operator and on a thorough understanding of the qualities of that information (e.g. recency), known as meta-information. Such qualities can critically influence how an operator will process information, understand information, and make decisions based on information (Pfautz et al, 2006). In recent years, researchers have explored various visualization techniques aimed at helping operators better integrate/assimilate meta-information in the environment. However, these attempts have traditionally focused on one type of meta-information – uncertainty (Finger et al, 2002) – and empirical assessments of the benefits of the techniques were limited in scope. While consideration of uncertainty in decision-making is important, we postulate that other forms of meta-information (e.g. staleness) must be considered as well. We believe that empirical testing of graphical concepts is critical to being able to provide a thorough evaluation of the benefits that such visualization techniques may hold for improving decision-making ability. Here, we briefly summarize ongoing research initiatives where we have used Cognitive Work Analysis techniques to identify the information and meta-information requirements of operators across various safety-critical domains. These techniques have supported the development and empirical evaluation of methods for visualizing meta-information to improving decision-making.
Intelligence analysts use networks of sensors to detect, classify, identify, and track threats. This visualization uses opacity to represent certainty of sensor coverage, size to represent certainty with which a vehicle is detected, and saturation to represent certainty with which a vehicle is classified, to aid the intelligence analysts in understanding how the behavior of the sensor network affects these tasks.
Commercial fishermen may use chemical readings from different water locations to identify the best places to find fish. To aid in this task, this visualization uses color to represent pH of water, symbol size to represent deviation from expected values, and symbol shape to identify the person who sampled the water.
Business analysts need to reason about interrelationships between companies and companies, and companies and their key employess. This visualization uses to line thickness to represent certainty of relationship existence, and dashed lines to represent the relative age of the information for each link.
References
Finger, R. & Bisantz, A.M. (2002). "Utilizing Graphical Formats to Convey Uncertainty in a Decision Making Task. Theo. Issues in Ergonomics Science," 3, 1, 1 - 25.
Pfautz, J., Roth, J., Bisantz, A., Thomas-Meyers, G., Llinas, J. & Fouse, A. (2006). "The Role of Meta-Information in C2 Decision-Support Systems," Proc. of CCRTS ‘06.
Bisantz, A., Pfautz, J. Stone, R., Roth, E, Thomas-Meyers, G. (2006). "Assessment of Color Variables for Displaying Meta-Information on Maps," to be presented at HFES 50th Ann. Mtg.
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