Evaluation of uncertainty visualization techniques for information fusion

Abstract

This paper highlights the importance of uncertainty visualization in information fusion, reviews general methods of representing uncertainty and presents perceptual and cognitive principles from Tufte, Chambers and Bertin as well as users experiments documented in the literature. Examples of uncertainty representations in information fusion are analyzed using these general theories. These principles can be used in future theoretical evaluations of existing or newly developed uncertainty visualization techniques before usability testing with actual users.

How to Assess Visual Communication of Uncertainty? A Systematic Review of Geospatial Uncertainty Visualisation User Studies

“…Studies that do not appear in our list include, e.g. conceptual evaluations that use heuristics derived from general guidelines and rules for data visualisation coined by Bertin, Tufte, Ware and Chambers (Riveiro, 2007a;Riveiro, 2007b;Wittenbrink et al, 1996;Zuk and Carpendale, 2006). Such studies, while not our focus here, do provide basic statements on the usability of different methods and therefore can help to choose suitable visualisation techniques.…”

The Perception of Visual UncertaintyRepresentation by Non-Experts

“…For example, blurring or degradation of the data has an intuitive relation with uncertainty: the harder it is to see or recognize something, the more uncertain it appears [5]. However, blurring or degradation could inadvertently be interpreted as poor visualization quality [10].…”

Reliable Visual Analytics, a Prerequisite for Outcome Assessment of Engineering Systems

“…The JDL/DFIG model 8 defines a six level approach for this purpose consisting of source preprocessing and subject assessment; object, situation, impact assessment; process refinement; and user (cognitive) refinement. The last level is necessary to overcome the HCI bottleneck in the information process fusion [51]. The important aspects are Cognitive aids that provide functions to aid and assist human understanding and exploitation of data Negative reasoning enhancement that helps to overcome the human tendency to seek for information which supports their hypothesis and ignore negative information…”

“…Uncertainty representation methods that are necessary to improve quantification, visualization and, with that, the understanding of uncertainty Time compression/expansion replay techniques that can assist in understanding of evolving tactical situations, on account of human capabilities to detect changes Focus/defocus of attention techniques that can assist in directing the attention of an analyst to different aspects of data Pattern morphing methods that can translate patterns of data into forms that are easier to interpret for a human Information fusion strategies mentioned above need to be supplemented by evaluation of uncertainty visualization techniques for them. This is due to the fact that "huge quantities of (higher dimensional) data from several sources carrying various forms of uncertainty" need to be represented "on a two or three dimensional device" [51], which can only be done in a reliable way if this uncertainty is properly translated using generally accepted perceptual and cognitive principles. Automated recommender platforms support users in selecting appropriate software frameworks, interfaces, and interaction styles.…”