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 technique… Show more
“…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.…”
For decades, uncertainty visualisation has attracted attention in disciplines such as cartography and geographic visualisation, scientific visualisation and information visualisation. Most of this research deals with the development of new approaches to depict uncertainty visually; only a small part is concerned with empirical evaluation of such techniques. This systematic review aims to summarize past user studies and describe their characteristics and findings, focusing on the field of geographic visualisation and cartography and thus on displays containing geospatial uncertainty. From a discussion of the main findings, we derive lessons learned and recommendations for future evaluation in the field of uncertainty visualisation. We highlight the importance of user tasks for successful solutions and recommend moving towards task-centered typologies to support systematic evaluation in the field of uncertainty visualisation.
“…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.…”
For decades, uncertainty visualisation has attracted attention in disciplines such as cartography and geographic visualisation, scientific visualisation and information visualisation. Most of this research deals with the development of new approaches to depict uncertainty visually; only a small part is concerned with empirical evaluation of such techniques. This systematic review aims to summarize past user studies and describe their characteristics and findings, focusing on the field of geographic visualisation and cartography and thus on displays containing geospatial uncertainty. From a discussion of the main findings, we derive lessons learned and recommendations for future evaluation in the field of uncertainty visualisation. We highlight the importance of user tasks for successful solutions and recommend moving towards task-centered typologies to support systematic evaluation in the field of uncertainty visualisation.
“…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].…”
We tested how non-experts judge point probability for seven different visual representations of uncertainty, using a case from an unfamiliar domain. Participants (n = 140) rated the probability that the boundary between two earth layers passed through a given point, for seven different visualizations of the positional uncertainty of the boundary. For all types of visualizations, most observers appear to construct an internal model of the uncertainty distribution that closely resembles a normal distribution. However, the visual form of the uncertainty range (i.e., the visualization type) affects this internal model and the internal model relates to participants' numeracy. We conclude that perceived certainty is affected by its visual representation. In a follow-up experiment we found no indications that the absence (or presence) of a prominent center line in the visualization affects the internal model. We discuss if and how our results inform which visual representation is most suitable for representing uncertainty and make suggestions for future work.
“…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…”
Section: General Vandv Assessmentmentioning
confidence: 99%
“…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.…”
Various evaluation approaches exist for multi-purpose visual analytics (VA) frameworks. They are based on empirical studies in information visualization or on community activities, for example, VA Science and Technology Challenge (2006-2014) created as a community evaluation resource to "decide upon the right metrics to use, and the appropriate implementation of those metrics including datasets and evaluators" 1 . In this paper, we propose to use evaluated VA environments for computer-based processes or systems with the main goal of aligning user plans, system models and software results. For this purpose, trust in VA outcome should be established, which can be done by following the (meta-)design principles of a human-centered verification and validation assessment and also in dependence on users' task models and interaction styles, since the possibility to work with the visualization interactively is an integral part of VA. To define reliable VA, we point out various dimensions of reliability along with their quality criteria, requirements, attributes and metrics. Several software packages are used to illustrate the concepts.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.