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![Figure 2.1: Pang et al.'s [1997] visualization pipeline showing three...](profile/Torre-Zuk/publication/286242934/figure/fig5/AS:669990533492740@1536749599244/Pang-et-als-1997-visualization-pipeline-showing-three-types-of-uncertainty_Q320.jpg)
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Context 1
... Table 5.1. The table also indicates whether direct programming of the graphics processing unit (GPU) would be advantageous, and this will be discussed in more detail in Section 5.5.2. ...
Context 2
... simply visually revealing whether there is uncertainty (at the Boolean level), can clearly be achieved, it is not clear what representations are most appropriate for specific tasks. Most of the uncertainty cues in Table 5.1 have a length above a Boolean indicator, but they may not be appropriate for some tasks, or may lead to confusion. For the task of simply eliciting possibilities, however, most of the cues should work. ...
Context 3
... Ensure visual variable has sufficient length -The length of different encodings was discussed in relation to ArkVis in Table 5.1. Interactive selection of the encod- ing allows the choice of an encoding with sufficient length. ...
Context 4
... Table 5.1 in Chapter 5 we presented a summary of various representations for the tem- poral uncertainty in this domain and made special note of the number of levels of encoding possible in each. The flexibility to choose an encoding with the simplest mapping to cog- nitive task was available in our visualization tool ArkVis. ...
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... Results showed a medium level of detail was most efficient. Work in computer science, cartography, and geography, by contrast, typically approach realism as correlating to certainty (Zuk, 2008), revealing explicit connections not between the rhetorical design and its intended effects but between the computer design and confidence in data (J. Kostelnick et al., 2013). ...
In visual risk communication, there has been a push towards using realism to show potential effects of sea level rise on coastal communities, often with the assumption that higher degrees of realism are more effective. We challenge this assumption by sharing the results of a user-based study exploring reactions to simulated images of flooded landmarks. The findings identify nuanced rhetorical and emotional responses, encouraging technical communicators to contribute to risk scholarship in psychology and cartography.
... Exactly how these duties should be carried out is not specified. When Torre Zuk (2008) addressed the visualization of uncertainty across the sciences, he reached the conclusion that uncertainty should be considered in terms of either data (D) or metadata (MD). 115 We express D within the visualization through cues such as non-photorealistic rendering (NPR) that disrupt the otherwisephotorealistic (PR) rendering of the data; 116 we express MD outside visualization through words. ...
... They argue that this allows them to establish clear distinction between visualisation of uncertainty -which is how we depict uncertainty specified with the data -and the uncertainty of visualisation -which considers how much inaccuracy occurs as the data is processed through the pipeline. With reference to Pang et al.'s [12] uncertainty visualization pipeline, Zuk [21] argues that combining the uncertainties from different stages of the pipeline is important for designing new visualisation method as it separates out the uncertainty introduced by a representation and the visualization itself. For user evaluation this also suggests the benefits of comparing multiple visualizations so that the uncertainty in the "combine stage" may be roughly estimated. ...
... According to Google Scholar, the Winkler and Smith paper has only been referenced 11 times since its publication: Jafar et al. (2007), Finkel (2008, Zuk (2008) Rushdi & Rushdi (2018). The majority of these are in papers relating to medical decision making. ...
... Recognizing that this produced insufficient insight for effective design, the team brainstormed together alternative methods (C-5, Communal, Emergent) to address the challenge of enabling in-situ interviews with internists (doctors who consult on internal medical problems), without putting a strain on their already busy and high-pressure workdays. With the medical collaborator and considerable advice from an ethics board (Communal), the visualization researchers designed what was essentially an in-situ interview (S-3) where the doctors gave a medical consult in the context of their working environment, minimizing their time commitment and maximizing the potential of observing their diagnostic process in a close-to-real situation [2,47]. For the internists, who had agreed to be approached for a consult on pulmonary embolism (PE), the visualization researcher could approach them in the hospital halls, and in a manner similar to how one doctor asks another, ask for a consult on a (non-existent) PE patient (Personal, Active). ...
While previous work exists on how to conduct and disseminate insights from problem-driven visualization projects and design studies, the literature does not address how to accomplish these goals in transdisciplinary teams in ways that advance all disciplines involved. In this paper we introduce and define a new methodological paradigm we call design by immersion, which provides an alternative perspective on problem-driven visualization work. Design by immersion embeds transdisciplinary experiences at the center of the visualization process by having visualization researchers participate in the work of the target domain (or domain experts participate in visualization research). Based on our own combined experiences of working on cross-disciplinary, problemdriven visualization projects, we present six case studies that expose the opportunities that design by immersion enables, including (1) exploring new domain-inspired visualization design spaces, (2) enriching domain understanding through personal experiences, and (3) building strong transdisciplinary relationships. Furthermore, we illustrate how the process of design by immersion opens up a diverse set of design activities that can be combined in different ways depending on the type of collaboration, project, and goals. Finally, we discuss the challenges and potential pitfalls of design by immersion.
... Recognizing that this produced insufficient insight for effective design, the team brainstormed together alternative methods (C-5, Communal, Emergent) to address the challenge of enabling in-situ interviews with internists (doctors who consult on internal medical problems), without putting a strain on their already busy and high-pressure workdays. With the medical collaborator and considerable advice from an ethics board (Communal), the visualization researchers designed what was essentially an in-situ interview (S-3) where the doctors gave a medical consult in the context of their working environment, minimizing their time commitment and maximizing the potential of observing their diagnostic process in a close-to-real situation [2,47]. For the internists, who had agreed to be approached for a consult on pulmonary embolism (PE), the visualization researcher could approach them in the hospital halls, and in a manner similar to how one doctor asks another, ask for a consult on a (non-existent) PE patient (Personal, Active). ...
While previous work exists on how to conduct and disseminate insights from problem-driven visualization projects and design studies, the literature does not address how to accomplish these goals in transdisciplinary teams in ways that advance all disciplines involved. In this paper we introduce and define a new methodological paradigm we call design by immersion, which provides an alternative perspective on problem-driven visualization work. Design by immersion embeds transdisciplinary experiences at the center of the visualization process by having visualization researchers participate in the work of the target domain (or domain experts participate in visualization research). Based on our own combined experiences of working on cross-disciplinary, problem-driven visualization projects, we present six case studies that expose the opportunities that design by immersion enables, including (1) exploring new domain-inspired visualization design spaces, (2) enriching domain understanding through personal experiences, and (3) building strong transdisciplinary relationships. Furthermore, we illustrate how the process of design by immersion opens up a diverse set of design activities that can be combined in different ways depending on the type of collaboration, project, and goals. Finally, we discuss the challenges and potential pitfalls of design by immersion.
... Brodlie et al. studied the sources of errors in the visualization pipeline [5] and demonstrated that the quantification of uncertainty in visualization is important in avoiding misleading interpretations regarding underlying data. In other words, uncertainty quantification can potentially improve the reliability of decision support systems [9,40], especially for sensitive applications. We study uncertainty quantification in the context of level-set visualization. ...
We present a framework for the analysis of uncertainty in isocontour extraction. The marching squares (MS) algorithm for isocontour reconstruction generates a linear topology that is consistent with hyperbolic curves of a piecewise bilinear interpolation. The saddle points of the bilinear interpolant cause topological ambiguity in isocontour extraction. The midpoint decider and the asymptotic decider are well-known mathematical techniques for resolving topological ambiguities. The latter technique investigates the data values at the cell saddle points for ambiguity resolution. The uncertainty in data, however, leads to uncertainty in underlying bilinear interpolation functions for the MS algorithm, and hence, their saddle points. In our work, we study the behavior of the asymptotic decider when data at grid vertices is uncertain. First, we derive closed-form distributions characterizing variations in the saddle point values for uncertain bilinear interpolants. The derivation assumes uniform and nonparametric noise models, and it exploits the concept of ratio distribution for analytic formulations. Next, the probabilistic asymptotic decider is devised for ambiguity resolution in uncertain data using distributions of the saddle point values derived in the first step. Finally, the confidence in probabilistic topological decisions is visualized using a colormapping technique. We demonstrate the higher accuracy and stability of the probabilistic asymptotic decider in uncertain data with regard to existing decision frameworks, such as deciders in the mean field and the probabilistic midpoint decider, through the isocontour visualization of synthetic and real datasets.
... Then, the missing data must be considered into the visualization by a specific information according to [10] and a discontinuity index have to be produced. Moreover, the possible imprecision and/or uncertainty should also be considered from the data modeling to their visualization [11], [12]. In fact, there are also high variation in the collected data of each sensor which may vary over time in terms of volume but also in term of diversity of collected banners. ...
... interview [15] and observation [16]. Both techniques could be used in visualization evaluations, and be used to collect feedbacks from domain experts. ...
Complexity and specificity of monitoring sensor data from marine operations brought a majority of challenges of how to evaluate the visualizations of such sensor data. It's urgently needed to develop and implement new evaluation methods to assess the visualization of maritime data. In this paper, we explore both quantitative and qualitative evaluation approaches to define evaluative metrics of our interactive visualization techniques, and present a case study carrying out by domain researches in real setting. This work is an important part to complement the visual analytics for maritime operations.
... There is, therefore, a balance to be found between minimizing the amount of additional information that is given to users and ensuring that the visual representation effectively communicates the intended message as well as the data's context [28]. Many metrics and heuristics for evaluating the appropriateness of visualizations have been proposed [32], [33]. A discussion of these heuristics and any accompanying metrics can be obtained from [33]. ...
... Many metrics and heuristics for evaluating the appropriateness of visualizations have been proposed [32], [33]. A discussion of these heuristics and any accompanying metrics can be obtained from [33]. ...
Adding uncertainty information to visualizations is becoming increasingly common across domains since its addition helps ensure that informed decisions are made. This work has shown the difficulty that is inherent to representing uncertainty. Moreover, the representation of uncertainty has yet to be thoroughly explored in educational domains even though visualizations are often used in educational reporting. We analyzed 50 uncertainty-augmented visualizations from various disciplines to map out how uncertainty has been represented. We then analyzed 106 visualizations from educational reporting systems where the learner can see the visualization; these visualizations provide learners with information about several factors including their knowledge, performance, and abilities. This analysis mapped the design space that has been employed to communicate a learner’s abilities, knowledge, and interests. It also revealed several opportunities for the inclusion of uncertainty information within visualizations of educational data. We describe how uncertainty information can be added to visualizations of educational data and illustrate these opportunities by augmenting several of the types of visualizations that are found in existing learning analytics reports. The definition of this design space, based on a survey of the literature, will enable the systematic exploration of how different design decisions affect learner trust, understanding, and decision making.
