Uncertainty visualization prototypes for materials modeling

poste

Applying insights on categorisation, communication, and dynamic decision-making: A case study of a ‘simple’ maritime military decision

A complete understanding of decision-making in military domains requires gathering insights from several fields of study. To make the task tractable, here we consider a specific example of short-term tactical decisions under uncertainty made by the military at sea. Through this lens, we sketch out relevant literature from three psychological tasks each underpinned by decision-making processes: categorisation, communication, and choice. From the literature, we note two general cognitive tendencies that emerge across all three stages: the effect of cognitive load and individual differences. Drawing on these tendencies, we recommend strategies, tools and future research that could improve performance in military domains—but, by extension, would also generalise to other high-stakes contexts. In so doing, we show the extent to which domain general properties of high order cognition are sufficient in explaining behaviours in domain specific contexts

Animated Visual Vibrations as an Uncertainty Visualisation Technique

Research into the visualisation of imprecise data is a relatively new field in visualisation. Work is beginning to appear detailing the process of visualising uncertainty in data. Continuing previous work by the author, this paper seeks to extend techniques used to visualise uncertainty from the spatial to the temporal domain, by using visual vibrations to indicate the level of imprecision at a visualised data point. The paper contains an analysis of the present visual features used to indicate imprecision, and then details a methodology for using visual vibrations to display the uncertainty contained in visualised data. Novel additions include addressing chart junk issues outlined by Tufte, additions of perceptual factors and extension to stereo vision applications

Evaluating Sketchy Lines for the Visualization of Qualitative Uncertainty

We report on results of a series of user studies on the perception of visual variables that are commonly used in the literature to depict uncertainty. To the best of our knowledge, we provide the first formal evaluation of the use of these variables to facilitate an easier reading of uncertainty in visualizations that rely on line graphical primitives. In addition to blur, dashing and grayscale, we investigate the use of 'sketchiness' as a visual variable because it conveys visual impreciseness that may be associated with data quality. Inspired by work in non-photorealistic rendering and by the features of hand-drawn lines, we generate line trajectories that resemble hand-drawn strokes of various levels of proficiency--ranging from child to adult strokes--where the amount of perturbations in the line corresponds to the level of uncertainty in the data. Our results show that sketchiness for the visualization of uncertainty in lines is as intuitive as blur; although people subjectively prefer dashing style over blur, grayscale and sketchiness. We discuss advantages and limitations of each technique and conclude with design considerations on how to deploy these visual variables to effectively depict various levels of uncertainty for line marks.Nous rapportons les résultats d'études utilisateurs sur la perception de variables visuelles qui sont couramment utilisées dans la littérature pour représenter l'incertitude. A notre connaissance, nous rapportons la première évaluation formelle de l'utilisation de ces variables pour faciliter la lecture de l'incertitude dans des visualisations qui reposent sur des lignes graphiques primitives. En plus du flouté, des pointillés et des niveaux de gris, nous avons étudié l'utilisation du dessin á main levée comme variable visuelle parce qu'il transmet l'imprécision qui peut être associée á la qualité des données. Inspirés par les travaux sur NPR (Non-Photorealistic Rendering) et par l'aspect des lignes tracées á la main, nous avons généré des trajectoires linéaires ressemblant á des traits dessinés á la main, et correspondant á différents niveaux de compétence--allant de l'enfant á l'adulte--pour lesquels la quantité de perturbations dans la ligne correspondait au niveau d'incertitude des données. Nos résultats montrent que l'utilisation du dessin á main levée pour la visualisation de l'incertitude dans les lignes est aussi intuitive que le flouté, bien qu'intuitivement les personnes préfèrent subjectivement les pointillés au flou, aux niveaux de gris ainsi qu'au dessin á main levée. Nous discutons les avantages et les limites de chaque technique et nous concluons sur des considérations de conception, concernant comment déployer ces variables visuelles pour représenter efficacement plusieurs niveaux d'incertitude pour des repères linéaires

Effective Visualizations of the Uncertainty in Hurricane Forecasts

The track forecast cone developed by the U.S. National Hurricane Center is the one most universally adopted by the general public, the news media, and governmental officials to enhance viewers\u27 understanding of the forecasts and their underlying uncertainties. However, current research has experimentally shown that it has limitations that result in misconceptions of the uncertainty included. Most importantly, the area covered by the cone tends to be misinterpreted as the region affected by the hurricane. In addition, the cone summarizes forecasts for the next three days into a single representation and, thus, makes it difficult for viewers to accurately determine crucial time-specific information. To address these limitations, this research develops novel alternative visualizations. It begins by developing a technique that generates and smoothly interpolates robust statistics from ensembles of hurricane predictions, thus creating visualizations that inherently include the spatial uncertainty by displaying three levels of positional storm strike risk at a specific point in time. To address the misconception of the area covered by the cone, this research develops time-specific visualizations depicting spatial information based on a sampling technique that selects a small, representative subset from an ensemble of points. It also allows depictions of such important storm characteristics as size and intensity. Further, this research generalizes the representative sampling framework to process ensembles of forecast tracks, selecting a subset of tracks accurately preserving the original distributions of available storm characteristics and keeping appropriately defined spatial separations. This framework supports an additional hurricane visualization portraying prediction uncertainties implicitly by directly showing the members of the subset without the visual clutter. We collaborated on cognitive studies that suggest that these visualizations enhance viewers\u27 ability to understand the forecasts because they are potentially interpreted more like uncertainty distributions. In addition to benefiting the field of hurricane forecasting, this research potentially enhances the visualization community more generally. For instance, the representative sampling framework for processing 2D points developed here can be applied to enhancing the standard scatter plots and density plots by reducing sizes of data sets. Further, as the idea of direct ensemble displays can possibly be extended to more general numerical simulations, it, thus, has potential impacts on a wide range of ensemble visualizations

Scalable visualization of spatial data in 3D terrain

Designing visualizations of spatial data in 3D terrain is challenging because various heterogeneous data aspects need to be considered, including the terrain itself, multiple data attributes, and data uncertainty. It is hardly possible to visualize these data at full detail in a single image. Therefore, this thesis devises a scalable visualization approach that focuses on relevant information to be emphasized, while less-relevant information can be attenuated. In this context, a noval concept of visualizing spatial data in 3D terrain and different soft- and hardware solutions are proposed.Die Erstellung von Visualisierungen für räumliche Daten im 3D-Gelände ist schwierig, da viele heterogene Datenaspekte wie das Gelände selbst, die verschiedenen Datenattribute sowie Unsicherheiten bei der Darstellung zu berücksichtigen sind. Im Allgemeinen ist es nicht möglich, diese Datenaspekte gleichzeitig in einer Visualisierung darzustellen. Daher werden in der Arbeit skalierbare Visualisierungsstrategien entwickelt, welche die wichtigen Informationen hervorheben und trotzdem gleichzeitig Kontextinformationen liefern. Hierfür werden neue Systematisierungen und Konzepte vorgestellt

Abstract Animated Visual Vibrations as an Uncertainty Visualisation Technique

Research into the visualisation of imprecise data is a relatively new field in visualisation. Work is beginning to appear detailing the process of visualising uncertainty in data. Continuing previous work by the author, this paper seeks to extend techniques used to visualise uncertainty from the spatial to the temporal domain, by using visual vibrations to indicate the level of imprecision at a visualised data point. The paper contains an analysis of the present visual features used to indicate imprecision, and then details a methodology for using visual vibrations to display the uncertainty contained in visualised data. Novel additions include addressing chart junk issues outlined by Tufte, additions of perceptual factors and extension to stereo vision applications