Evaluation of hazard brochures using topic viewing durations: Application to tsunami evacuation brochures.

This study describes a novel method of assessing risk communication effectiveness by reporting an evaluation of a tsunami information brochure by 90 residents of three Pacific coast communities that are vulnerable to a Cascadia Subduction Zone earthquake and tsunami-Commencement Bay, Washington; Lincoln City, Oregon; and Eureka, California. Study participants viewed information that was presented in DynaSearch, an internet-based computer system that allowed them to view text boxes and tsunami inundation zone maps. DynaSearch recorded the number of times each text box or map was clicked and the length of time that it was viewed. This information viewing phase was followed by questionnaire pages assessing important aspects of tsunami hazard and sources of tsunami warnings. Participants gave the longest click durations to what to do in the emergency period during earthquake shaking and in its immediate aftermath before a tsunami arrives-topics that should be displayed prominently in tsunami brochures and emphasized in talks to community groups. The smallest adjusted click durations were associated with advance preparations for a tsunami-topics that can be posted on websites whose URLs are printed in the brochures.

Using Microsaccades to Estimate Task Difficulty During Visual Search of Layered Surfaces.

We develop an approach to using microsaccade dynamics for the measurement of task difficulty/cognitive load imposed by a visual search task of a layered surface. Previous studies provide converging evidence that task difficulty/cognitive load can influence microsaccade activity. We corroborate this notion. Specifically, we explore this relationship during visual search for features embedded in a terrain-like surface, with the eyes allowed to move freely during the task. We make two relevant contributions. First, we validate an approach to distinguishing between the ambient and focal phases of visual search. We show that this spectrum of visual behavior can be quantified by a single previously reported estimator, known as Krejtz's K coefficient. Second, we use ambient/focal segments based on K as a moderating factor for microsaccade analysis in response to task difficulty. We find that during the focal phase of visual search (a) microsaccade magnitude increases significantly, and (b) microsaccade rate decreases significantly, with increased task difficulty. We conclude that the combined use of K and microsaccade analysis may be helpful in building effective tools that provide an indication of the level of cognitive activity within a task while the task is being performed.

A tutorial on DynaSearch: A Web-based system for collecting process-tracing data in dynamic decision tasks.

This tutorial describes DynaSearch, a Web-based system that supports process-tracing experiments on coupled-system dynamic decision-making tasks. A major need in these tasks is to examine the process by which decision makers search over a succession of situation reports for the information they need in order to make response decisions. DynaSearch provides researchers with the ability to construct and administer Web-based experiments containing both between- and within-subjects factors. Information search pages record participants' acquisition of verbal, numeric, and graphic information. Questionnaire pages query participants' recall of information, inferences from that information, and decisions about appropriate response actions. Experimenters can access this information in an online viewer to verify satisfactory task completion and can download the data in comma-separated text files that can be imported into statistical analysis packages.

Visualizing Uncertain Tropical Cyclone Predictions using Representative Samples from Ensembles of Forecast Tracks.

A common approach to sampling the space of a prediction is the generation of an ensemble of potential outcomes, where the ensemble's distribution reveals the statistical structure of the prediction space. For example, the US National Hurricane Center generates multiple day predictions for a storm's path, size, and wind speed, and then uses a Monte Carlo approach to sample this prediction into a large ensemble of potential storm outcomes. Various forms of summary visualizations are generated from such an ensemble, often using spatial spread to indicate its statistical characteristics. However, studies have shown that changes in the size of such summary glyphs, representing changes in the uncertainty of the prediction, are frequently confounded with other attributes of the phenomenon, such as its size or strength. In addition, simulation ensembles typically encode multivariate information, which can be difficult or confusing to include in a summary display. This problem can be overcome by directly displaying the ensemble as a set of annotated trajectories, however this solution will not be effective if ensembles are densely overdrawn or structurally disorganized. We propose to overcome these difficulties by selectively sampling the original ensemble, constructing a smaller representative and spatially well organized ensemble. This can be drawn directly as a set of paths that implicitly reveals the underlying spatial uncertainty distribution of the prediction. Since this approach does not use a visual channel to encode uncertainty, additional information can more easily be encoded in the display without leading to visual confusion. To demonstrate our argument, we describe the development of a visualization for ensembles of tropical cyclone forecast tracks, explaining how their spatial and temporal predictions, as well as other crucial storm characteristics such as size and intensity, can be clearly revealed. We verify the effectiveness of this visualization approach through a cognitive study exploring how storm damage estimates are affected by the density of tracks drawn, and by the presence or absence of annotating information on storm size and intensity.

Uncertainty Visualization by Representative Sampling from Prediction Ensembles.

Data ensembles are often used to infer statistics to be used for a summary display of an uncertain prediction. In a spatial context, these summary displays have the drawback that when uncertainty is encoded via a spatial spread, display glyph area increases in size with prediction uncertainty. This increase can be easily confounded with an increase in the size, strength or other attribute of the phenomenon being presented. We argue that by directly displaying a carefully chosen subset of a prediction ensemble, so that uncertainty is conveyed implicitly, such misinterpretations can be avoided. Since such a display does not require uncertainty annotation, an information channel remains available for encoding additional information about the prediction. We demonstrate these points in the context of hurricane prediction visualizations, showing how we avoid occlusion of selected ensemble elements while preserving the spatial statistics of the original ensemble, and how an explicit encoding of uncertainty can also be constructed from such a selection. We conclude with the results of a cognitive experiment demonstrating that the approach can be used to construct storm prediction displays that significantly reduce the confounding of uncertainty with storm size, and thus improve viewers' ability to estimate potential for storm damage.

Texturing of layered surfaces for optimal viewing.

This paper is a contribution to the literature on perceptually optimal visualizations of layered three-dimensional surfaces. Specifically, we develop guidelines for generating texture patterns, which, when tiled on two overlapped surfaces, minimize confusion in depth-discrimination and maximize the ability to localize distinct features. We design a parameterized texture space and explore this texture space using a "human in the loop" experimental approach. Subjects are asked to rate their ability to identify Gaussian bumps on both upper and lower surfaces of noisy terrain fields. Their ratings direct a genetic algorithm, which selectively searches the texture parameter space to find fruitful areas. Data collected from these experiments are analyzed to determine what combinations of parameters work well and to develop texture generation guidelines. Data analysis methods include ANOVA, linear discriminant analysis, decision trees, and parallel coordinates. To confirm the guidelines, we conduct a post-analysis experiment, where subjects rate textures following our guidelines against textures violating the guidelines. Across all subjects, textures following the guidelines consistently produce high rated textures on an absolute scale, and are rated higher than those that did not follow the guidelines.

An approach to the perceptual optimization of complex visualizations.

This paper proposes a new experimental framework within which evidence regarding the perceptual characteristics of a visualization method can be collected, and describes how this evidence can be explored to discover principles and insights to guide the design of perceptually near-optimal visualizations. We make the case that each of the current approaches for evaluating visualizations is limited in what it can tell us about optimal tuning and visual design. We go on to argue that our new approach is better suited to optimizing the kinds of complex visual displays that are commonly created in visualization. Our method uses human-in-the-loop experiments to selectively search through the parameter space of a visualization method, generating large databases of rated visualization solutions. Data mining is then used to extract results from the database, ranging from highly specific exemplar visualizations for a particular data set, to more broadly applicable guidelines for visualization design. We illustrate our approach using a recent study of optimal texturing for layered surfaces viewed in stereo and in motion. We show that a genetic algorithm is a valuable way of guiding the human-in-the-loop search through visualization parameter space. We also demonstrate several useful data mining methods including clustering, principal component analysis, neural networks, and statistical comparisons of functions of parameters.