Ambiguity is pervasive in the complex sensemaking domains of risk assessment and prediction but there remains little research on how to design visual analytics tools to accommodate it. We report on findings from a qualitative study based on a conceptual framework of sensemaking processes to investigate how both new visual analytics designs and existing tools, primarily data tables, support the cognitive work demanded in avalanche forecasting. While both systems yielded similar analytic outcomes we observed differences in ambiguous sensemaking and the analytic actions either afforded. Our findings challenge conventional visualization design guidance in both perceptual and interaction design, highlighting the need for data interfaces that encourage reflection, provoke alternative interpretations, and support the inherently ambiguous nature of sensemaking in this critical application. We review how different visual and interactive forms support or impede analytic processes and introduce "gisting" as a significant yet unexplored analytic action for visual analytics research. We conclude with design implications for enabling ambiguity in visual analytics tools to scaffold sensemaking in risk assessment.

We report a study investigating the viability of using interactive visualizations to aid architectural design with building codes. While visualizations have been used to support general architectural design exploration, existing computational solutions treat building codes as separate from, rather than part of, the design process, creating challenges for architects. Through a series of participatory design studies with professional architects, we found that interactive visualizations have promising potential to aid design exploration and sensemaking in early stages of architectural design by providing feedback about potential allowances and consequences of design decisions. However, implementing a visualization system necessitates addressing the complexity and ambiguity inherent in building codes. To tackle these challenges, we propose various user-driven knowledge management mechanisms for integrating, negotiating, interpreting, and documenting building code rules.

Ambiguity, the state in which alternative interpretations are plausible or even desirable, is an inexorable part of complex sensemaking. Its challenges are compounded when analysis involves risk, is constrained, and needs to be shared with others. We report on several studies with avalanche forecasters that illuminated these challenges and identified how visualization designs can better support ambiguity. Like many complex analysis domains, avalanche forecasting relies on highly heterogeneous and incomplete data where the relevance and meaning of such data is context-sensitive, dependant on the knowledge and experiences of the observer, and mediated by the complexities of communication and collaboration. In this paper, we characterize challenges of ambiguous interpretation emerging from data , analytic processes , and collaboration and communication and describe several management strategies for ambiguity. Our findings suggest several visual analytics design approaches that explicitly address ambiguity in complex sensemaking around risk.

Risk assessment and follow-up of oral potentially malignant disorders in patients with mild or moderate oral epithelial dysplasia is an ongoing challenge for improved oral cancer prevention. Part of the challenge is a lack of understanding of how observable features of such dysplasia, gathered as data by clinicians during follow-up, relate to underlying biological processes driving progression. Current research is at an exploratory phase where the precise questions to ask are not known. While traditional statistical and the newer machine learning and artificial intelligence methods are effective in well-defined problem spaces with large datasets, these are not the circumstances we face currently. We argue that the field is in need of exploratory methods that can better integrate clinical and scientific knowledge into analysis to iteratively generate viable hypotheses. In this perspective, we propose that visual analytics presents a set of methods well-suited to these needs. We illustrate how visual analytics excels at generating viable research hypotheses by describing our experiences using visual analytics to explore temporal shifts in the clinical presentation of epithelial dysplasia. Visual analytics complements existing methods and fulfills a critical and at-present neglected need in the formative stages of inquiry we are facing.

Ambiguity, an information state where multiple interpretations are plausible, is a common challenge in visual analytics (VA) systems. We discuss lessons learned from a case study designing VA tools for Canadian avalanche forecasters. Avalanche forecasting is a complex and collaborative risk-based decision-making and analysis domain, demanding experience and knowledge-based interpretation of human reported and uncertain data. Differences in reporting practices, organizational contexts, and the particularities of individual reports result in a variety of potential interpretations that have to be negotiated as part of the forecaster's sensemaking processes. We describe our preliminary research using glyphs to support sensemaking under ambiguity. Ambiguity is not unique to public avalanche forecasting. There are many other domains where the way data are measured and reported vary in ways not accounted explicitly in the data and require analysts to negotiate multiple potential meanings. We argue that ambiguity is under-served by visualization research and would benefit from more explicit VA support.

Forecasting snow avalanches requires a reliable stream of field observations, which are often difficult and expensive to collect. Despite the increasing capability of simulating snowpack conditions with physical models, models have seen limited adoption by avalanche forecasters. Feedback from forecasters suggests that model data are presented in ways that are difficult to interpret and irrelevant to operational needs. We apply a visualization design framework to enhance the value of snowpack models to avalanche forecasters. An established risk-based avalanche forecasting workflow is used to define the ways forecasters solve problems with snowpack data. We suggest that model data be visualized in ways that directly support common forecasting tasks such as identifying snowpack features related to avalanche problems and locating avalanche problems in terrain at relevant spatial scales. Examples of visualizations that support these tasks and follow established perceptual and cognitive principles from the field of information visualization are presented. Interactive designs play a critical role in understanding these complex datasets and are well suited for forecasting workflows. Although extensive user testing is still needed to evaluate the effectiveness of these designs, visualization design principles open the door to more relevant and interpretable applications of snowpack model for avalanche forecasters. This work sets the stage for implementing snowpack models into visualization tools where forecasters can test their operational value and learn their capabilities and deficiencies.

: Although weather forecast models play a key role in avalanche forecasting, snowpack models have had limited uptake. As the quantity of weather and snowpack data grows with increasing computer power, methods to distill meaningful patterns will become more important. We generated spatially distributed snowpack data for over 15,000 km2 of western Canada during the 2017-18 winter. The snow cover model SNOWPACK was forced with meteorological data from a numerical weather prediction model on a 2.5 km grid. We developed visualization software to explore multiple snow profiles at the same time and shared it in real-time with six avalanche safety organizations. The visualization tool revealed common features in the profiles that could be aligned according to their deposition date. A case study at Mike Wiegele Helicopter Skiing found the visualizations contained many of the snowpack features described in their operational hazard assessments. Improvements to the visualizations and further post-processing of the model output could allow practitioners to interact with snowpack models in more meaningful ways. To be presented at the International Snow Science Workshop in

Narrative visualizations engage audience in data stories, evoking emotions by using narrative patterns, rhetoric, visual design, and content among other strategies. How these elements combine to influence user experiences is complex and difficult to measure using empirical methods. This is partly due to the fact that narrative visualizations influence audiences affectively and implicitly [1]–[3]. Evaluations of narrative visualizations that aim to better understand these mechanisms should capture this rich complexity by focusing on gathering descriptions of lived experience. Micro-phenomenology, a rigorous set of methods developed for soliciting descriptions of experiences, has empirically been shown to improve recollection of otherwise implicit aspects of experience [4]. Building on work using micro-phenomenological interviews to evaluate static visualizations [5], we apply these methods to interactive narrative visualizations. We conducted a small study to explore the potential of these methods in this context. Our findings reveal how narrative patterns and designs influence affective states, how they support various forms of exploratory analysis, and how they can facilitate or hinder non-analytical reflection such as the imagining of stories described within visualizations. These types of insights can inform future designs and help researchers understand how techniques employed in narrative visualizations influence users in specific and often implicit ways.

The visual information that contributes to speech perception has been known for over a half century (Sumby and Pollack, JASA, 1954). More recent work has demonstrated two important constraints on the benefit of visual information to speech perception. First, the benefit patterns with temporal constraints on general audiovisual sensory processing, and degrades when auditory and visual signals are misaligned by an offset on the order of the duration of a syllable. Second, studies have demonstrated that low spatiotemporal frequency visual information is sufficient for boosting speech intelligibility. Collectively, these findings suggest that visual information facilitates processing of linguistic information organized in the speech stream primarily at the level of the syllable. Our experiments address how manipulation of the phasing and amplitude of visual components of the audiovisual stream associated with reiterant speech affects the perception of linguistic stress. Our analyses focus on determining the sensitivity of speech perception to changes in the fine-grained structure of visible motion (kinematics) and its alignment with the speech acoustics.