Designing for Ambiguity in Visual Analytics: Lessons from Risk Assessment and Prediction.

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.

Finding Their Data Voice: Practices and Challenges of Dashboard Users.

Dashboards are the ubiquitous means of data communication within organizations. Yet we have limited understanding of how they factor into data practices in the workplace, particularly for data workers who do not self-identify as professional analysts. We focus on data workers who use dashboards as a primary interface to data, reporting on an interview study that characterizes their data practices and the accompanying barriers to seamless data interaction. While dashboards are typically designed for data consumption, our findings show that dashboard users have far more diverse needs. To capture these activities, we frame data workers' practices as data conversations: conversations with data capture classic analysis (asking and answering data questions), while conversations through and around data involve constructing representations and narratives for sharing and communication. Dashboard users faced substantial barriers in their data conversations: their engagement with data was often intermittent, dependent on experts, and involved an awkward assembly of tools. We challenge the visualization and analytics community to embrace dashboard users as a population and design tools that blend seamlessly into their work contexts.

Identifying Visualization Opportunities to Help Architects Manage the Complexity of Building Codes.

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.

Untidy Data: The Unreasonable Effectiveness of Tables.

Working with data in table form is usually considered a preparatory and tedious step in the sensemaking pipeline; a way of getting the data ready for more sophisticated visualization and analytical tools. But for many people, spreadsheets - the quintessential table tool - remain a critical part of their information ecosystem, allowing them to interact with their data in ways that are hidden or abstracted in more complex tools. This is particularly true for data workers [61], people who work with data as part of their job but do not identify as professional analysts or data scientists. We report on a qualitative study of how these workers interact with and reason about their data. Our findings show that data tables serve a broader purpose beyond data cleanup at the initial stage of a linear analytic flow: users want to see and "get their hands on" the underlying data throughout the analytics process, reshaping and augmenting it to support sensemaking. They reorganize, mark up, layer on levels of detail, and spawn alternatives within the context of the base data. These direct interactions and human-readable table representations form a rich and cognitively important part of building understanding of what the data mean and what they can do with it. We argue that interactive tables are an important visualization idiom in their own right; that the direct data interaction they afford offers a fertile design space for visual analytics; and that sense making can be enriched by more flexible human-data interaction than is currently supported in visual analytics tools.

Visual Analytics: A Method to Explore Natural Histories of Oral Epithelial Dysplasia.

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.

What Do We Talk About When We Talk About Dashboards?

Dashboards are one of the most common use cases for data visualization, and their design and contexts of use are considerably different from exploratory visualization tools. In this paper, we look at the broad scope of how dashboards are used in practice through an analysis of dashboard examples and documentation about their use. We systematically review the literature surrounding dashboard use, construct a design space for dashboards, and identify major dashboard types. We characterize dashboards by their design goals, levels of interaction, and the practices around them. Our framework and literature review suggest a number of fruitful research directions to better support dashboard design, implementation, and use.

Personal Visualization and Personal Visual Analytics.

Data surrounds each and every one of us in our daily lives, ranging from exercise logs, to archives of our interactions with others on social media, to online resources pertaining to our hobbies. There is enormous potential for us to use these data to understand ourselves better and make positive changes in our lives. Visualization (Vis) and visual analytics (VA) offer substantial opportunities to help individuals gain insights about themselves, their communities and their interests; however, designing tools to support data analysis in non-professional life brings a unique set of research and design challenges. We investigate the requirements and research directions required to take full advantage of Vis and VA in a personal context. We develop a taxonomy of design dimensions to provide a coherent vocabulary for discussing personal visualization and personal visual analytics. By identifying and exploring clusters in the design space, we discuss challenges and share perspectives on future research. This work brings together research that was previously scattered across disciplines. Our goal is to call research attention to this space and engage researchers to explore the enabling techniques and technology that will support people to better understand data relevant to their personal lives, interests, and needs.

Exploring ambient and artistic visualization for residential energy use feedback.

Providing effective feedback on resource consumption in the home is a key challenge of environmental conservation efforts. One promising approach for providing feedback about residential energy consumption is the use of ambient and artistic visualizations. Pervasive computing technologies enable the integration of such feedback into the home in the form of distributed point-of-consumption feedback devices to support decision-making in everyday activities. However, introducing these devices into the home requires sensitivity to the domestic context. In this paper we describe three abstract visualizations and suggest four design requirements that this type of device must meet to be effective: pragmatic, aesthetic, ambient, and ecological. We report on the findings from a mixed methods user study that explores the viability of using ambient and artistic feedback in the home based on these requirements. Our findings suggest that this approach is a viable way to provide resource use feedback and that both the aesthetics of the representation and the context of use are important elements that must be considered in this design space.

Whisper, don't scream: grids and transparency.

Visual elements such as grids, labels, and contour lines act as reference structures that support the primary information being presented. Such structures need to be usefully visible, but not so obtrusive that they clutter the presentation. Visual designers know how to carefully manage transparency and layering in an image to balance these elements. We want the presentation of these structures in complex, dynamic, computer-generated visualizations to reflect the same subtlety and comfort of good design. Our goal is to determine the physical, perceptual, and cognitive characteristics of such structures in a way that enables automatic presentation. Our approach to this problem does not try to characterize "ideal" or "best," but instead seeks boundary conditions that define a range of visible yet subtle legibility. All presentations that are clearly bad lie outside of this range, and can easily be avoided. In this paper, we report three experiments investigating the effects of grid color and spacing on these boundary conditions, defined by manipulating the transparency (alpha) of thin rectangular grids over scatter plots. Our results show that while there is some variation due to user preference and image properties, bounding alpha allows us to reliably predict a range of usable yet unobtrusive grids over a wide variety of conditions.

The effect of colour and transparency on the perception of overlaid grids.

Overlaid reference elements need to be sufficiently visible to effectively relate to the underlying information, but not so obtrusive that they clutter the presentation. We seek to create guidelines for presenting such structures through experimental studies to define boundary conditions for visual intrusiveness. We base our work on the practice of designers, who use transparency to integrate overlaid grids with their underlying imagery. Previous work discovered a useful range of alpha values for black or white grids overlayed on scatterplot images rendered in shades of gray over gray backgrounds of different lightness values. This work compares black grids to blue and red ones on different image types of scatterplots and maps. We expected that the coloured grids over grayscale images would be more visually salient than black ones, resulting in lower alpha values. Instead, we found that there was no significant difference between the boundaries set for red and black grids, but that the boundaries for blue grids were set consistently higher (more opaque). As in our previous study, alpha values are affected by image density rather than image type, and are consistently lower than many default settings. These results have implications for the design of subtle reference structures.

Chasing the negawatt: visualization for sustainable living.

Energy and resource management is an important and growing research area at the intersection of conservation, sustainable design, alternative energy production, and social behavior. Energy consumption can be significantly reduced by simply changing how occupants inhabit and use buildings, with little or no additional costs. Reflecting this fact, an emerging measure of grid energy capacity is the negawatt: a unit of power saved by increasing efficiency or reducing consumption.Visualization clearly has an important role in enabling residents to understand and manage their energy use. This role is tied to providing real-time feedback of energy use, which encourages people to conserve energy.The challenge is to understand not only what kinds of visualizations are most effective but also where and how they fit into a larger information system to help residents make informed decisions. In this article, we also examine the effective display of home energy-use data using a net-zero solar-powered home (North House) and the Adaptive Living Interface System (ALIS), North House's information backbone.