Abstract

We report on three years of shared reflection among nine colleagues with different but complementary backgrounds, roles, and career stages who share a common interest in and commitment to visualization research, teaching, and learning. In our international community of practice, we reflect on our visualization belief systems through critical visualization activities that help us consider, develop and question our values in the context of our distinct personal and professional lives. To ground our research, we review the history of critical and reflective visualization. We suggest that reflective visualization practices offer opportunities for (1) deepening the role that value systems play in visualization teaching, research, and practice (including ethical, cultural, and personal values), (2) developing pedagogical activities in which diverse approaches to data visualization are used to acknowledge the role of values, and (3) nurturing critical reflective practices across communities of students, teachers, practitioners, and researchers. We anticipate that these opportunities are widely applicable to data visualization. These reflective practices may benefit from approaches that encourage surfacing the diverse values of visualization. We hope our sharing and reflection can contribute to a vibrant, ongoing discussion of the role of values in visualization research, teaching, and practice; drive the creation and sharing of new activities and approaches; and encourage the formation of new reflective and supportive groups within our larger visualization community.

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Abstract

We take the well-established use of physical scale models in architecture and identify new opportunities for using them to interactively visualize and examine multiple streams of geospatial data. Overlaying, comparing, or integrating visualizations of complementary data sets in the same physical space is often challenging given the constraints of various data types and the limited design space of possible visual encodings. Our vision of “simultaneous worlds” uses physical models as a substrate upon which visualizations of multiple data streams can be dynamically and concurrently integrated. To explore the potential of this concept, we created three design explorations that use an illuminated campus model to integrate visualizations about building energy use, climate, and movement paths on a university campus. We use a research through design approach, documenting how our interdisciplinary collaborations with domain experts, students, and architects informed our designs. Based on our observations, we characterize the benefits of models for 1) situating visualizations, 2) composing visualizations, and 3) manipulating and authoring visualizations. Our work highlights the potential of physical models to support embodied exploration of spatial and non-spatial visualizations through fluid interactions.

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Abstract

Complex data visualization design projects often entail collaboration between people with different visualization-related skills. For example, many teams include both designers who create new visualization designs and developers who implement the resulting visualization software. We identify gaps between data characterization tools, visualization design tools, and development platforms that pose challenges for designer-developer teams working to create new data visualizations. While it is common for commercial interaction design tools to support collaboration between designers and developers, creating data visualizations poses several unique challenges that are not supported by current tools. In particular, visualization designers must characterize and build an understanding of the underlying data, then specify layouts, data encodings, and other data-driven parameters that will be robust across many different data values. In larger teams, designers must also clearly communicate these mappings and their dependencies to developers, clients, and other collaborators. We report observations and reflections from five large multidisciplinary visualization design projects and highlight six data-specific visualization challenges for design specification and handoff. These challenges include adapting to changing data, anticipating edge cases in data, understanding technical challenges, articulating data-dependent interactions, communicating data mappings, and preserving the integrity of data mappings across iterations. Based on these observations, we identify opportunities for future tools for prototyping, testing, and communicating data-driven designs, which might contribute to more successful and collaborative data visualization design.

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