Active Reading of Visualizations

We investigate whether the notion of active reading for text might be usefully applied to visualizations. Through a qualitative study we explored whether people apply observable active reading techniques when reading paper-based node-link visualizations. Participants used a range of physical actions while reading, and from these we synthesized an initial set of active reading techniques for visualizations. To learn more about the potential impact such techniques may have on visualization reading, we implemented support for one type of physical action from our observations (making freeform marks) in an interactive node-link visualization. Results from our quantitative study of this implementation show that interactive support for active reading techniques can improve the accuracy of performing low-level visualization tasks. Together, our studies suggest that the active reading space is ripe for research exploration within visualization and can lead to new interactions that make for a more flexible and effective visualization reading experience

Interaction for Immersive Analytics

International audienceIn this chapter, we briefly review the development of natural user interfaces and discuss their role in providing human-computer interaction that is immersive in various ways. Then we examine some opportunities for how these technologies might be used to better support data analysis tasks. Specifically, we review and suggest some interaction design guidelines for immersive analytics. We also review some hardware setups for data visualization that are already archetypal. Finally, we look at some emerging system designs that suggest future directions

Belief at first sight: Data visualization and the rationalization of seeing

Data visualizations are often represented in public discourse as objective proof of facts. However, a visualization is only a single translation of reality, just like any other media, representation devices, or modes of representation. If we wish to encourage thoughtful, informed, and literate consumption of data visualizations, it is crucial that we consider why they are often presented and interpreted as objective. We reflect theoretically on data visualization as a system of representation historically anchored in science, rationalism, and notions of objectivity. It establishes itself within a lineage of conventions for visual representations which extends from the Renaissance to the present and includes perspective drawing, photography, cinema and television, as well as computer graphics. By examining our tendency to see credibility in data visualizations and grounding that predisposition in a historical context, we hope to encourage more critical and nuanced production and interpretation of data visualizations in the public discourse.Natural Sciences and Engineering Research Council (NSERC)Alberta Innovates - Research GrantOthe

Discussing Open Energy Data and Data Visualizations with Canadians

Despite an abundance of data and prevalent open data initiatives from democratic governments, there are many unknowns about how to make open data truly accessible, engaging, and empowering to the general public. We present results from an interview study with 19 Canadians from diverse demographic and occupational backgrounds on their experiences, attitudes, and barriers regarding open government data and visualizations of open data, specifically in the energy domain. We observe among participants three categories of receptiveness to taking in new information on the topic of energy: Data-Interpretation-Receptive (DI-R), Interpretation-Receptive (I-R), and Data-Interpretation-Avoidant (DI-A). For each category, we unpack the barriers, values, and needs of participants, while identifying opportunities for open data and visualizations of open data to better inform, engage, and empower diverse members of the public. Our findings suggest a need for open data and open data visualizations for the public to move beyond a “one-size-fits-all” approach by considering the needs of data-interpretation-avoidant, interpretation-receptive, and data-interpretation-receptive as a step towards broadening the accessibly of open data.University of Calgary - Research Gran

Constructive Visualization

Best Paper Honorable MentionInternational audienceIf visualization is to be democratized, we need to provide means for non-experts to create visualizations that allow them to engage directly with datasets. We present constructive visualization a new paradigm for the simple creation of flexible, dynamic visualizations. Constructive visualization is simple--in that the skills required to build and manipulate the visualizations are akin to kindergarten play; it is expressive-- in that one can build within the constraints of the chosen environment, and it also supports dynamics -- in that these constructed visualizations can be rebuilt and adjusted. We de- scribe the conceptual components and processes underlying constructive visualization, and present real-world examples to illustrate the utility of this approach. The constructive visualization approach builds on our inherent understanding and experience with physical building blocks, offering a model that enables non-experts to create entirely novel visualizations, and to engage with datasets in a manner that would not have otherwise been possible.Si la visualisation doit être démocratisé, il faut concevoir des moyens engageants qui permettent aux personnes non-expertes de créer des visualisations. Nous présentons la *construction de visualisation* un nouveau paradigme pour la création simple de visualisations dynamiques, et flexibles. Ce paradigme est simple car les compétences ces nécessaires a mettre en œuvre pour construire et manipuler une visualisations sont semblables à celle développer à l'école maternelle; il est expressif - dans la mesure des contraintes de l'environnement choisi; et il permet également les mises à jour dynamique - les visualisations construites peuvent être reconstruits et adaptés. Nous décrivons les composants conceptuels et processus sous-jacents des visualisations constructives, et nous présentons des exemples concrets pour illustrer l'utilité de cette approche. L'approche de visualisation constructif s'appuie sur notre compréhension et notre expérience des manipulations de blocs de construction physique, offrant un modèle qui permet aux non-experts de créer entièrement de nouvelles visualisations, tout en s'engageant dans une activité de manipulation et d'analyse de données d'une façons qui n'aurait pas été possible autrement