Jagoda Walny’s research while affiliated with University of Calgary and other places

Despite an abundance of open data initiatives aimed to inform and empower “general” audiences, we still know little about the ways people outside of traditional data analysis communities experience and engage with public data and visualizations. To investigate this gap, we present results from an in-depth qualitative interview study with 19 participants from diverse ethnic, occupational, and demographic backgrounds. Our findings characterize a set of lived experiences with open data and visualizations in the domain of energy consumption, production, and transmission. This work exposes information receptivity - an individual's transient state of willingness or openness to receive information - as a blind spot for the data visualization community, complementary to but distinct from previous notions of data visualization literacy and engagement. We observed four clusters of receptivity responses to data- and visualization-based rhetoric: Information-Avoidant, Data-Cautious, Data-Enthusiastic, and Domain-Grounded. Based on our findings, we highlight research opportunities for the visualization community. This exploratory work identifies the existence of diverse receptivity responses, highlighting the need to consider audiences with varying levels of openness to new information. Our findings also suggest new approaches for improving the accessibility and inclusivity of open data and visualization initiatives targeted at broad audiences. A free copy of this paper and all supplemental materials are available at https://OSF.IO/MPQ32.</uri

Despite an abundance of open data initiatives aimed to inform and empower “general” audiences, we still know little about the ways people outside of traditional data analysis communities experience and engage with public data and visualizations. To investigate this gap, we present results from an in-depth qualitative interview study with 19 participants from diverse ethnic, occupational, and demographic backgrounds. Our findings characterize a set of lived experiences with open data and visualizations in the domain of energy consumption, production, and transmission. This work exposes information receptivity — an individual’s transient state of willingness or openness to receive information — as a blind spot for the data visualization community, complementary to but distinct from previous notions of data visualization literacy and engagement. We observed four clusters of receptivity responses to data- and visualization-based rhetoric: Information-Avoidant, Data-Cautious, Data-Enthusiastic, and Domain-Grounded. Based on our findings, we highlight research opportunities for the visualization community. This exploratory work identifies the existence of diverse receptivity responses, highlighting the need to consider audiences with varying levels of openness to new information. Our findings also suggest new approaches for improving the accessibility and inclusivity of open data and visualization initiatives targeted at broad audiences.

We present PixelClipper, a tool built for facilitating data engagement events. PixelClipper supports conversations around visualizations in public settings through annotation and commenting capabilities. It is recognized that understanding data is important for an informed society. However, even when visualizations are available on the web, open data is not yet reaching all audiences. Public facilitated events centred around data visualizations may help bridge this gap. PixelClipper is designed to promote discussion and engagement with visualizations in public settings. It allows viewers to quickly and expressively extract visual clippings from visualizations and add comments to them. Ambient and facilitator displays attract attention by showing clippings. They function as entry points to the full visualizations while supporting deeper conversations about the visualizations and data. We describe the design goals of PixelClipper, share our experiences from deploying it, and discuss its future potential in supporting data visualization engagement events.

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.

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.

Constructive approaches to visualization authoring have been shown to offer advantages such as providing options for flexible outputs, scaffolding and ideation of new data mappings, personalized exploration of data, as well as supporting data understanding and literacy. However, visualization authoring tools based on a constructive approach do not scale well to larger datasets. As construction often involves manipulating small pieces of data and visuals, it requires a significant amount of time, effort, and repetitive steps. We present ReConstructor, an authoring tool in which a visualization is constructed by instantiating its structural and functional components through four interaction elements (objects, modifiers, activators, and tools). This design preserves most of the benefits of a constructive process while avoiding scalability issues by allowing designers to propagate individual mapping steps to all the elements of a visualization. We also discuss the perceived benefits of our approach and propose avenues for future research in this area.

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.

Sketching is universal. It enables us to work through problems, communicate complexity, work with people who have diverse needs, and document work processes we employ within Human-Computer Interaction. Increased interest in sketching as a methodology within HCI has led to increased attendance of interactive courses, meet-ups, and discussion groups, from those who are complete beginners, to seasoned researchers with the skills and knowledge to support others. By bringing together these individuals, we are able to advance the understanding of how sketching underpins research, and how we might work with sketching as technology advances. SketCHI 2.0 aims to support ongoing discussions and collaborations around sketching in HCI, and further build the Sketching HCI community. As well as drawing on location, feedback, and discussion, we will form collaborative working groups to further our collective interest in this area and conduct high-level discussions about the practical applications and outputs of sketching in HCI.

For this demo, we will show two interactive visualizations: Energy Futures and Pipeline Incidents. We designed and developed these visualizations as part of an open data initiative that aims to create interactive data visualizations to help make Canada's energy data publicly accessible, transparent, and understandable. This work was conducted in collaboration with the National Energy Board of Canada (NEB) and a visualization software development company, VizworX.

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.