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Deceptive visualizations are visualizations that, whether intentionally or not, lead the reader to an understanding of the data which varies from the actual data. Examples of deceptive visualizations can be found in every digital platform, and, despite their widespread use in the wild, there have been limited efforts to alert laypersons to common deceptive visualization practices. In this paper, we present a tool for annotating line charts in the wild that reads line chart images and outputs text and visual annotations to assess the line charts for distortions and help guide the reader towards an honest understanding of the chart data. We demonstrate the usefulness of our tool through a series of case studies on real-world charts. Finally, we perform a crowdsourced experiment to evaluate the ability of the proposed tool to educate readers about potentially deceptive visualization practices.
Designing a data physicalization requires a myriad of different considerations. Despite the cross-disciplinary nature of these considerations, research currently lacks a synthesis across the different communities data physicalization sits upon, including their approaches, theories, and even terminologies. To bridge these communities synergistically, we present a design space that describes and analyzes physicalizations according to three facets: context (end-user considerations), structure (the physical structure of the artifact), and interactions (interactions with both the artifact and data). We construct this design space through a systematic review of 47 physicalizations and analyze the interrelationships of key factors when designing a physicalization. This design space cross-pollinates knowledge from relevant HCI communities, providing a cohesive overview of what designers should consider when creating a data physicalization while suggesting new design possibilities. We analyze the design decisions present in current physicalizations, discuss emerging trends, and identify underlying open challenges.
Physicalizations represent data through their tangible and material properties. In contrast to screen-based visualizations, there is currently very limited understanding of how to label or annotate physicalizations to support people in interpreting the data encoded by the physicalization. Because of its spatiality, contextualization through labeling or annotation is crucial to communicate data across different orientations. In this paper, we study labeling approaches as part of the overall construction process of bar chart physicalizations. We designed a toolkit of physical tokens and paper data labels and asked 16 participants to construct and contextualize their own data physicalizations. We found that (i) the construction and contextualization of physicalizations is a highly intertwined process, (ii) data labels are integrated with physical constructs in the final design, and (iii) these are both influenced by orientation changes. We contribute with an understanding of the role of data labeling in the creation and contextualization of physicalizations.
Data visualizations are now widely used across many disciplines. However, many of them are not easily accessible for visually impaired people. In this work, we use three-staged mixed methods to understand the current practice of accessible visualization design for visually impaired people. We analyzed 95 visualizations from various venues to inspect how they are made inaccessible. To understand the rationale and context behind the design choices, we also conducted surveys with 144 practitioners in the U.S. and follow-up interviews with ten selected survey participants. Our findings include the difficulties of handling modern complex and interactive visualizations and the lack of accessibility support from visualization tools in addition to personal and organizational factors making it challenging to perform accessible design practices.
Matrix visualizations are widely used to display large-scale network, tabular, set, or sequential data. They typically only encode a single value per cell, e.g., through color. However, this can greatly limit the visualizations' utility when exploring multivariate data, where each cell represents a data point with multiple values (referred to as details). Three well-established interaction approaches can be applicable in multivariate matrix visualizations (or MMV): focus+context, pan&zoom, and overview+detail. However, there is little empirical knowledge of how these approaches compare in exploring MMV. We report on two studies comparing them for locating, searching, and contextualizing details in MMV. We first compared four focus+context techniques and found that the fisheye lens overall outperformed the others. We then compared the fisheye lens, to pan&zoom and overview+detail. We found that pan&zoom was faster in locating and searching details, and as good as overview+detail in contextualizing details.