2024

PREVis: Perceived Readability Evaluation for Visualizations

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PREVis website
Download PREVis questionnaire and guidelines
Download research article (full paper with appendices)
See github code repository
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Although readability is recognized as an essential quality of data visualizations, so far there has not been a unified definition of the construct in the context of visual representations. As a result, researchers often lack guidance for determining how to ask people to rate their perceived readability of a visualization. To address this issue, we engaged in a rigorous process to develop the first validated instrument targeted at the subjective readability of visual data representations. Our final instrument consists of 11 items across 4 dimensions: understandability, layout clarity, readability of data values, and readability of data patterns.

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A cognitive load approach to designing and evaluating data visualizations

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Visual representations of data are increasingly prevalent, but we lack a detailed theoretical framework to explain what factors make easy or difficult to read and understand; nor do we know how such factors can impact data visualizations’ efficiency as learning material.

In this Master’s Thesis, I address this gap by exploring the validity and applicability of Cognitive Load Theory, an educational and cognitive science theoretical framework, for designing and evaluating data visualizations. Beyond empirical findings, I also contribute an interdisciplinary perspective on the cognitive processing of visualizations, and I discuss implications in assessing readability in visualization studies.

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A case to study the relationship between data visualization readability and visualization literacy (position paper)

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Only with reliable and relevant measures can we assess how a potential factor affects a reader’s performance; accordingly, only with appropriate measuring instruments can we start to investigate the tight web of interactions between individual characteristics, features of the visual design, and reading tasks requirements. As we slowly progress in our understanding of how people process information from data visualization, and based on these improved tools and other developments, we can further develop theoretical foundations in data visualization.

This position paper was accepted at ACM CHI24 Workshop: Toward a More Comprehensive Understanding of Visualization Literacy.

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Open Questions about the Visualization of Sociodemographic Data

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Triangle with "Efficiency", "Inclusiveness" and "Simplicity" as summits, representing the trade-offs for visualization of sociodemographic data.

The visualization research community engaged a reflection on how to represent data about people. This work collects a set of open research questions and highlights some future research directions.

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Florent Cabric, Margrét Vilborg Bjarnadóttir, Anne-Flore Cabouat, and Petra Isenberg. Open Questions about the Visualization of Sociodemographic Data. In Workshop on Visualization for Social Good (VIS4Good). IEEE. 2023.

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