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Data Visualization Literacy
Why Most People Can't Actually Read a Chart
Visualization literacy research shows a consistent pattern: people read simple charts at reasonable accuracy, but performance drops significantly with more complex forms — choropleth maps, scatter plots with encoded variables, dual axes, diverging scales. The research and policy visualizations that matter most tend to be the most complex. The audience that most needs to understand them tends to have the least training in reading them.
Reviewed existing research across data literacy, visual cognition, and education to build a framework for understanding where and why visualization comprehension fails. Mapped failure modes against common chart types and identified the interventions most supported by the evidence: annotation density, guided interpretation layers, and progressive disclosure of complexity.
- Situated DVL against digital, data, and AI literacies — each foundational to the next, with DVL specifically focused on evaluating, interpreting, and creating visualizations
- Synthesized the field's existing assessment infrastructure: the Visualization Literacy Assessment Test (VLAT, Lee et al. 2017) for per-chart diagnostics and the Data Visualization Literacy Framework (DVL-FW, Börner et al. 2019) for typology-based curriculum design
- Documented the field's central tension: widespread DVL also enables high-quality counternarratives (Lee et al. 2021 on COVID anti-masking visualizations), so chart-reading literacy has to be paired with source-evaluation and methodological critique
Making a chart is not the same thing as communicating data
The field spends enormous energy on making visualizations more sophisticated and elegant. I'd argue we'd get more value from making them more readable. A chart that's misunderstood by 60% of its audience hasn't done its job regardless of how well it's designed. Legibility and rigor should not be in tension.