V-FRAMER: Visualization Framework for Mitigating Reasoning Errors in Public Policy
Résumé
Existing data visualization design guidelines focus primarily onconstructing grammatically-correct visualizations that faithfullyconvey the values and relationships in the underlying data. However,a designer may create a grammatically-correct visualizationthat still leaves audiences susceptible to reasoning misleaders, e.g.by failing to normalize data or using unrepresentative samples. Reasoningmisleaders are especially pernicious when presenting publicpolicy data, where data-driven decisions can affect public health, safety, and economic development. Through textual analysis, aformative evaluation, and iterative design with 19 policy communicators,we construct an actionable visualization design framework,V-FRAMER, that effectively synthesizes ways of mitigating reasoningmisleaders. We discuss important design considerations forframeworks like V-FRAMER, including using concrete examplesto help designers understand reasoning misleaders, and using ahierarchical structure to support example-based accessing. We furtherdescribe V-FRAMER’s congruence with current practice andhow practitioners might integrate the framework into their existingworkflows. Related materials available at: https://osf.io/q3uta/.
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