proporr

Proporr is a term used in design and data visualization to describe a normative approach that prioritizes proportional representation of data values in visual encodings. It advocates that visual properties should map to data quantities in a way that preserves true relationships among values.

Etymology and meaning: The term is widely treated as a portmanteau of proportion and error (or proportion and mirror), signaling a policy of reducing perceptual distortion and misinterpretation when data are displayed.

Origins: Proporr arose in online discussions during the late 2010s among practitioners exploring accurate data displays. While not tied to a single organization, it has been referenced in forums, blogs, and some visualization guidelines as a principle rather than a strict rule.

Principles: Core ideas include using area to encode quantity where appropriate, preserving the relative ordering of values, avoiding non-proportional distortions in length, and ensuring that comparative judgments reflect the data. It also encompasses transparency about any perceptual biases introduced by chart types.

Applications and examples: Proporr informs the design of bar charts, pie or donut charts (with caveats), bubble charts with area-proportional sizing, treemaps with proportional areas, and data dashboards that rely on quick, accurate comparisons.

Reception and critique: As with other visual-analytic principles, proporr is subject to practical tradeoffs; readability, space, and audience familiarity may require deviations from strict proportionality. Proponents stress that explicit attention to proportional encoding improves accuracy, while critics note that exact proportionality can conflict with clarity or compactness in complex visuals.