High Fidelity Visualization: Parameter Mapping as an Analytical Tool for Assessing Structural Correspondence

Authors

Carmen Hull (Northeastern University), Jagoda Walny (Independent Researcher)

Presentation

Session

Form Follows Function

Time

Tuesday, Nov 10, 10:54 – 11:03 (US/Eastern) · session 10:00 – 11:30

Location

Hall Essex north

Keywords

Design methodology, data visualization, fidelity, structural correspondence, data physicalization, sonification.

Abstract

Encoding theory provides principled guidance for mapping data types to visual channels, but it operates at the data level. Whether a representation’s structural form corresponds to the structure of the phenomenon it depicts remains a separate question; one the field currently lacks a tool to assess. We name structural correspondence as critical theory for visualization design, define fidelity as the degree of structural correspondence between a representation’s parameters and the structural properties of the phenomenon it depicts, and propose parameter mapping — systematic assessment of each parameter of a representation against the structural properties of its phenomenon — as the analytical tool for assessing it. For each parameter, the method asks: what structural property of the phenomenon does it correspond to (fidelity gained)? What structural dimensions are absent or collapsed (fidelity lost)? What relationships does the representation introduce that the phenomenon does not have (false structure)? Applied to six existing representations spanning visualization, sonification, and physicalization, the framework surfaces patterns of structural and arbitrary coupling, deliberate abstraction, and false structure that current methods do not detect.

For Practitioners

Visualization designers, sonification designers, and data physicalization practitioners face representational choices that encoding theory alone does not resolve: whether a representation's structural form corresponds to the phenomenon it depicts. Parameter mapping gives these practitioners a design-time tool for assessing candidate representations before committing to construction. By listing each parameter of a proposed form and asking what it corresponds to, what it collapses, and what it introduces, designers can identify false structure, distinguish deliberate abstraction from ungrounded fidelity loss, and make structural commitments visible to collaborators. The framework is particularly relevant to data journalists and science communicators who select representational forms for public audiences, where imposed structure can shape interpretation in ways neither designer nor viewer may recognize. Domain experts collaborating with visualization teams can use the phenomenon column of the parameter mapping table to surface structural properties the designer may not independently know, making the tool a shared vocabulary for interdisciplinary design.