From design spaces to visual designbookmark_border

Representational fidelity is routinely treated as a single abstract–realistic continuum, a simplification that limits how it is described and compared across research and design contexts. We introduce a multidimensional design space of representational fidelity for didactic visualization in science & engineering, inductively derived from a 175-item corpus spanning several disciplines, modalities, and instructional aims. The resulting design space specifies five dimensions: Morphological, Dynamic, Cueing, Contextual, and Interactive Fidelity, with seven sub-dimensions. We demonstrate the design space’s descriptive power through successive rounds of expansion and refinement and analyze the corpus to reveal relationships among dimensions and implications for design and research. We further validate the design space through a pilot focus group in which participants applied the dimensions in an open-ended design exercise. Resulting sketches and verbal rationales informed a single-designer applied case study, offering preliminary evidence of the design space's generative potential as a structured aid to design exploration. Together, these contributions lay the groundwork for future research and more intentional design practice.

Quantum circuit visualizations play an essential role in supporting sense-making and communication of quantum programs. While several tools exist for rendering quantum circuits, they vary widely in encoding options due to idiosyncrasies among machine and platform providers. We observe an opportunity to coalesce these disparate rendering approaches under a single, unified grammar to enable consistent, cross-platform enhancement of quantum circuit visualizations. However, it is unclear how to design such a grammar to best support the quantum computing community. Towards this end, we contribute a design space of quantum circuit visualizations by analyzing 182 static and 12 interactive cases collected from online tutorials and documentations, research publications, public presentations, and prior systems. Based on our analysis, we discuss how our design space relates to existing visualization principles yet exhibits unique aspects. We conclude with opportunities for future systems regarding data structure, cognition, and integrability.

To direct attention and communicate context, visualization designers often employ augmentations such as annotations, animated transitions, and stylistic layouts. While graphical perception principles can helpfully prescribe effective low-level representations of data, they lack guidance for augmentations that service higher-level communication goals. To bridge this gap, we contribute a design space that frames designer intent as viewer-oriented cognitive behaviors, grounding communicative aims in actionable visualization design techniques, including both visual encodings and augmentations. This design space consists of common augmentation tactics (annotation, animation, stylized encodings, etc.) that implement design strategies (spotlighting, sequencing, association, etc.) to achieve higher-level design goals (observe, interpret, introspect), often simultaneously. We demonstrate the analytic and generative value of our design space with examples across varied designer objectives. We discuss how our contributions help align designer intent with reader takeaways, pave the way for readers to learn more effectively, and enable future (semi-)automated systems to support visualization designers in achieving their communication goals.

Data visualization research has developed many influential forms of design knowledge, including perceptual principles, design guidelines, process models, and formalized representations of design constraints. These contributions have been effective at articulating explicit, portable, and codified forms of knowledge. Yet the broader landscape on which visualization design depends remains less clearly articulated, especially with respect to intermediate-level knowledge, precedents, tacit repertoires, and situated forms of knowing. In this paper, we draw on design theory to map this broader landscape of design knowledge in data visualization. Through this lens, we show how visualization research has built substantial strengths in some regions while leaving others comparatively underarticulated. We further argue that visualization design depends not only on knowledge artifacts such as theories, guidelines, and patterns, but also on knowledge-in-use---the situated interpretation, adaptation, and coordination of multiple forms of knowing in concrete design situations. This broader account has implications for how the field conceptualizes design expertise, evaluates and develops scholarly contributions, and approaches AI-assisted design. Rather than treating visualization design as either fully formalizable or wholly resistant to computational support, we argue for a differentiated view in which computational systems can support some forms of design knowing, while others remain inseparable from human judgment, contextual interpretation, and the ongoing reorganization of design work in practice.

With increasing evidence that affective connotation (emotional association) is an important aspect of visual communication, there is a need for methods to predict affective connotation of visualizations. Many aspects of visualization design, including colors, textures, and shapes, can contribute to affective connotation, and a key question is how multiple design properties combine to determine the emotion association of a whole visualization. In this study, we focused specifically on color and tested whether it is possible to predict the affective connotation of whole visualizations by aggregating the emotion associations of the individual, constituent colors (additivity hypothesis). We also tested whether accounting for the size of colored regions, as determined by the underlying dataset, improved predictions (data-dependence hypothesis). We found that for colormap data visualizations in which colors were well-distributed across all colors in the color scale, the mean estimated associations of individual colors effectively predicted emotional associations of the maps as a whole (additivity; Exp. 1). For colormaps whose underlying datasets were biased to map more to colors at one end of the color scale, emotional associations were better predicted by a weighted mean that accounted for color frequency in the colormap (data-dependence; Exp. 2). Effects of additivity and data-dependence generalized to dot plots and bar charts (Exp. 3). These results suggest it is viable to predict affective connotation of whole visualizations from their individual design components, which has important implications for automating affective visualization design to support visual communication.

In visualization, visual encodings map data attributes to perceptual channels such as position, color, and size, while visual fusion integrates multiple channels into a coherent representation within a shared frame of reference. In mathematics, sheaf theory provides a formal framework for relating local structure to global structure. In this paper, we introduce a sheaf-theoretic framework for analyzing visual encodings and visual fusion, and argue that this perspective advances the theoretical foundations of visualization. Through four case studies---including composite scientific visualizations, glyph-based scatter plots, and edge blending for graphs and hypergraphs---we demonstrate that sheaves provide a rigorous and unifying formulation of these processes. Motivated by sheaf-theoretic approaches to data fusion and grounded in these case studies, we propose a set of axioms that formalize the requirements for a sheaf-based theory of visual fusion. Together, our results establish a principled mathematical foundation for understanding and designing visual encodings and fusion as coherent local-to-global constructions.

Classically, affordance research investigates how the shape of objects communicates actions to potential users. Cognitive affordances, a subset of this research, characterize how the design of objects influences cognitive actions, such as information processing. Within visualization, cognitive affordances inform how graphs' design decisions communicate information to their readers. Although several related concepts exist in visualization, a formal translation of affordance theory to visualization is still lacking. In this paper, we review and translate affordance theory to visualization by formalizing how cognitive affordances operate within a visualization context. We also review common methods and terms, and compare related constructs to cognitive affordances in visualization. Based on a synthesis of research from psychology, human-computer interaction, and visualization, we propose a framework of cognitive affordances in visualization that enumerates design decisions and reader characteristics that influence a visualization's hierarchy of communicated information. Finally, we demonstrate how this framework can guide the evaluation and redesign of visualizations.