data visualization designers

Abstract: 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.

Abstract: Science fiction often features characters or objects that undergo radical changes in form while still being recognizable as the same entity. For example, a vehicle might unfold into a robot, or one body might flow or shape-shift into another. We use such transformations as a reflective lens to study similar shape-shifting in visualization, where one visual representation transforms into a completely different one. For example, a node-link diagram might transform into a parallel coordinates plot as a user's task changes from understanding relationships to identifying correlations. Based on four instances of science-fiction transformation, our paper highlights two key questions: First, why is a transformation into a target form needed, or what is the purpose? Second, what remains recognizable across the transformation from source to target, or what is the correspondence? Through four hypothetical transitions between visual representations of a multifaceted dataset, we demonstrate that purpose is also essential to visualization transformations. Furthermore, we show that the question of correspondence results in different levels of complexity in terms of designing and understanding transitions. Depending on whether correspondence is 1:1, 1:N, or N:1, visualization transformations may be direct and easy to understand, or they may require more elaborate transition constructs, implying increased cognitive effort for interpretation. Particularly, cases of 1:N and N:1 correspondence pose challenges for designing visualization transformations.

Abstract: Communicative visualizations can support multiple chart-grounded takeaways, including divergent or conflicting ones; we refer to this variation as interpretive ambiguity. Such ambiguity can undermine a visualization's communicative goal when the supported takeaways diverge from its intended message. Evaluating visualizations with target audiences can surface this variation, but such evaluations can be costly and difficult to scale. In this work, we explore persona-conditioned Vision Language Models (VLMs) as a complementary evaluation tool for interpretive ambiguity. For a given visualization, our pipeline uses topic-relevant personas to elicit candidate contextual takeaways supported by interpretations of perceived visual patterns, such as trends and clusters. We derive a Target--Pattern--Scope framework for comparing these interpretations, use LLM judges to assign Compatible, Disjoint, or Opposing ambiguity labels to statement pairs, and summarize their distribution in an ambiguity profile. To improve chart grounding, we benchmark candidate VLMs on chart comprehension and select the highest-performing model for each chart type. We evaluate statement-pair labeling against human-authored annotations and apply the approach to real-world, public-facing visualizations curated from established datasets. Through case studies, we examine how perceived visual patterns relate to contextual takeaways and how a visualization redesign changes the sampled interpretations. Our findings show how persona-conditioned VLMs can complement human feedback by generating candidate contextual takeaways and surfacing potential ambiguity for further inspection.

Abstract: In the classical necklace splitting problem, beads of different colors lie on a line, and the goal is to divide them among $k$ agents using as few cuts as possible so that every agent receives the same number of beads of each color. We study the planar analog in which a cut is a full straight line and the objective is minimizing the number of lines: $m$ points colored red and blue must be separated by straight-line cuts into regions that can be allocated to $k$ agents so that each agent receives the same number of points of each color. Projection to a line gives $2(k-1)$ cuts, and $k-1$ are sometimes necessary; we give algorithms, static and dynamic, that provably close part of this gap. We develop several approaches that capitalize on the extra degree of freedom: a ``star'' search that produces solutions with as few as $\lceil \frac{k}{2} \rceil$ cuts and never more than $k$ whenever a fair contiguous circular partition exists, with further guarantees for evenly spaced input, and a general algorithm that produces at most $k + \log_2(k+1) - 2$ cuts in $Θ(m \log k)$ time, built on a one-dimensional necklace splitting subroutine that requires no presorting. We then adapt the general algorithm to a dynamic setting, maintaining exact fairness and the cut bound under arbitrary relocation of points, with expected update time that scales with the total distance moved.

Abstract: Visualization research has long examined embellishment and animation, while the design of rich interaction-contingent feedback remains under-articulated as a systematic design space. We introduce JuicyVIS, a theory-informed operationalization of juicy feedback for interactive visualization that translates ideas from game studies into a visualization-centered framework. JuicyVIS introduces three dimensions, interaction type, feedback timing, and feedback intensity, which we explore through 26 controlled prototypes, grounded in established visualization interaction categories and prior work on juicy feedback in games. We evaluate the juicy design space through three exploratory mixed-methods online studies. Results show that juicy feedback was consistently associated with higher engagement and aesthetic experience, while the clearest gains came from post-interaction feedback, not, for example, by simply increasing intensity. We discuss how juicy feedback can be thought of as a design material in visualizations, shaping how visualization interactions feel, how action consequences are perceived, and how users remain engaged with data representations.

Abstract: We adapt Stevens's power law to measure the innate ability of AI models to read visualizations, which can reveal the built-in perceptual mechanisms of algorithmic models. In our pilot study, models see no legend. A model first views a reference visual representation and estimates its magnitude, then estimates the magnitude of each subsequent image of the same representation relative to that reference. Our evaluation of twelve visual variables makes how algorithmic models read visual encodings measurable, comparable with human perception, and more interpretable to humans.

Abstract: Opacity is a widely used channel in data visualization, but it remains less well understood compared to channels such as color, length, size, etc. Recent work from Meng et al. investigated the impact of opacity across competing color schemes, finding that certain color schemes were associated with better participant accuracy. We examine these effects further in a controlled two-alternative forced-choice setup to determine whether opacity differences are truly equal across possible opacity comparison ranges. In a within-subjects study with 96 trials, including two competing color schemes (best and worst from Meng et al.) and 48 opacity pairs, we find little differences between color schemes but larger individual differences in accuracy. Further, results show stable performance in middle opacity ranges, with more errors occurring when comparing extreme values. We discuss potential implications for design guidelines and further study and make our study materials, analysis scripts, and data available at https://osf.io/zv9dx/overview?view_only=38d03cea1b3d42788593e3e6b1016cfd.

Abstract: Large language models (LLMs) are enticing in their promise to support data visualization (Vis) through faster and simpler workflows for data prep, analysis, and visualization creation. Yet LLMs are notoriously error-prone and not built for data visualization tasks. Few studies have explored LLM adoption among Vis practitioners. To fill this gap, we conducted semi-structured interviews with members of the Data Visualization Society, a global community of data visualization designers. Our findings show that Vis designers actively use LLMs for both creative and technical aspects of the visualization process. A new visualization workflow is emerging, a process we call vibe analysis, analogous to vibe coding. Some key challenges raised by participants parallel those of vibe coding, while others are Vis-specific, like gaps in Vis knowledge and chart verification. This work opens up opportunities for research combining LLM-mediated work with Vis tools that incorporate data visualization guidance, constraints, and best practices.

Abstract: Foundational frameworks in visualization have operationalized literacy as an individual competency, measured through chart-comprehension tasks. This focus raises a question: why has measurement become the dominant frame for understanding literacy? Rather than asking whether literacy should be measured, we ask how measurement became the field's way of understanding it. We trace visualization literacy back through the history of textual literacy and argue that in adopting the term, the field imported three values rooted in early government statistics: quantification, individualization, and binary classification. To mark the shift this history makes possible, we distinguish two waves: a first centered on assessment and individual proficiency, and a second that treats literacy as a situated practice, attentive to how people use visualizations in context and to the purposes they serve. Examining the construct this way suggests that refining assessment instruments does not, by itself, settle what literacy is, and points to directions for second-wave research: formative studies of literacy in context, culturally grounded instruments, and critical reading. This paper shows that visualization literacy inherited its measurement frame rather than discovering it, offers a two-wave vocabulary distinguishing assessment-based literacy from situated practice, and connects the field to a critical-literacy tradition with concrete alternatives to measurement. These contributions allow us to treat measurement and meaning as two parts of a single question, shifting attention from who counts as literate to what literacy is meant to do.

Abstract: This study examines data visualization design evolution over 12.5 years, reflecting on the impact of Large Language Models over the last 3.75 years. Using a longitudinal corpus of 55 visualizations from a single-subject design record, the study identifies how LLMs have aided design-space exploration: reducing coding effort, enabling new design opportunities, shock, excitement, accomplishments, and shifts to the design process.