Formal representations of the visualization design space, such as knowledge bases and graphs, consolidate design practices into a shared resource and enable automated reasoning and interpretable design recommendations. However, prior approaches typically depend on fixed, manually authored rules, making it difficult to build novel representations or extend them for different visualization domains. Instead, we propose data-driven methods that automatically synthesize visualization design knowledge bases. Specifically, our methods (1) extract candidate design features from a visualization corpus, (2) select features forward and backward, and (3) render the final knowledge base. In our benchmark evaluation compared to Draco 2, our synthesized knowledge base offers general and interpretable design features and improves the accuracy of predicting effective designs by 1–15% in varied training and test sets. When we apply our approach to genomics visualization, the synthesized knowledge base includes sensible features with accuracy up to 97%, demonstrating the applicability of our approach to other visualization domains.
Storytelling infographics are a powerful medium for communicating data-driven stories through visual presentation. However, existing authoring tools lack support for maintaining story consistency and aligning with users' story goals throughout the design process. To address this gap, we conducted formative interviews and a quantitative analysis to identify design needs and common story-informed layout patterns in infographics. Based on these insights, we propose a narrative-centric workflow for infographic creation consisting of three phases: story construction, visual encoding, and spatial composition. Building on this workflow, we developed InfoAlign, a human–AI co-creation system that transforms long or unstructured text into stories, recommends semantically aligned visual designs, and generates layout blueprints. Users can intervene and refine the design at any stage, ensuring their intent is preserved and the infographic creation process remains transparent. Evaluations show that InfoAlign preserves story coherence across authoring stages and effectively supports human–AI co-creation for storytelling infographic design.
Existing frameworks in visualization and HCI emphasize iteration, data grounding, and stakeholder needs; however, they have not fully explored how evaluation might persist across phases, adapt to compressed timelines, and aid stakeholder engagement and elicitation. Building on prior frameworks, we introduce an evaluation-first design that centers evaluation as a material component in the design process, expanding evaluation to include when it occurs, who participates, how results inform design, and how metrics anchor stakeholder engagement and adoption. Evaluation-first design (EvalOps) emphasizes tighter feedback loops, co-evaluation with stakeholders, malleable forms of evaluation, and goals-to-metrics grounding. We illustrate how EvalOps shapes design outcomes through two case studies of data-visualization and LLM-enabled reasoning tools, demonstrating how evaluation-driven design facilitates alignment and trust, uncovers opportunities earlier, and supports cohesiveness under rapidly changing constraints. We contrast EvalOps with current visualization design methodologies and discuss opportunities for expanding evaluation-centered framings to other active areas of design research.
Should you place three pie charts side by side, or should you avoid pie charts altogether? Publicly available visualization style guides offer contradictory answers to such questions. Despite their growing influence on how people encounter data, these guides are seldom studied as a collective phenomenon. Addressing this gap, this paper presents the first systematic analysis of 53 publicly accessible visualization style guides from diverse domains, including journalism, government, non-profit, corporate, and academic sectors. We build a standardized corpus, conduct a multi-method analysis that reveals both consensus and contradiction, and develop a companion Guidelines Explorer to support transparency and future use. This work sheds light on organizational visualization design norms and provides a foundation for future work that helps bridge the gap between academic and industry practices. In doing so, we help reframe style guides as sociotechnical artifacts that encode values as much as design rules.
Data visualization practitioners routinely invoke inspiration, yet we know little about how it is constructed in public conversations. We conduct a discourse analysis of 31 episodes from five popular data visualization podcasts. Podcasts are public-facing and inherently performative: guests manage impressions, articulate values, and model “good practice” for broad audiences. We use this performative setting to examine how legitimacy, identity, and practice are negotiated in community talk. We show that “inspiration talk” is operative rather than ornamental: speakers legitimize what counts, who counts, and how work proceeds. Our analysis surfaces four adjustable evaluation criteria by which inspiration is judged—novelty, authority, authenticity, and affect—and three operative metaphors that license different practices—spark, muscle, and resource bank. We argue that treating inspiration as a boundary object helps explain why these frames coexist across contexts. Findings provide a vocabulary for examining how inspiration is mobilized in visualization practice, with implications for evaluation, pedagogy, and the design of galleries and repositories that surface inspirational examples.
Text plays a fundamental yet understudied role as a narrative device in data visualization. While existing research has extensively explored text as data input and interaction modality, its function in supporting storytelling and interpretation remains fragmented. To address this gap, this work presents a systematic review of 98 publications that provide insights into using text as narrative. We investigate how text can be utilized in visualization, analyze its functions and effects, and explore how it can be designed to facilitate data communication. Our synthesis identifies significant research gaps in this domain and proposes future directions to advance the integration of text and visualization, ultimately aiming to provide guidance for designing text that enhances narrative clarity and fosters engagement.
Visual metaphors illuminate infographics by leveraging graphical representations from more familiar source domains (e.g., a dandelion) to explain concepts in more abstract target domains (e.g., information propagation). However, designing effective visual metaphors remains a challenge, especially for novice designers, because it requires selecting a suitable source concept for the target concept and devising a reconstruction strategy that maps the source concept to the target concept. Through a systematic review of 2,029 metaphoric infographics, we derive a design space that characterizes visual metaphors across three dimensions: target, source, and reconstruction strategy. We demonstrate the utility of our design space by transforming it into actionable design knowledge for prompting generative models in metaphor ideation. A user study with 30 participants shows that design-space-augmented prompting generates more diverse and inspiring metaphor designs than direct prompting without design-space cues.