data visualization

This work addresses the prevalence of misleading visualizations resulting from violations of fundamental design principles, a problem exacerbated by existing tools’ lack of contextual understanding and the unreliability of general-purpose large language models in providing actionable guidance. To bridge this gap, the authors propose a novel approach that integrates chart de-rendering, vision-language reasoning, and a knowledge base of visualization principles to reconstruct structured chart representations from images, identify design flaws, and generate interpretable, executable improvement suggestions. The system enables human-in-the-loop interactive optimization and re-rendering. Evaluated on 1,000 charts from the Chart2Code benchmark, it produced 10,452 recommendations, clustered into 10 categories—including axis formatting and color accessibility—demonstrating its effectiveness in enhancing both visualization quality and user literacy.

Existing visualization tools commonly embed data loading and transformation logic within visualization components, leading to redundant development efforts, high user learning costs, and difficulties in cross-tool data interoperability. Method: This paper proposes a modular architecture that systematically decouples data processing from visualization logic for the first time. It defines standardized data interfaces and a dynamic integration mechanism, implemented as a web-based prototype supporting bidirectional data flow and parallel collaboration across heterogeneous tools while preserving their autonomy. Contribution/Results: The architecture establishes a unified data interaction layer without compromising tool independence. Empirical evaluation demonstrates significant reductions in developers’ reimplementing overhead and users’ learning curves. Moreover, it enables scalable, open, and collaborative visualization ecosystems—introducing a novel, extensible paradigm for integrated visual analytics.

This study addresses the lack of systematic understanding regarding the creation, application, and organizational impact of data visualization style guides. Through interviews with nine authors from journalism, government, and industry, complemented by a cross-case analysis of 26 published guides, the paper proposes the PRISM socio-technical framework to elucidate their operational logic across four dimensions: Purpose, Rules and mechanisms, Institutional enforcers, and Situational flexibility. The findings reveal an inherent tension between standardization and adaptability, demonstrating that publicly available guides represent only partial manifestations of more comprehensive internal systems. By unpacking how these guides function in practice, the research offers both theoretical grounding and novel practical insights for the future development of visualization design standards.

This study identifies a systematic “interpretation gap” between designers’ communicative intent and audiences’ comprehension in data visualization. Employing a mixed-methods approach—including participatory workshops with data journalists, in-depth interviews across diverse audience groups (students, job seekers, designers, older adults), qualitative coding, and comparative analysis of intended versus perceived information—we empirically uncover three primary causes: ambiguous terminology, cognitive overload, and mismatches between visual conventions and audience expectations. From this, we derive six recurrent patterns of misinterpretation. Building on these findings, we formulate audience-cognition–informed chart design principles grounded in empirical evidence, offering actionable guidelines to enhance visualization interpretability. This work bridges a critical gap in human-centered visualization research from a communication perspective, advancing both theoretical understanding and practical design practice.

Existing visualization research predominantly focuses on *how to use* interactive features, neglecting the critical question of *how to construct* them. Method: We propose the first three-layer decoupled interaction authoring task model—intent–technique–component—derived from empirical coding and abstraction of 592 interaction units across 47 real-world applications. Contribution/Results: This model provides descriptive, evaluative, and generative capabilities, enabling the first unified formalization of interaction authoring intent, technical implementation, and component instantiation. It yields a reusable, theory-grounded classification framework that supports critical evaluation of existing visualization tools and informs the design and validation of next-generation low-code interaction authoring systems.

This study addresses the cognitive bias in scatterplots where “data-induced grouping”—arising from the interplay between data values and visual encoding—leads users to misinterpret spatial arrangements as meaningful patterns. Through two user studies, the authors systematically demonstrate the prevalence of this phenomenon, develop the first perceptual model capable of predicting whether users perceive a given set of points as a coherent group, and propose a visualization intervention strategy that integrates user perception with data reordering. Notably, the model effectively captures users’ tendency to group points based on trends even in nominal data contexts. Applied to visualization diagnosis and optimization, this approach significantly enhances the accuracy and reliability of graphical representations.

This study addresses the mismatch between assumed and actual user reading behaviors in dashboard design, where existing approaches often presume a fixed component viewing order. Through a mixed-methods investigation involving 18 designers and 16 users, the work systematically identifies and quantifies six key factors influencing interaction sequences: layout, visual salience, semantics, functional role, interactivity, and user context. Integrating interviews, behavioral logs, and sequential analysis, the research uncovers both consistent patterns and diverse strategies in how users navigate dashboards, revealing representative navigation pathways. These findings provide an empirical foundation and actionable design insights for computational modeling of dashboard comprehension and the development of intelligent guidance systems that adapt to real user behavior.

Magic: The Gathering—Commander players lack effective tools for match data analysis. Method: This study employs user task analysis, iterative visualization design (including heatmaps and line charts), and structured usability testing to derive dashboard design principles centered on contextual relevance, outcome orientation, and progressive disclosure. It prioritizes adaptability, customizability, and accuracy equally—departing from conventional generic dashboard paradigms. Contribution/Results: Empirical evaluation demonstrates that heatmaps and line charts significantly improve players’ comprehension efficiency of key metrics such as win rate and play tempo. Players strongly prefer localized views, context-driven metrics, and personalized configurations. The study culminates in a domain-specific visualization design guideline for trading card games (TCGs), offering both methodological foundations and empirical validation for context-aware, domain-adapted game analytics tool design.

This study addresses the lack of systematic research on data visualization design practices in real-world public health contexts, which hinders the evaluation of their effectiveness and suitability for public communication. For the first time, it collects and analyzes over 4,000 visualizations from the websites of more than 20 domestic and international public health agencies. Combining manual annotation with qualitative content analysis, the work systematically codes key features—including chart types, accessibility, decorative elements, and design flaws—and establishes the first reusable annotated dataset and classification framework for public health visualizations. The findings reveal prevalent design patterns and common shortcomings, offering empirical evidence to improve the communication of official health data. The study also publicly releases a visualization corpus to support future research in this domain.

This work addresses the limitations of existing data visualization tools, which often fail to support fine-grained visual customization while preserving data binding, thereby forcing users to resort to external graphic editors. To bridge this gap, we present TailVis, a system that extends the InfoVis reference model to encompass a post-rendering design phase. TailVis integrates element-level direct selection, natural language input, and dynamic GUI controls, augmented by referential interactions that enable data-aware, open-ended editing. The system maintains data-binding integrity through a dedicated preservation mechanism, employs an extensible selection model, and supports iterative design via provenance-aware history management. User studies demonstrate that TailVis significantly reduces repetitive operations and effectively enables expressive, data-consistent chart refinement within a unified environment.