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Visual analytics produces provenance graphs that capture analytic states and their semantic relationships. To communicate findings derived from such exploration, data narratives have proven effective by organizing key insights into coherent sequences accompanied by visualizations. However, to our knowledge, this task still lacks a benchmark dataset, hindering controlled comparison and systematic evaluation of existing methods. To bridge this gap, we present ProvTales, the first large-scale dataset for converting visual exploration histories into data narratives. ProvTales follows a narrative-first, provenance graph-second construction pipeline. Specifically, we first use large language models (LLMs) to generate narratives progressively through topic planning, blueprint design, and data-fact instantiation. Next, we treat each narrative as a backbone and expand it into a provenance graph by introducing auxiliary non-narrative nodes that emulate realistic exploratory behaviors. The ProvTales further defines constrained and unconstrained settings based on whether an explicit communication intent is available. Comprehensive statistical analyses and evaluations demonstrate the quality and diversity of ProvTales. Finally, we discuss limitations in data construction and possible directions for future improvement. The dataset is publicly available at https://huggingface.co/datasets/ZtZheng/ProvTales.

Geographic narrative visualization integrates maps, charts, and explanatory text into cohesive spatial stories and plays an important role in journalism, urban planning, and public communication. However, creating these narratives still requires collaboration among GIS specialists, data analysts, and web developers, creating a high barrier to adoption. Existing visualization tools rely on static dashboards or rigid templates with steep learning curves, while large language models (LLMs) alone struggle to produce spatially grounded, visually coherent storytelling pages. To bridge this gap, we introduce GeoNarrative, an LLM-augmented system that transforms natural-language queries into multi-module narrative pages composed of text, maps, and charts. GeoNarrative includes three components: (i) an agent-based scheduling module for intent understanding and spatial data retrieval, (ii) a CLIP-based layout retriever that selects professionally inspired page templates, and (iii) a generative visualization engine that populates content while preserving narrative coherence. A built-in provenance mechanism lets users trace every data element to its origin, whether a named authoritative source or the generating model, supporting transparency and trust. We evaluate GeoNarrative through a controlled comparative experiment (N=16), an expert usability study (N=8), and a component-level ablation study. The results indicate that GeoNarrative produces narrative pages with stronger information flow and visual coherence than baseline methods, with large effect sizes, and that each architectural component contributes meaningfully to overall quality; given the modest sample sizes we regard these findings as promising rather than definitive. We also release an annotated dataset of 5,321 modules from 199 real-world narrative visualization pages to support future research.

Visualizations are widely used to communicate insights about data, and many authoring tools now exist to simplify the chart creation process. Unfortunately, when datasets contain sensitive information, such charts and tools can risk privacy exposure. For many dataset owners, it is a significant challenge to balance (i) effectively representing desired insights and (ii) ensuring privacy when visualizing their data. We investigate this by studying how to integrate privacy mechanisms into the chart authoring process. Based on a pre-study with domain experts, we design a compact-yet-inclusive declarative grammar for specifying differentially private visualizations. Using this, we develop Counterbalance, an authoring tool that supports creating privacy-preserving charts in a declarative manner. Notably, Counterbalance employs a novel scheme recommendation model to identify the implementation details of differential privacy approaches that successfully achieve user-defined goals such as preserving sensitive information while still effectively communicating desired insights. The tool also supports post-generation feedback on goal achievement as a way to validate goal achievement and mitigate potential misuse (such as inadvertent deductive disclosure) based on ambiguous declarations. Based on our process of designing, implementing, and empirically evaluating Counterbalance, we discuss how declarative approaches can support data owner accessibility, provide security guarantees, and promote human-in-the-loop privacy-preserving visualization.

Social safety plays a critical role in queer travel, as discrimination at destinations can lead to restrained expression, exclusion, or physical harm. Yet information relevant to safer travel decisions remains fragmented across formal sources, personal networks, and unstructured online accounts. To address this gap, we conducted a design study with queer participants to investigate how social safety is perceived in travel decision making, what information is missing from existing resources, and how community experiences can complement formal data. Based on these insights, we developed QueerTravel, an interactive visualization tool that combines curated social safety indicators with community-contributed experiences for 203 countries. The system supports overview, comparison, and inspection through coordinated map-based, tabular, and comparative views, enabling users to examine destinations across criteria such as same-sex marriage laws, transgender legal status, morality laws, and violence-related indicators alongside structured, location-specific accounts of lived experiences. In a subsequent study, participants used QueerTravel to reason about destinations under social safety considerations and reflect on the value, limits, and sensitivities of combining institutional and experiential evidence in one decision-support environment. Our work contributes insights into designing multi-criteria decision making (MCDM) visualization tools for queer travel safety, showing how experiential knowledge can complement formalized data and shifting attention from risk and vulnerability toward participation and empowerment of marginalized populations. Supplementary materials are available at https://osf.io/2mvb4/overview?view_only=3db8245eccf44c929d3e2de17d63a3c4X.

We investigate how to support authoring narrative visualizations in motion in sports videos, drawing on automated data preparation, systematic analysis, technology probe design, and evaluation, using swimming races as a case study. Sports videos are widely broadcast and shared across social media, where content creators increasingly seek to present and explain complex events to general audiences. Visualization in motion has been explored as an efficient way to embed data into videos and to move with the data referents, providing additional information and helping audiences understand races. However, existing approaches primarily focus on embedding visualizations in videos, lacking exploration of how to support authoring narratives that coordinate views, data, and temporal progression to explain the unfolding races. To address this gap, we use swimming videos as an ideal case for exploration, as swimming is a sport with rich, dynamic data and visualizations in practice. We develop an automated pipeline that extracts structured data from videos, derive narrative constructs through observational analysis of sports broadcasts, and design a technology probe that supports authoring using data prepared by our pipeline and narrative constructs derived from our observations. We evaluate our approach with experienced content creators and/or graphic designers to examine the benefits and challenges of authoring narrative visualizations in motion. All supplemental materials are described in the Supplemental Material Pointers section and are on OSF: osf.io/bq47n/.

This paper assesses how visualization badges---small informative visual labels placed alongside visualizations---affect people’s ability to read, interpret, and learn from visualizations. Similar to visual labels on product packaging, visualization badges highlight potentially relevant information to the reader, such as pointing to a `Major Finding', cautioning a `Truncated Axis', or informing about `AI Involvement'. We scrutinize 10 qualities of badges through three complementary studies: (1) a controlled Prolific study with a general audience (N=24); (2) interviews with 12 domain experts experiencing badges as part of professional visualization applications; and (3) interviews with 5 visualization designers gauging the feasibility of badges for themselves and their audiences. We found clear evidence for badges attracting curiosity, aiding interpretation, fostering trust, and motivating broader adoption. We conclude by discussing the role of badges in data and visualization literacy and exploring their potential to become an accepted convention for visualization. By providing our study materials, reusable badge implementation, and additional badge resources at https://vis-badges.github.io, we invite the community to use, adapt, and further study visualization badges.

Citizen environmental complaints provide a valuable, citizen-centered perspective on urban environmental conditions, complementing traditional sensor-based monitoring systems. However, extracting actionable insights from such large-scale, heterogeneous data remains challenging due to the entanglement of episodic events and persistent anomalies, as well as the highly diverse spatial propagation patterns across different pollution types. Moreover, effective multi-scale visual analytics tools that can jointly integrate subjective public feedback with objective urban physical context are still lacking, limiting comprehensive cross-level understanding and interpretation. In this paper, we present EnvLens, a visual analytics system that unifies geo-referenced citizen complaints with satellite-derived surface semantic embeddings within H3 cells. To capture the nuanced spatial dynamics of disturbances, we introduce a directional propagation analysis method that drives a data-driven anisotropic diffusion heatmap based on a von Mises parametric kernel. Based on Jensen–Shannon Divergence (JSD) and Mean Resultant Length (MRL), we propose a JSD-MRL spatial-behavior typology that systematically classifies complaint patterns into three behavioral types, population-following, structural-diffusive, and spatial-specific. EnvLens employs a dual-embedding comparison workflow to recommend and compare urban regions, revealing pairs with highly similar built environments but markedly different complaint profiles. Through case studies utilizing 42,755 environmental complaints from Shenzhen and domain expert feedback, we demonstrate that EnvLens successfully pinpoints type-specific spatial anomalies, interprets directional propagation footprints, and generates robust cross-regional hypotheses for urban governance.