arXiv subjects

Benjamin Bach

Publications and source records attributed to Benjamin Bach.

At least 19 recordsLinked to original sources

arXiv2026

Designing a Visualization Atlas: Lessons & Reflections from The UK Co-Benefits Atlas for Climate Mitigation

This paper reports on the process of designing the UK Co-Benefits Atlas, which communicates and publicizes data for climate mitigation. Visualization atlases--an emerging type of platform to make data about complex topics comprehensive through interactive visualizations and explanatory content--pose challenges beyond traditional visualization projects. Atlases must address diverse and often uncertain audiences and use cases, support both explanatory and guided exploration, and accommodate complex, evolving data. Over 10 months, our team of visualization and domain experts conducted 8 design workshops, iterative prototyping, 15 stakeholder onboarding sessions, and continuous reflection. These intertwined processes informed the development of the Atlas, comprising over 400 pages of visualizations and explanations. They also enabled a deeper understanding of how stakeholders may critically engage with the atlas in practice, in terms of interests, potential frictions when navigating huge amounts of data, and envisioned usage scenarios. Reflecting on our design process, we identify five driving forces in atlas design--data, people, stories, context, and the atlas itsel--whose shifting dynamics influence different stages of visualization atlas design in different ways. Grounded in our case study, we discuss using these forces as a conceptual starting point for structuring and reflecting on future atlas design processes.

cs.HC↗

arXiv2026

Disrupting Cognitive Passivity: Rethinking AI-Assisted Data Literacy through Cognitive Alignment

AI chatbots are increasingly stepping into roles as collaborators or teachers in analyzing, visualizing, and reasoning through data and domain problem. Yet, AI's default assistant mode with its comprehensive and one-off responses may undermine opportunities for practitioners to develop literacy through their own thinking, inducing cognitive passivity. Drawing on evidence from empirical studies and theories, we argue that disrupting cognitive passivity necessitates a nuanced approach: rather than simply making AI promote deliberative thinking, there is a need for more dynamic and adaptive strategy through cognitive alignment -- a framework that characterizes effective human-AI interaction as a function of alignment between users' cognitive demand and AI's interaction mode. In the framework, we provide the mapping between AI's interaction mode (transmissive or deliberative) and users' cognitive demand (receptive or deliberative), otherwise leading to either cognitive passivity or friction. We further discuss implications and offer open questions for future research on data literacy.

cs.HC↗

arXiv2026

Towards Measuring Interactive Visualization Abilities: Connecting With Existing Literacies and Assessments

How do we assess people's abilities to interact with data visualizations? The current state-of-the-art visualization literacy tests -- such as VLAT and its derivatives -- only involve the use of static visualizations. Despite advances in investigating multiple visualization abilities, we do not yet have formal methods to assess the ability of a person to interact with a data visualization effectively. In this position paper, we discuss related literacy concepts and assessments to propose and compare different approaches for assessing the abilities that people leverage to use visualizations in interactive sensemaking tasks.

cs.HC↗

arXiv2026

A Multiliteracy Model for Interactive Visualization Literacy: Definitions, Literacies, and Steps for Future Research

This paper presents a theoretical model for interactive visualization literacy to describe how people use interactive data visualizations and systems. Literacies have become an important concept in describing modern life skills, with visualization literacy generally referring to the use and interpretation of data visualizations. However, prior work on visualization literacy overlooks interaction and its associated challenges, despite it being an intrinsic aspect of using visualizations. Based on existing theoretical frameworks, we derive a two-dimensional model that combines four well-known literacies with five novel ones. We found evidence for our model through analyzing existing visualization systems as well as through observations from an exploratory study involving such systems. We conclude by outlining steps towards measuring, evaluating, designing for, and teaching interactive visualization literacy.

cs.HC↗

arXiv2026

From Answer Givers to Design Mentors: Guiding LLMs with the Cognitive Apprenticeship Model

Design feedback helps practitioners improve their artifacts while also fostering reflection and design reasoning. Large Language Models (LLMs) such as ChatGPT can support design work, but often provide generic, one-off suggestions that limit reflective engagement. We investigate how to guide LLMs to act as design mentors by applying the Cognitive Apprenticeship Model, which emphasizes demonstrating reasoning through six methods: modeling, coaching, scaffolding, articulation, reflection, and exploration. We operationalize these instructional methods through structured prompting and evaluate them in a within-subjects study with data visualization practitioners. Participants interacted with both a baseline LLM and an instructional LLM designed with cognitive apprenticeship prompts. Surveys, interviews, and conversational log analyses compared experiences across conditions. Our findings show that cognitively informed prompts elicit deeper design reasoning and more reflective feedback exchanges, though the baseline is sometimes preferred depending on task types or experience levels. We distill design considerations for AI-assisted feedback systems that foster reflective practice.

cs.HC↗

arXiv2025

Investigating the Capabilities and Limitations of Machine Learning for Identifying Bias in English Language Data with Information and Heritage Professionals

Despite numerous efforts to mitigate their biases, ML systems continue to harm already-marginalized people. While predominant ML approaches assume bias can be removed and fair models can be created, we show that these are not always possible, nor desirable, goals. We reframe the problem of ML bias by creating models to identify biased language, drawing attention to a dataset's biases rather than trying to remove them. Then, through a workshop, we evaluated the models for a specific use case: workflows of information and heritage professionals. Our findings demonstrate the limitations of ML for identifying bias due to its contextual nature, the way in which approaches to mitigating it can simultaneously privilege and oppress different communities, and its inevitability. We demonstrate the need to expand ML approaches to bias and fairness, providing a mixed-methods approach to investigating the feasibility of removing bias or achieving fairness in a given ML use case.

cs.CL↗

arXiv2025

Towards Collective Storytelling: Investigating Audience Annotations in Data Visualizations

This work investigates personal perspectives in visualization annotations as devices for collective data-driven storytelling. Inspired by existing efforts in critical cartography, we show how people share personal memories in a visualization of COVID-19 data and how comments by other visualization readers influence the reading and understanding of visualizations. Analyzing interaction logs, reader surveys, visualization annotations, and interviews, we find that reader annotations help other viewers relate to other people's stories and reflect on their own experiences. Further, we found that annotations embedded directly into the visualization can serve as social traces guiding through a visualization and help readers contextualize their own stories. With that, they supersede the attention paid to data encodings and become the main focal point of the visualization.

cs.HC↗

arXiv2024

Constraint-Based Breakpoints for Responsive Visualization Design and Development

This paper introduces constraint-based breakpoints, a technique for designing responsive visualizations for a wide variety of screen sizes and datasets. Breakpoints in responsive visualization define when different visualization designs are shown. Conventionally, breakpoints are static, pre-defined widths, and as such do not account for changes to the visualized dataset or visualization parameters. To guarantee readability and efficient use of space across datasets, these static breakpoints would require manual updates. Constraint-based breakpoints solve this by evaluating visualization-specific constraints on the size of visual elements, overlapping elements, and the aspect ratio of the visualization and available space. Once configured, a responsive visualization with constraint-based breakpoints can adapt to different screen sizes for any dataset. We describe a framework that guides designers in creating a stack of visualization designs for different display sizes and defining constraints for each of these designs. We demonstrate constraint-based breakpoints for different data types and their visualizations: geographic data (choropleth map, proportional circle map, Dorling cartogram, hexagonal grid map, bar chart, waffle chart), network data (node-link diagram, adjacency matrix, arc diagram), and multivariate data (scatterplot, heatmap). Interactive demos and supplemental material are available at https://responsive-vis.github.io/breakpoints/.

cs.HC↗

arXiv2024

Visualization Atlases: Explaining and Exploring Complex Topics through Data, Visualization, and Narration

This paper defines, analyzes, and discusses the emerging genre of visualization atlases. We currently witness an increase in web-based, data-driven initiatives that call themselves "atlases" while explaining complex, contemporary issues through data and visualizations: climate change, sustainability, AI, or cultural discoveries. To understand this emerging genre and inform their design, study, and authoring support, we conducted a systematic analysis of 33 visualization atlases and semi-structured interviews with eight visualization atlas creators. Based on our results, we contribute (1) a definition of a visualization atlas as a compendium of (web) pages aimed at explaining and supporting exploration of data about a dedicated topic through data, visualizations and narration. (2) a set of design patterns of 8 design dimensions, (3) insights into the atlas creation from interviews and (4) the definition of 5 visualization atlas genres. We found that visualization atlases are unique in the way they combine i) exploratory visualization, ii) narrative elements from data-driven storytelling and iii) structured navigation mechanisms. They target a wide range of audiences with different levels of domain knowledge, acting as tools for study, communication, and discovery. We conclude with a discussion of current design practices and emerging questions around the ethics and potential real-world impact of visualization atlases, aimed to inform the design and study of visualization atlases.

cs.HC↗

arXiv2024

Does This Have a Particular Meaning? Interactive Pattern Explanation for Network Visualizations

This paper presents an interactive technique to explain visual patterns in network visualizations to analysts who do not understand these visualizations and who are learning to read them. Learning a visualization requires mastering its visual grammar and decoding information presented through visual marks, graphical encodings, and spatial configurations. To help people learn network visualization designs and extract meaningful information, we introduce the concept of interactive pattern explanation that allows viewers to select an arbitrary area in a visualization, then automatically mines the underlying data patterns, and explains both visual and data patterns present in the viewer's selection. In a qualitative and a quantitative user study with a total of 32 participants, we compare interactive pattern explanations to textual-only and visual-only (cheatsheets) explanations. Our results show that interactive explanations increase learning of i) unfamiliar visualizations, ii) patterns in network science, and iii) the respective network terminology.

cs.HC↗

arXiv2024

Practices and Strategies in Responsive Thematic Map Design: A Report from Design Workshops with Experts

This paper discusses challenges and design strategies in responsive design for thematic maps in information visualization. Thematic maps pose a number of unique challenges for responsiveness, such as inflexible aspect ratios that do not easily adapt to varying screen dimensions, or densely clustered visual elements in urban areas becoming illegible at smaller scales. However, design guidance on how to best address these issues is currently lacking. We conducted design sessions with eight professional designers and developers of web-based thematic maps for information visualization. Participants were asked to redesign a given map for various screen sizes and aspect ratios and to describe their reasoning for when and how they adapted the design. We report general observations of practitioners' motivations, decision-making processes, and personal design frameworks. We then derive seven challenges commonly encountered in responsive maps, and 17 strategies to address them, such as repositioning elements, segmenting the map, or using alternative visualizations. We compile these challenges and strategies into an illustrated cheat sheet targeted at anyone designing or learning to design responsive maps. The cheat sheet is available online: https://responsive-vis.github.io/map-cheat-sheet

cs.HC↗

arXiv2024

Discursive Patinas: Anchoring Discussions in Data Visualizations

This paper presents discursive patinas, a technique to visualize discussions onto data visualizations, inspired by how people leave traces in the physical world. While data visualizations are widely discussed in online communities and social media, comments tend to be displayed separately from the visualization and we lack ways to relate these discussions back to the content of the visualization, e.g., to situate comments, explain visual patterns, or question assumptions. In our visualization annotation interface, users can designate areas within the visualization. Discursive patinas are made of overlaid visual marks (anchors), attached to textual comments with category labels, likes, and replies. By coloring and styling the anchors, a meta visualization emerges, showing what and where people comment and annotate the visualization. These patinas show regions of heavy discussions, recent commenting activity, and the distribution of questions, suggestions, or personal stories. We ran workshops with 90 students, domain experts, and visualization researchers to study how people use anchors to discuss visualizations and how patinas influence people's understanding of the discussion. Our results show that discursive patinas improve the ability to navigate discussions and guide people to comments that help understand, contextualize, or scrutinize the visualization. We discuss the potential of anchors and patinas to support discursive engagements, including critical readings of visualizations, design feedback, and feminist approaches to data visualization.

cs.HC↗

arXiv2024

ChatGPT in Data Visualization Education: A Student Perspective

Unlike traditional educational chatbots that rely on pre-programmed responses, large-language model-driven chatbots, such as ChatGPT, demonstrate remarkable versatility to serve as a dynamic resource for addressing student needs from understanding advanced concepts to solving complex problems. This work explores the impact of such technology on student learning in an interdisciplinary, project-oriented data visualization course. Throughout the semester, students engaged with ChatGPT across four distinct projects, designing and implementing data visualizations using a variety of tools such as Tableau, D3, and Vega-lite. We collected conversation logs and reflection surveys after each assignment and conducted interviews with selected students to gain deeper insights into their experiences with ChatGPT. Our analysis examined the advantages and barriers of using ChatGPT, students' querying behavior, the types of assistance sought, and its impact on assignment outcomes and engagement. We discuss design considerations for an educational solution tailored for data visualization education, extending beyond ChatGPT's basic interface.

cs.HC↗

arXiv2024

IEEE VIS Workshop on Visualization for Climate Action and Sustainability

This first workshop on visualization for climate action and sustainability aims to explore and consolidate the role of data visualization in accelerating action towards addressing the current environmental crisis. Given the urgency and impact of the environmental crisis, we ask how our skills, research methods, and innovations can help by empowering people and organizations. We believe visualization holds an enormous power to aid understanding, decision making, communication, discussion, participation, education, and exploration of complex topics around climate action and sustainability. Hence, this workshop invites submissions and discussion around these topics with the goal of establishing a visible and actionable link between these fields and their respective stakeholders. The workshop solicits work-in-progress and research papers as well as pictorials and interactive demos from the whole range of visualization research (dashboards, interactive spaces, scientific visualization, storytelling, visual analytics, explainability etc.), within the context of environmentalism (climate science, sustainability, energy, circular economy, biodiversity, etc.) and across a range of scenarios from public awareness and understanding, visual analysis, expert decision making, science communication, personal decision making etc. After presentations of submissions, the workshop will feature dedicated discussion groups around data driven interactive experiences for the public, and tools for personal and professional decision making.

cs.HC↗

arXiv2024

Feature-Action Design Patterns for Storytelling Visualizations with Time Series Data

We present a method to create storytelling visualization with time series data. Many personal decisions nowadays rely on access to dynamic data regularly, as we have seen during the COVID-19 pandemic. It is thus desirable to construct storytelling visualization for dynamic data that is selected by an individual for a specific context. Because of the need to tell data-dependent stories, predefined storyboards based on known data cannot accommodate dynamic data easily nor scale up to many different individuals and contexts. Motivated initially by the need to communicate time series data during the COVID-19 pandemic, we developed a novel computer-assisted method for meta-authoring of stories, which enables the design of storyboards that include feature-action patterns in anticipation of potential features that may appear in dynamically arrived or selected data. In addition to meta-storyboards involving COVID-19 data, we also present storyboards for telling stories about progress in a machine learning workflow. Our approach is complementary to traditional methods for authoring storytelling visualization, and provides an efficient means to construct data-dependent storyboards for different data-streams of similar contexts.

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arXiv2023

NetPanorama: A Declarative Grammar for Network Construction, Transformation, and Visualization

This paper introduces NetPanorama, a domain-specific language and declarative grammar for interactive network visualization design that supports multivariate, temporal, and geographic networks. NetPanorama allows users to specify network visualizations as combinations of primitives and building blocks. These support network creation and transformation, including computing metrics; orderings, seriations and layouts; visual encodings, including glyphs, faceting, and label visibility; and interaction for exploration and modifying styling. This approach allows the creation of a range of visualizations including many types of node-link diagrams, adjacency matrices using diverse cell encodings and node orderings, arc diagrams, PivotGraph, small multiples, time-arcs, geographic map visualizations, and hybrid techniques such as NodeTrix. NetPanorama aims to remove the need to use multiple libraries for analysis, wrangling, and visualization. Consequently, NetPanorama supports the agile development of applications for visual exploration of networks and data-driven storytelling. Documentation, source code, further examples, and an interactive online editor can be found online: https://netpanorama.netlify.app/.

cs.HC↗

arXiv2023

Show Me My Users: A Dashboard Visualizing User Interaction Logs

This paper describes the design of a dashboard and analysis pipeline to monitor users of visualization tools in the wild. Our pipeline describes how to extract analytical KPIs from extensive log event data involving a mix of user types. The resulting three-page dashboard displays live KPIs, helping analysts understand users, detect exploratory behaviors, plan education interventions, and improve tool features. We propose this case study as a motivation to use the dashboard approach for a more `casual' monitoring of users and building carer mindsets for visualization tools.

cs.HC↗

arXiv2023

How Good is ChatGPT in Giving Advice on Your Visualization Design?

Data visualization creators often lack formal training, resulting in a knowledge gap in design practice. Large language models such as ChatGPT, with their vast internet-scale training data, offer transformative potential to address this gap. In this study, we used both qualitative and quantitative methods to investigate how well ChatGPT can address visualization design questions. First, we quantitatively compared the ChatGPT-generated responses with anonymous online Human replies to data visualization questions on the VisGuides user forum. Next, we conducted a qualitative user study examining the reactions and attitudes of practitioners toward ChatGPT as a visualization design assistant. Participants were asked to bring their visualizations and design questions and received feedback from both Human experts and ChatGPT in randomized order. Our findings from both studies underscore ChatGPT's strengths, particularly its ability to rapidly generate diverse design options, while also highlighting areas for improvement, such as nuanced contextual understanding and fluid interaction dynamics beyond the chat interface. Drawing on these insights, we discuss design considerations for future LLM-based design feedback systems.

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