arXiv subjects

Leilani Battle

Publications and source records attributed to Leilani Battle.

At least 19 recordsLinked to original sources

arXiv2026

What Do Visualization Instructors Want Students to Learn? Introducing a Concept Inventory for Visualization Design

The term "visualization design" encompasses multiple concepts and skills that go well beyond current assessments of graphical perception and visualization literacy. In the context of education, what exactly should a student be able to do if they "know" visualization design? To answer this question, we draw on existing methodology from the field of education to propose a concept inventory for visualization design, i.e., a theoretical model capturing the most important concepts and skills commonly associated with visualization design. We initially draft the concept inventory using a qualitative analysis of course objectives from visualization course syllabi. Then, we iteratively refine the concept inventory by soliciting feedback from instructors through semi-structured interviews. Based on our experiences in developing the concept inventory, we reflect on open questions and future research directions in visualization education, such as developing assessments for visualization design (similar to those for visualization literacy) and providing automated assistance for learning and teaching visualization design. Our supplemental materials are available at https://osf.io/dma3f/.

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arXiv2026

A Design Space for Quantum Circuit Visualizations

Quantum circuit visualizations play an essential role in supporting sense-making and communication of quantum programs. While several tools exist for rendering quantum circuits, they vary widely in encoding options due to idiosyncrasies among machine and platform providers. We observe an opportunity to coalesce these disparate rendering approaches under a single, unified grammar to enable consistent, cross-platform enhancement of quantum circuit visualizations. However, it is unclear how to design such a grammar to best support the quantum computing community. Towards this end, we contribute a design space of quantum circuit visualizations by analyzing 182 static and 12 interactive cases collected from online tutorials and documentations, research publications, public presentations, and prior systems. Based on our analysis, we discuss how our design space relates to existing visualization principles yet exhibits unique aspects. We conclude with opportunities for future systems regarding data structure, cognition, and integrability.

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arXiv2026

Task-Centered Benchmark for Interactive Network Visualization & Analysis

Interactive network visualization and analysis (INVA) enables iterative, visual and algorithmic analysis of large network datasets. Although numerous benchmarks have been developed to evaluate different graph analysis algorithms and systems, we observe a lack of such efforts for interactive network data understanding. In this work, we address the question - How well do existing graph systems serve the purpose of Interactive Network Visualization and Analysis? To this end, we build and demonstrate the use of the first task-centered benchmarking framework to evaluate a variety of graph system backends on INVA workloads. Our benchmarking results highlight a gap between both the capabilities and performance of existing graph systems for INVA use cases, and uncover possible bugs in these systems. Based on our benchmarking results, we reveal new opportunities for research and development to better support interactive network visualization and analysis.

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arXiv2025

A Crowdsourced Study of ChatBot Influence in Value-Driven Decision Making Scenarios

Similar to social media bots that shape public opinion, healthcare and financial decisions, LLM-based ChatBots like ChatGPT can persuade users to alter their behavior. Unlike prior work that persuades via overt-partisan bias or misinformation, we test whether framing alone suffices. We conducted a crowdsourced study, where 336 participants interacted with a neutral or one of two value-framed ChatBots while deciding to alter US defense spending. In this single policy domain with controlled content, participants exposed to value-framed ChatBots significantly changed their budget choices relative to the neutral control. When the frame misaligned with their values, some participants reinforced their original preference, revealing a potentially replicable backfire effect, originally considered rare in the literature. These findings suggest that value-framing alone lowers the barrier for manipulative uses of LLMs, revealing risks distinct from overt bias or misinformation, and clarifying risks to countering misinformation.

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arXiv2025

African Data Ethics: A Discursive Framework for Black Decolonial Data Science

The shift towards pluralism in global data ethics acknowledges the importance of including perspectives from the Global Majority to develop responsible data science practices that mitigate systemic harms in the current data science ecosystem. Sub-Saharan African (SSA) practitioners, in particular, are disseminating progressive data ethics principles and best practices for identifying and navigating anti-blackness and data colonialism. To center SSA voices in the global data ethics discourse, we present a framework for African data ethics informed by the thematic analysis of an interdisciplinary corpus of 50 documents. Our framework features six major principles: 1) Challenge Power Asymmetries, 2) Assert Data Self-Determination, 3) Invest in Local Data Institutions & Infrastructures, 4) Utilize Communalist Practices, 5) Center Communities on the Margins, and 6) Uphold Common Good. We compare our framework to seven particularist data ethics frameworks to find similar conceptual coverage but diverging interpretations of shared values. Finally, we discuss how African data ethics demonstrates the operational value of data ethics frameworks. Our framework highlights Sub-Saharan Africa as a pivotal site of responsible data science by promoting the practice of communalism, self-determination, and cultural preservation.

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arXiv2024

Behavior Matters: An Alternative Perspective on Promoting Responsible Data Science

Data science pipelines inform and influence many daily decisions, from what we buy to who we work for and even where we live. When designed incorrectly, these pipelines can easily propagate social inequity and harm. Traditional solutions are technical in nature; e.g., mitigating biased algorithms. In this vision paper, we introduce a novel lens for promoting responsible data science using theories of behavior change that emphasize not only technical solutions but also the behavioral responsibility of practitioners. By integrating behavior change theories from cognitive psychology with data science workflow knowledge and ethics guidelines, we present a new perspective on responsible data science. We present example data science interventions in machine learning and visual data analysis, contextualized in behavior change theories that could be implemented to interrupt and redirect potentially suboptimal or negligent practices while reinforcing ethically conscious behaviors. We conclude with a call to action to our community to explore this new research area of behavior change interventions for responsible data science.

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arXiv2024

DracoGPT: Extracting Visualization Design Preferences from Large Language Models

Trained on vast corpora, Large Language Models (LLMs) have the potential to encode visualization design knowledge and best practices. However, if they fail to do so, they might provide unreliable visualization recommendations. What visualization design preferences, then, have LLMs learned? We contribute DracoGPT, a method for extracting, modeling, and assessing visualization design preferences from LLMs. To assess varied tasks, we develop two pipelines--DracoGPT-Rank and DracoGPT-Recommend--to model LLMs prompted to either rank or recommend visual encoding specifications. We use Draco as a shared knowledge base in which to represent LLM design preferences and compare them to best practices from empirical research. We demonstrate that DracoGPT can accurately model the preferences expressed by LLMs, enabling analysis in terms of Draco design constraints. Across a suite of backing LLMs, we find that DracoGPT-Rank and DracoGPT-Recommend moderately agree with each other, but both substantially diverge from guidelines drawn from human subjects experiments. Future work can build on our approach to expand Draco's knowledge base to model a richer set of preferences and to provide a robust and cost-effective stand-in for LLMs.

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arXiv2024

Considering Visualization Example Galleries

Example galleries are often used to teach, document, and advertise visually-focused domain-specific languages and libraries, such as those producing visualizations, diagrams, or webpages. Despite their ubiquity, there is no consensus on the role of "example galleries", let alone what the best practices might be for their creation or curation. To understand gallery meaning and usage, we interviewed the creators (N=11) and users (N=9) of prominent visualization-adjacent tools. From these interviews we synthesized strategies and challenges for gallery curation and management (e.g. weighing the costs/benefits of adding new examples and trade-offs in richness vs ease of use), highlighted the differences between planned and actual gallery usage (e.g. opportunistic reuse vs search-engine optimization), and reflected on parts of the gallery design space not explored (e.g. highlighting the potential of tool assistance). We found that galleries are multi-faceted structures whose form and content are motivated to accommodate different usages--ranging from marketing material to test suite to extended documentation. This work offers a foundation for future support tools by characterizing gallery design and management, as well as by highlighting challenges and opportunities in the space (such as how more diverse galleries make reuse tasks simpler, but complicate upkeep).

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arXiv2024

How do Observable Users Decompose D3 Code? A Qualitative Study

Many toolkit developers seek to streamline the visualization programming process through structured support such as prescribed templates and example galleries. However, few projects examine how users organize their own visualization programs and how their coding choices may deviate from the intents of toolkit developers, impacting visualization prototyping and design. Further, is it possible to infer users' reasoning indirectly through their code, even when users copy code from other sources? We explore this question through a qualitative analysis of 715 D3 programs on Observable. We identify three levels of program organization based on how users decompose their code into smaller blocks: Program-, Chart-, and Component-Level code decomposition, with a strong preference for Component-Level reasoning. In a series of interviews, we corroborate that these levels reflect how Observable users reason about visualization programs. We compare common user-made components with those theorized in the Grammar of Graphics to assess overlap in user and toolkit developer reasoning. We find that, while the Grammar of Graphics covers basic visualizations well, it falls short in describing complex visualization types, especially those with animation, interaction, and parameterization components. Our findings highlight how user practices differ from formal grammars and reinforce ongoing efforts to rethink visualization toolkit support, including augmenting learning tools and AI assistants to better reflect real-world coding strategies.

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arXiv2024

Optimizing Dataflow Systems for Scalable Interactive Visualization

Supporting the interactive exploration of large datasets is a popular and challenging use case for data management systems. Traditionally, the interface and the back-end system are built and optimized separately, and interface design and system optimization require different skill sets that are difficult for one person to master. To enable analysts to focus on visualization design, we contribute VegaPlus, a system that automatically optimizes interactive dashboards to support large datasets. To achieve this, VegaPlus leverages two core ideas. First, we introduce an optimizer that can reason about execution plans in Vega, a back-end DBMS, or a mix of both environments. The optimizer also considers how user interactions may alter execution plan performance, and can partially or fully rewrite the plans when needed. Through a series of benchmark experiments on seven different dashboard designs, our results show that VegaPlus provides superior performance and versatility compared to standard dashboard optimization techniques.

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arXiv2023

How Does User Behavior Evolve During Exploratory Visual Analysis?

Exploratory visual analysis (EVA) is an essential stage of the data science pipeline, where users often lack clear analysis goals at the start and iteratively refine them as they learn more about their data. Accurate models of users' exploration behavior are becoming increasingly vital to developing responsive and personalized tools for exploratory visual analysis. Yet we observe a discrepancy between the static view of human exploration behavior adopted by many computational models versus the dynamic nature of EVA. In this paper, we explore potential parallels between the evolution of users' interactions with visualization tools during data exploration and assumptions made in popular online learning techniques. Through a series of empirical analyses, we seek to answer the question: how might users' exploration behavior evolve in response to what they have learned from the data during EVA? We present our findings and discuss their implications for the future of user modeling for system design.

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arXiv2023

What Do We Mean When We Say "Insight"? A Formal Synthesis of Existing Theory

Researchers have derived many theoretical models for specifying users' insights as they interact with a visualization system. These representations are essential for understanding the insight discovery process, such as when inferring user interaction patterns that lead to insight or assessing the rigor of reported insights. However, theoretical models can be difficult to apply to existing tools and user studies, often due to discrepancies in how insight and its constituent parts are defined. This paper calls attention to the consistent structures that recur across the visualization literature and describes how they connect multiple theoretical representations of insight. We synthesize a unified formalism for insights using these structures, enabling a wider audience of researchers and developers to adopt the corresponding models. Through a series of theoretical case studies, we use our formalism to compare and contrast existing theories, revealing interesting research challenges in reasoning about a user's domain knowledge and leveraging synergistic approaches in data mining and data management research.

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arXiv2023

Too Many Cooks: Exploring How Graphical Perception Studies Influence Visualization Recommendations in Draco

Findings from graphical perception can guide visualization recommendation algorithms in identifying effective visualization designs. However, existing algorithms use knowledge from, at best, a few studies, limiting our understanding of how complementary (or contradictory) graphical perception results influence generated recommendations. In this paper, we present a pipeline of applying a large body of graphical perception results to develop new visualization recommendation algorithms and conduct an exploratory study to investigate how results from graphical perception can alter the behavior of downstream algorithms. Specifically, we model graphical perception results from 30 papers in Draco -- a framework to model visualization knowledge -- to develop new recommendation algorithms. By analyzing Draco-generated algorithms, we showcase the feasibility of our method to (1) identify gaps in existing graphical perception literature informing recommendation algorithms, (2) cluster papers by their preferred design rules and constraints, and (3) investigate why certain studies can dominate Draco's recommendations, whereas others may have little influence. Given our findings, we discuss the potential for mutually reinforcing advancements in graphical perception and visualization recommendation research.

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arXiv2023

WhaleVis: Visualizing the History of Commercial Whaling

Whales are an important part of the oceanic ecosystem. Although historic commercial whale hunting a.k.a. whaling has severely threatened whale populations, whale researchers are looking at historical whaling data to inform current whale status and future conservation efforts. To facilitate this, we worked with experts in aquatic and fishery sciences to create WhaleVis -- an interactive dashboard for the commercial whaling dataset maintained by the International Whaling Commission (IWC). We characterize key analysis tasks among whale researchers for this database, most important of which is inferring spatial distribution of whale populations over time. In addition to facilitating analysis of whale catches based on the spatio-temporal attributes, we use whaling expedition details to plot the search routes of expeditions. We propose a model of the catch data as a graph, where nodes represent catch locations, and edges represent whaling expedition routes. This model facilitates visual estimation of whale search effort and in turn the spatial distribution of whale populations normalized by the search effort -- a well known problem in fisheries research. It further opens up new avenues for graph analysis on the data, including more rigorous computation of spatial distribution of whales normalized by the search effort, and enabling new insight generation. We demonstrate the use of our dashboard through a real life use case.

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arXiv2023

What Exactly is an Insight? A Literature Review

Insights are often considered the ideal outcome of visual analysis sessions. However, there is no single definition of what an insight is. Some scholars define insights as correlations, while others define them as hypotheses or aha moments. This lack of a clear definition can make it difficult to build visualization tools that effectively support insight discovery. In this paper, we contribute a comprehensive literature review that maps the landscape of existing insight definitions. We summarize key themes regarding how insight is defined, with the goal of helping readers identify which definitions of insight align closely with their research and tool development goals. Based on our review, we also suggest interesting research directions, such as synthesizing a unified formalism for insight and connecting theories of insight to other critical concepts in visualization research.

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arXiv2023

Toward a Scalable Census of Dashboard Designs in the Wild: A Case Study with Tableau Public

Dashboards remain ubiquitous artifacts for presenting or reasoning with data across different domains. Yet, there has been little work that provides a quantifiable, systematic, and descriptive overview of dashboard designs at scale. We propose a schematic representation of dashboard designs as node-link graphs to better understand their spatial and interactive structures. We apply our approach to a dataset of 25,620 dashboards curated from Tableau Public to provide a descriptive overview of the core building blocks of dashboards in the wild and derive common dashboard design patterns. To guide future research, we make our dashboard corpus publicly available and discuss its application toward the development of dashboard design tools.

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arXiv2023

User-Driven Support for Visualization Prototyping in D3

Templates have emerged as an effective approach to simplifying the visualization design and programming process. For example, they enable users to quickly generate multiple visualization designs even when using complex toolkits like D3. However, these templates are often treated as rigid artifacts that respond poorly to changes made outside of the template's established parameters, limiting user creativity. Preserving the user's creative flow requires a more dynamic approach to template-based visualization design, where tools can respond gracefully to users' edits when they modify templates in unexpected ways. In this paper, we leverage the structural similarities revealed by templates to design resilient support features for prototyping D3 visualizations: recommendations to suggest complementary interactions for a user's D3 program; and code augmentation to implement recommended interactions with a single click, even when users deviate from pre-defined templates. We demonstrate the utility of these features in Mirny, a d design-focused prototyping environment for D3. In a user study with 20 D3 users, we find that these automated features enable participants to prototype their design ideas with significantly fewer programming iterations. We also characterize key modification strategies used by participants to customize D3 templates. Informed by our findings and participants' feedback, we discuss the key implications of the use of templates for interleaving visualization programming and design.

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arXiv2023

A Review and Collation of Graphical Perception Knowledge for Visualization Recommendation

Selecting appropriate visual encodings is critical to designing effective visualization recommendation systems, yet few findings from graphical perception are typically applied within these systems. We observe two significant limitations in translating graphical perception knowledge into actionable visualization recommendation rules/constraints: inconsistent reporting of findings and a lack of shared data across studies. How can we translate the graphical perception literature into a knowledge base for visualization recommendation? We present a review of 59 papers that study user perception and performance across ten visual analysis tasks. Through this study, we contribute a JSON dataset that collates existing theoretical and experimental knowledge and summarizes key study outcomes in graphical perception. We illustrate how this dataset can inform automated encoding decisions with three representative visualization recommendation systems. Based on our findings, we highlight open challenges and opportunities for the community in collating graphical perception knowledge for a range of visualization recommendation scenarios.

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