DevLan/vispubs huggingface dataset:
Gemini: A Grammar and Recommender System for Animated Transitions in Statistical Graphics
Authors: Younghoon Kim, Jeffrey Heer
Published at InfoVis, 2020

Animated transitions help viewers follow changes between related visualizations. Specifying effective animations demands significant effort: authors must select the elements and properties to animate, provide transition parameters, and coordinate the timing of stages. To facilitate this process, we present Gemini, a declarative grammar and recommendation system for animated transitions between single-view statistical graphics. Gemini specifications define transition “steps” in terms of high-level visual components (marks, axes, legends) and composition rules to synchronize and concatenate steps. With this grammar, Gemini can recommend animation designs to augment and accelerate designers' work. Gemini enumerates staged animation designs for given start and end states, and ranks those designs using a cost function informed by prior perceptual studies. To evaluate Gemini, we conduct both a formative study on Mechanical Turk to assess and tune our ranking function, and a summative study in which 8 experienced visualization developers implement animations in D3 that we then compare to Gemini's suggestions. We find that most designs (9/11) are exactly replicable in Gemini, with many (8/11) achievable via edits to suggestions, and that Gemini suggestions avoid multiple participant errors.
Guidelines For Pursuing and Revealing Data Abstractions
Authors: Alex Bigelow, Katy Williams, Katherine E. Isaacs
Published at InfoVis, 2020

Many data abstraction types, such as networks or set relationships, remain unfamiliar to data workers beyond the visualization research community. We conduct a survey and series of interviews about how people describe their data, either directly or indirectly. We refer to the latter as latent data abstractions. We conduct a Grounded Theory analysis that (1) interprets the extent to which latent data abstractions exist, (2) reveals the far-reaching effects that the interventionist pursuit of such abstractions can have on data workers, (3) describes why and when data workers may resist such explorations, and (4) suggests how to take advantage of opportunities and mitigate risks through transparency about visualization research perspectives and agendas. We then use the themes and codes discovered in the Grounded Theory analysis to develop guidelines for data abstraction in visualization projects. To continue the discussion, we make our dataset open along with a visual interface for further exploration.
Humane Visual AI: Telling the Stories Behind a Medical Condition
Authors: Wonyoung So, Edyta Paulina Bogucka, Sanja Scepanovic, Sagar Joglekar 0001, Ke Zhou 0003, Daniele Quercia
Published at InfoVis, 2020

A biological understanding is key for managing medical conditions, yet psychological and social aspects matter too. The main problem is that these two aspects are hard to quantify and inherently difficult to communicate. To quantify psychological aspects, this work mined around half a million Reddit posts in the sub-communities specialised in 14 medical conditions, and it did so with a new deep-learning framework. In so doing, it was able to associate mentions of medical conditions with those of emotions. To then quantify social aspects, this work designed a probabilistic approach that mines open prescription data from the National Health Service in England to compute the prevalence of drug prescriptions, and to relate such a prevalence to census data. To finally visually communicate each medical condition's biological, psychological, and social aspects through storytelling, we designed a narrative-style layered Martini Glass visualization. In a user study involving 52 participants, after interacting with our visualization, a considerable number of them changed their mind on previously held opinions: 10% gave more importance to the psychological aspects of medical conditions, and 27% were more favourable to the use of social media data in healthcare, suggesting the importance of persuasive elements in interactive visualizations.
Implicit Multidimensional Projection of Local Subspaces
Authors: Rongzheng Bian, Yumeng Xue, Liang Zhou 0001, Jian Zhang 0070, Baoquan Chen, Daniel Weiskopf, Yunhai Wang
Published at InfoVis, 2020

We propose a visualization method to understand the effect of multidimensional projection on local subspaces, using implicit function differentiation. Here, we understand the local subspace as the multidimensional local neighborhood of data points. Existing methods focus on the projection of multidimensional data points, and the neighborhood information is ignored. Our method is able to analyze the shape and directional information of the local subspace to gain more insights into the global structure of the data through the perception of local structures. Local subspaces are fitted by multidimensional ellipses that are spanned by basis vectors. An accurate and efficient vector transformation method is proposed based on analytical differentiation of multidimensional projections formulated as implicit functions. The results are visualized as glyphs and analyzed using a full set of specifically-designed interactions supported in our efficient web-based visualization tool. The usefulness of our method is demonstrated using various multi- and high-dimensional benchmark datasets. Our implicit differentiation vector transformation is evaluated through numerical comparisons; the overall method is evaluated through exploration examples and use cases.
Insights From Experiments With Rigor in an EvoBio Design Study
Authors: Jennifer Rogers, Austin H. Patton, Luke Harmon, Alexander Lex, Miriah D. Meyer
Published at InfoVis, 2020

Design study is an established approach of conducting problem-driven visualization research. The academic visualization community has produced a large body of work for reporting on design studies, informed by a handful of theoretical frameworks, and applied to a broad range of application areas. The result is an abundance of reported insights into visualization design, with an emphasis on novel visualization techniques and systems as the primary contribution of these studies. In recent work we proposed a new, interpretivist perspective on design study and six companion criteria for rigor that highlight the opportunities for researchers to contribute knowledge that extends beyond visualization idioms and software. In this work we conducted a year-long collaboration with evolutionary biologists to develop an interactive tool for visual exploration of multivariate datasets and phylogenetic trees. During this design study we experimented with methods to support three of the rigor criteria: ABUNDANT, REFLEXIVE, and TRANSPARENT. As a result we contribute two novel visualization techniques for the analysis of multivariate phylogenetic datasets, three methodological recommendations for conducting design studies drawn from reflections over our process of experimentation, and two writing devices for reporting interpretivist design study. We offer this work as an example for implementing the rigor criteria to produce a diverse range of knowledge contributions.
Introducing Layers of Meaning (LoM): A Framework to Reduce Semantic Distance of Visualization In Humanistic Research
Authors: Houda Lamqaddam, Andrew Vande Moere, Vero Vanden Abeele, Koenraad Brosens, Katrien Verbert
Published at InfoVis, 2020

Information visualization (infovis) is a powerful tool for exploring rich datasets. Within humanistic research, rich qualitative data and domain culture make traditional infovis approaches appear reductive and disconnected, leading to low adoption. In this paper, we use a multi-step approach to scrutinize the relationship between infovis and the humanities and suggest new directions for it. We first look into infovis from the humanistic perspective by exploring the humanistic literature around infovis. We validate and expand those findings though a co-design workshop with humanist and infovis experts. Then, we translate our findings into guidelines for designers and conduct a design critique exercise to explore their effect on the perception of humanist researchers. Based on these steps, we introduce Layers of Meaning, a framework to reduce the semantic distance between humanist researchers and visualizations of their research material, by grounding infovis tools in time and space, physicality, terminology, nuance, and provenance.
Investigating Visual Analysis of Differentially Private Data
Authors: Dan Zhang, Ali Sarvghad, Gerome Miklau
Published at InfoVis, 2020

Differential Privacy is an emerging privacy model with increasing popularity in many domains. It functions by adding carefully calibrated noise to data that blurs information about individuals while preserving overall statistics about the population. Theoretically, it is possible to produce robust privacy-preserving visualizations by plotting differentially private data. However, noise-induced data perturbations can alter visual patterns and impact the utility of a private visualization. We still know little about the challenges and opportunities for visual data exploration and analysis using private visualizations. As a first step towards filling this gap, we conducted a crowdsourced experiment, measuring participants' performance under three levels of privacy (high, low, non-private) for combinations of eight analysis tasks and four visualization types (bar chart, pie chart, line chart, scatter plot). Our findings show that for participants' accuracy for summary tasks (e.g., find clusters in data) was higher that value tasks (e.g., retrieve a certain value). We also found that under DP, pie chart and line chart offer similar or better accuracy than bar chart. In this work, we contribute the results of our empirical study, investigating the task-based effectiveness of basic private visualizations, a dichotomous model for defining and measuring user success in performing visual analysis tasks under DP, and a set of distribution metrics for tuning the injection to improve the utility of private visualizations.
Kyrix-S: Authoring Scalable Scatterplot Visualizations of Big Data
Authors: Wenbo Tao, Xinli Hou, Adam Sah, Leilani Battle, Remco Chang, Michael Stonebraker
Published at InfoVis, 2020

Static scatterplots often suffer from the overdraw problem on big datasets where object overlap causes undesirable visual clutter. The use of zooming in scatterplots can help alleviate this problem. With multiple zoom levels, more screen real estate is available, allowing objects to be placed in a less crowded way. We call this type of visualization scalable scatterplot visualizations, or SSV for short. Despite the potential of SSVs, existing systems and toolkits fall short in supporting the authoring of SSVs due to three limitations. First, many systems have limited scalability, assuming that data fits in the memory of one computer. Second, too much developer work, e.g., using custom code to generate mark layouts or render objects, is required. Third, many systems focus on only a small subset of the SSV design space (e.g. supporting a specific type of visual marks). To address these limitations, we have developed Kyrix-S, a system for easy authoring of SSVs at scale. Kyrix-S derives a declarative grammar that enables specification of a variety of SSVs in a few tens of lines of code, based on an existing survey of scatterplot tasks and designs. The declarative grammar is supported by a distributed layout algorithm which automatically places visual marks onto zoom levels. We store data in a multi-node database and use multi-node spatial indexes to achieve interactive browsing of large SSVs. Extensive experiments show that 1) Kyrix-S enables interactive browsing of SSVs of billions of objects, with response times under 500ms and 2) Kyrix-S achieves 4X-9X reduction in specification compared to a state-of-the-art authoring system.
Lyra 2: Designing Interactive Visualizations by Demonstration
Authors: Jonathan Zong, Dhiraj Barnwal, Rupayan Neogy, Arvind Satyanarayan
Published at InfoVis, 2020

Recent graphical interfaces offer direct manipulation mechanisms for authoring visualizations, but are largely restricted to static output. To author interactive visualizations, users must instead turn to textual specification, but such approaches impose a higher technical burden. To bridge this gap, we introduce Lyra 2, a system that extends a prior visualization design environment with novel methods for authoring interaction techniques by demonstration. Users perform an interaction (e.g., button clicks, drags, or key presses) directly on the visualization they are editing. The system interprets this performance using a set of heuristics and enumerates suggestions of possible interaction designs. These heuristics account for the properties of the interaction (e.g., target and event type) as well as the visualization (e.g., mark and scale types, and multiple views). Interaction design suggestions are displayed as thumbnails; users can preview and test these suggestions, iteratively refine them through additional demonstrations, and finally apply and customize them via property inspectors. We evaluate our approach through a gallery of diverse examples, and evaluate its usability through a first-use study and via an analysis of its cognitive dimensions. We find that, in Lyra 2, interaction design by demonstration enables users to rapidly express a wide range of interactive visualizations.
MetroSets: Visualizing Sets as Metro Maps
Authors: Ben Jacobsen, Markus Wallinger, Stephen G. Kobourov, Martin Nöllenburg
Published at InfoVis, 2020

We propose MetroSets, a new, flexible online tool for visualizing set systems using the metro map metaphor. We model a given set system as a hypergraph $\mathcal{H}=(V,\ \mathcal{S})$, consisting of a set $V$ of vertices and a set $\mathcal{S}$, which contains subsets of $V$ called hyperedges. Our system then computes a metro map representation of $\mathcal{H}$, where each hyperedge $E$ in $\mathcal{S}$ corresponds to a metro line and each vertex corresponds to a metro station. Vertices that appear in two or more hyperedges are drawn as interchanges in the metro map, connecting the different sets. MetroSets is based on a modular 4-step pipeline which constructs and optimizes a path-based hypergraph support, which is then drawn and schematized using metro map layout algorithms. We propose and implement multiple algorithms for each step of the MetroSet pipeline and provide a functional prototype with easy-to-use preset configurations. Furthermore, using several real-world datasets, we perform an extensive quantitative evaluation of the impact of different pipeline stages on desirable properties of the generated maps, such as octolinearity, monotonicity, and edge uniformity.
MobileVisFixer: Tailoring Web Visualizations for Mobile Phones Leveraging an Explainable Reinforcement Learning Framework
Authors: Aoyu Wu, Wai Tong, Tim Dwyer, Bongshin Lee, Petra Isenberg, Huamin Qu
Published at InfoVis, 2020

We contribute MobileVisFixer, a new method to make visualizations more mobile-friendly. Although mobile devices have become the primary means of accessing information on the web, many existing visualizations are not optimized for small screens and can lead to a frustrating user experience. Currently, practitioners and researchers have to engage in a tedious and time-consuming process to ensure that their designs scale to screens of different sizes, and existing toolkits and libraries provide little support in diagnosing and repairing issues. To address this challenge, MobileVisFixer automates a mobile-friendly visualization re-design process with a novel reinforcement learning framework. To inform the design of MobileVisFixer, we first collected and analyzed SVG-based visualizations on the web, and identified five common mobile-friendly issues. MobileVisFixer addresses four of these issues on single-view Cartesian visualizations with linear or discrete scales by a Markov Decision Process model that is both generalizable across various visualizations and fully explainable. MobileVisFixer deconstructs charts into declarative formats, and uses a greedy heuristic based on Policy Gradient methods to find solutions to this difficult, multi-criteria optimization problem in reasonable time. In addition, MobileVisFixer can be easily extended with the incorporation of optimization algorithms for data visualizations. Quantitative evaluation on two real-world datasets demonstrates the effectiveness and generalizability of our method.
Modeling the Influence of Visual Density on Cluster Perception in Scatterplots Using Topology
Authors: Ghulam Jilani Quadri, Paul Rosen 0001
Published at InfoVis, 2020

Scatterplots are used for a variety of visual analytics tasks, including cluster identification, and the visual encodings used on a scatterplot play a deciding role on the level of visual separation of clusters. For visualization designers, optimizing the visual encodings is crucial to maximizing the clarity of data. This requires accurately modeling human perception of cluster separation, which remains challenging. We present a multi-stage user study focusing on four factors-distribution size of clusters, number of points, size of points, and opacity of points-that influence cluster identification in scatterplots. From these parameters, we have constructed two models, a distance-based model, and a density-based model, using the merge tree data structure from Topological Data Analysis. Our analysis demonstrates that these factors play an important role in the number of clusters perceived, and it verifies that the distance-based and density-based models can reasonably estimate the number of clusters a user observes. Finally, we demonstrate how these models can be used to optimize visual encodings on real-world data.
Multi-Perspective, Simultaneous Embedding
Authors: Md. Iqbal Hossain, Vahan Huroyan, Stephen G. Kobourov, Raymundo Navarrete
Published at InfoVis, 2020

We describe MPSE: a Multi-Perspective Simultaneous Embedding method for visualizing high-dimensional data, based on multiple pairwise distances between the data points. Specifically, MPSE computes positions for the points in 3D and provides different views into the data by means of 2D projections (planes) that preserve each of the given distance matrices. We consider two versions of the problem: fixed projections and variable projections. MPSE with fixed projections takes as input a set of pairwise distance matrices defined on the data points, along with the same number of projections and embeds the points in 3D so that the pairwise distances are preserved in the given projections. MPSE with variable projections takes as input a set of pairwise distance matrices and embeds the points in 3D while also computing the appropriate projections that preserve the pairwise distances. The proposed approach can be useful in multiple scenarios: from creating simultaneous embedding of multiple graphs on the same set of vertices, to reconstructing a 3D object from multiple 2D snapshots, to analyzing data from multiple points of view. We provide a functional prototype of MPSE that is based on an adaptive and stochastic generalization of multi-dimensional scaling to multiple distances and multiple variable projections. We provide an extensive quantitative evaluation with datasets of different sizes and using different number of projections, as well as several examples that illustrate the quality of the resulting solutions.
NL4DV: A Toolkit for Generating Analytic Specifications for Data Visualization from Natural Language Queries
Authors: Arpit Narechania, Arjun Srinivasan, John T. Stasko
Published at InfoVis, 2020

Natural language interfaces (NLls) have shown great promise for visual data analysis, allowing people to flexibly specify and interact with visualizations. However, developing visualization NLIs remains a challenging task, requiring low-level implementation of natural language processing (NLP) techniques as well as knowledge of visual analytic tasks and visualization design. We present NL4DV, a toolkit for natural language-driven data visualization. NL4DV is a Python package that takes as input a tabular dataset and a natural language query about that dataset. In response, the toolkit returns an analytic specification modeled as a JSON object containing data attributes, analytic tasks, and a list of Vega-Lite specifications relevant to the input query. In doing so, NL4DV aids visualization developers who may not have a background in NLP, enabling them to create new visualization NLIs or incorporate natural language input within their existing systems. We demonstrate NL4DV's usage and capabilities through four examples: 1) rendering visualizations using natural language in a Jupyter notebook, 2) developing a NLI to specify and edit Vega-Lite charts, 3) recreating data ambiguity widgets from the DataTone system, and 4) incorporating speech input to create a multimodal visualization system.
No mark is an island: Precision and category repulsion biases in data reproductions
Authors: Caitlyn M. McColeman, Lane Harrison, Mi Feng, Steven Franconeri
Published at InfoVis, 2020

Data visualization is powerful in large part because it facilitates visual extraction of values. Yet, existing measures of perceptual precision for data channels (e.g., position, length, orientation, etc.) are based largely on verbal reports of ratio judgments between two values (e.g., [7]). Verbal report conflates multiple sources of error beyond actual visual precision, introducing a ratio computation between these values and a requirement to translate that ratio to a verbal number. Here we observe raw measures of precision by eliminating both ratio computations and verbal reports; we simply ask participants to reproduce marks (a single bar or dot) to match a previously seen one. We manipulated whether the mark was initially presented (and later drawn) alone, paired with a reference (e.g. a second ‘100%’ bar also present at test, or a y-axis for the dot), or integrated with the reference (merging that reference bar into a stacked bar graph, or placing the dot directly on the axis). Reproductions of smaller values were overestimated, and larger values were underestimated, suggesting systematic memory biases. Average reproduction error was around 10% of the actual value, regardless of whether the reproduction was done on a common baseline with the original. In the reference and (especially) the integrated conditions, responses were repulsed from an implicit midpoint of the reference mark, such that values above 50% were overestimated, and values below 50% were underestimated. This reproduction paradigm may serve within a new suite of more fundamental measures of the precision of graphical perception.
Palettailor: Discriminable Colorization for Categorical Data
Authors: Kecheng Lu, Mi Feng, Xin Chen, Michael Sedlmair, Oliver Deussen, Dani Lischinski, Zhanglin Cheng, Yunhai Wang
Published at InfoVis, 2020

We present an integrated approach for creating and assigning color palettes to different visualizations such as multi-class scatterplots, line, and bar charts. While other methods separate the creation of colors from their assignment, our approach takes data characteristics into account to produce color palettes, which are then assigned in a way that fosters better visual discrimination of classes. To do so, we use a customized optimization based on simulated annealing to maximize the combination of three carefully designed color scoring functions: point distinctness, name difference, and color discrimination. We compare our approach to state-of-the-art palettes with a controlled user study for scatterplots and line charts, furthermore we performed a case study. Our results show that Palettailor, as a fully-automated approach, generates color palettes with a higher discrimination quality than existing approaches. The efficiency of our optimization allows us also to incorporate user modifications into the color selection process.