DevLan/vispubs huggingface dataset:
A Comparative Evaluation of Animation and Small Multiples for Trend Visualization on Mobile Phones
Authors: Matthew Brehmer, Bongshin Lee, Petra Isenberg, Eun Kyoung Choe
Published at InfoVis, 2019

We compare the efficacy of animated and small multiples variants of scatterplots on mobile phones for comparing trends in multivariate datasets. Visualization is increasingly prevalent in mobile applications and mobile-first websites, yet there is little prior visualization research dedicated to small displays. In this paper, we build upon previous experimental research carried out on larger displays that assessed animated and non-animated variants of scatterplots. Incorporating similar experimental stimuli and tasks, we conducted an experiment where 96 crowdworker participants performed nine trend comparison tasks using their mobile phones. We found that those using a small multiples design consistently completed tasks in less time, albeit with slightly less confidence than those using an animated design. The accuracy results were more task-dependent, and we further interpret our results according to the characteristics of the individual tasks, with a specific focus on the trajectories of target and distractor data items in each task. We identify cases that appear to favor either animation or small multiples, providing new questions for further experimental research and implications for visualization design on mobile devices. Lastly, we provide a reflection on our evaluation methodology.
A Comparison of Radial and Linear Charts for Visualizing Daily Patterns
Authors: Manuela Waldner, Alexandra Diehl, Denis Gracanin, Rainer Splechtna, Claudio Delrieux, Kresimir Matkovic
Published at InfoVis, 2019

Radial charts are generally considered less effective than linear charts. Perhaps the only exception is in visualizing periodical time-dependent data, which is believed to be naturally supported by the radial layout. It has been demonstrated that the drawbacks of radial charts outweigh the benefits of this natural mapping. Visualization of daily patterns, as a special case, has not been systematically evaluated using radial charts. In contrast to yearly or weekly recurrent trends, the analysis of daily patterns on a radial chart may benefit from our trained skill on reading radial clocks that are ubiquitous in our culture. In a crowd-sourced experiment with 92 non-expert users, we evaluated the accuracy, efficiency, and subjective ratings of radial and linear charts for visualizing daily traffic accident patterns. We systematically compared juxtaposed 12-hours variants and single 24-hours variants for both layouts in four low-level tasks and one high-level interpretation task. Our results show that over all tasks, the most elementary 24-hours linear bar chart is most accurate and efficient and is also preferred by the users. This provides strong evidence for the use of linear layouts – even for visualizing periodical daily patterns.
A Comparison of Visualizations for Identifying Correlation over Space and Time
Authors: Vanessa Peña Araya, Emmanuel Pietriga, Anastasia Bezerianos
Published at InfoVis, 2019

Observing the relationship between two or more variables over space and time is essential in many domains. For instance, looking, for different countries, at the evolution of both the life expectancy at birth and the fertility rate will give an overview of their demographics. The choice of visual representation for such multivariate data is key to enabling analysts to extract patterns and trends. Prior work has compared geo-temporal visualization techniques for a single thematic variable that evolves over space and time, or for two variables at a specific point in time. But how effective visualization techniques are at communicating correlation between two variables that evolve over space and time remains to be investigated. We report on a study comparing three techniques that are representative of different strategies to visualize geo-temporal multivariate data: either juxtaposing all locations for a given time step, or juxtaposing all time steps for a given location; and encoding thematic attributes either using symbols overlaid on top of map features, or using visual channels of the map features themselves. Participants performed a series of tasks that required them to identify if two variables were correlated over time and if there was a pattern in their evolution. Tasks varied in granularity for both dimensions: time (all time steps, a subrange of steps, one step only) and space (all locations, locations in a subregion, one location only). Our results show that a visualization's effectiveness depends strongly on the task to be carried out. Based on these findings we present a set of design guidelines about geo-temporal visualization techniques for communicating correlation.
A Deep Generative Model for Graph Layout
Authors: Oh-Hyun Kwon, Kwan-Liu Ma
Published at InfoVis, 2019

Different layouts can characterize different aspects of the same graph. Finding a “good” layout of a graph is thus an important task for graph visualization. In practice, users often visualize a graph in multiple layouts by using different methods and varying parameter settings until they find a layout that best suits the purpose of the visualization. However, this trial-and-error process is often haphazard and time-consuming. To provide users with an intuitive way to navigate the layout design space, we present a technique to systematically visualize a graph in diverse layouts using deep generative models. We design an encoder-decoder architecture to learn a model from a collection of example layouts, where the encoder represents training examples in a latent space and the decoder produces layouts from the latent space. In particular, we train the model to construct a two-dimensional latent space for users to easily explore and generate various layouts. We demonstrate our approach through quantitative and qualitative evaluations of the generated layouts. The results of our evaluations show that our model is capable of learning and generalizing abstract concepts of graph layouts, not just memorizing the training examples. In summary, this paper presents a fundamentally new approach to graph visualization where a machine learning model learns to visualize a graph from examples without manually-defined heuristics.
A Recursive Subdivision Technique for Sampling Multi-class Scatterplots
Authors: Xin Chen, Tong Ge, Jian Zhang 0070, Baoquan Chen, Chi-Wing Fu, Oliver Deussen, Yunhai Wang
Published at InfoVis, 2019

We present a non-uniform recursive sampling technique for multi-class scatterplots, with the specific goal of faithfully presenting relative data and class densities, while preserving major outliers in the plots. Our technique is based on a customized binary kd-tree, in which leaf nodes are created by recursively subdividing the underlying multi-class density map. By backtracking, we merge leaf nodes until they encompass points of all classes for our subsequently applied outlier-aware multi-class sampling strategy. A quantitative evaluation shows that our approach can better preserve outliers and at the same time relative densities in multi-class scatterplots compared to the previous approaches, several case studies demonstrate the effectiveness of our approach in exploring complex and real world data.
An Incremental Dimensionality Reduction Method for Visualizing Streaming Multidimensional Data
Authors: Takanori Fujiwara, Jia-Kai Chou, Shilpika, Panpan Xu, Ren Liu, Kwan-Liu Ma
Published at InfoVis, 2019

Dimensionality reduction (DR) methods are commonly used for analyzing and visualizing multidimensional data. However, when data is a live streaming feed, conventional DR methods cannot be directly used because of their computational complexity and inability to preserve the projected data positions at previous time points. In addition, the problem becomes even more challenging when the dynamic data records have a varying number of dimensions as often found in real-world applications. This paper presents an incremental DR solution. We enhance an existing incremental PCA method in several ways to ensure its usability for visualizing streaming multidimensional data. First, we use geometric transformation and animation methods to help preserve a viewer's mental map when visualizing the incremental results. Second, to handle data dimension variants, we use an optimization method to estimate the projected data positions, and also convey the resulting uncertainty in the visualization. We demonstrate the effectiveness of our design with two case studies using real-world datasets.
BarcodeTree: Scalable Comparison of Multiple Hierarchies
Authors: Guozheng Li 0002, Yu Zhang 0043, Yu Dong, Christy Jie Liang, Jinson Zhang, Jinsong Wang, Michael J. McGuffin, Xiaoru Yuan
Published at InfoVis, 2019

We propose BarcodeTree (BCT), a novel visualization technique for comparing topological structures and node attribute values of multiple trees. BCT can provide an overview of one hundred shallow and stable trees simultaneously, without aggregating individual nodes. Each BCT is shown within a single row using a style similar to a barcode, allowing trees to be stacked vertically with matching nodes aligned horizontally to ease comparison and maintain space efficiency. We design several visual cues and interactive techniques to help users understand the topological structure and compare trees. In an experiment comparing two variants of BCT with icicle plots, the results suggest that BCTs make it easier to visually compare trees by reducing the vertical distance between different trees. We also present two case studies involving a dataset of hundreds of trees to demonstrate BCT's utility.
Biased Average Position Estimates in Line and Bar Graphs: Underestimation, Overestimation, and Perceptual Pull
Authors: Cindy Xiong, Cristina R. Ceja, Casimir J. H. Ludwig, Steven Franconeri
Published at InfoVis, 2019

In visual depictions of data, position (i.e., the vertical height of a line or a bar) is believed to be the most precise way to encode information compared to other encodings (e.g., hue). Not only are other encodings less precise than position, but they can also be prone to systematic biases (e.g., color category boundaries can distort perceived differences between hues). By comparison, position's high level of precision may seem to protect it from such biases. In contrast, across three empirical studies, we show that while position may be a precise form of data encoding, it can also produce systematic biases in how values are visually encoded, at least for reports of average position across a short delay. In displays with a single line or a single set of bars, reports of average positions were significantly biased, such that line positions were underestimated and bar positions were overestimated. In displays with multiple data series (i.e., multiple lines and/or sets of bars), this systematic bias still persisted. We also observed an effect of “perceptual pull”, where the average position estimate for each series was ‘pulled’ toward the other. These findings suggest that, although position may still be the most precise form of visual data encoding, it can also be systematically biased.
CerebroVis: Designing an Abstract yet Spatially Contextualized Cerebral Artery Network Visualization
Authors: Aditeya Pandey, Harsh Shukla, Geoffrey S. Young, Lei Qin, Amir A. Zamani, Liangge Hsu, Raymond Huang, Cody Dunne, Michelle Borkin
Published at InfoVis, 2019

Blood circulation in the human brain is supplied through a network of cerebral arteries. If a clinician suspects a patient has a stroke or other cerebrovascular condition, they order imaging tests. Neuroradiologists visually search the resulting scans for abnormalities. Their visual search tasks correspond to the abstract network analysis tasks of browsing and path following. To assist neuroradiologists in identifying cerebral artery abnormalities, we designed CerebroVis, a novel abstract—yet spatially contextualized—cerebral artery network visualization. In this design study, we contribute a novel framing and definition of the cerebral artery system in terms of network theory and characterize neuroradiologist domain goals as abstract visualization and network analysis tasks. Through an iterative, user-centered design process we developed an abstract network layout technique which incorporates cerebral artery spatial context. The abstract visualization enables increased domain task performance over 3D geometry representations, while including spatial context helps preserve the user's mental map of the underlying geometry. We provide open source implementations of our network layout technique and prototype cerebral artery visualization tool. We demonstrate the robustness of our technique by successfully laying out 61 open source brain scans. We evaluate the effectiveness of our layout through a mixed methods study with three neuroradiologists. In a formative controlled experiment our study participants used CerebroVis and a conventional 3D visualization to examine real cerebral artery imaging data to identify a simulated intracranial artery stenosis. Participants were more accurate at identifying stenoses using CerebroVis (absolute risk difference 13%). A free copy of this paper, the evaluation stimuli and data, and source code are available at osf.io/e5sxt.
Color Crafting: Automating the Construction of Designer Quality Color Ramps
Authors: Stephen Smart, Keke Wu, Danielle Albers Szafir
Published at InfoVis, 2019

Visualizations often encode numeric data using sequential and diverging color ramps. Effective ramps use colors that are sufficiently discriminable, align well with the data, and are aesthetically pleasing. Designers rely on years of experience to create high-quality color ramps. However, it is challenging for novice visualization developers that lack this experience to craft effective ramps as most guidelines for constructing ramps are loosely defined qualitative heuristics that are often difficult to apply. Our goal is to enable visualization developers to readily create effective color encodings using a single seed color. We do this using an algorithmic approach that models designer practices by analyzing patterns in the structure of designer-crafted color ramps. We construct these models from a corpus of 222 expert-designed color ramps, and use the results to automatically generate ramps that mimic designer practices. We evaluate our approach through an empirical study comparing the outputs of our approach with designer-crafted color ramps. Our models produce ramps that support accurate and aesthetically pleasing visualizations at least as well as designer ramps and that outperform conventional mathematical approaches.
Common Fate for Animated Transitions in Visualization
Authors: Amira Chalbi, Jacob Ritchie, Deok Gun Park 0001, Jungu Choi, Nicolas Roussel 0001, Niklas Elmqvist, Fanny Chevalier
Published at InfoVis, 2019

The Law of Common Fate from Gestalt psychology states that visual objects moving with the same velocity along parallel trajectories will be perceived by a human observer as grouped. However, the concept of common fate is much broader than mere velocity; in this paper we explore how common fate results from coordinated changes in luminance and size. We present results from a crowdsourced graphical perception study where we asked workers to make perceptual judgments on a series of trials involving four graphical objects under the influence of conflicting static and dynamic visual factors (position, size and luminance) used in conjunction. Our results yield the following rankings for visual grouping: motion > (dynamic luminance, size, luminance); dynamic size > (dynamic luminance, position); and dynamic luminance > size. We also conducted a follow-up experiment to evaluate the three dynamic visual factors in a more ecologically valid setting, using both a Gapminder-like animated scatterplot and a thematic map of election data. The results indicate that in practice the relative grouping strengths of these factors may depend on various parameters including the visualization characteristics and the underlying data. We discuss design implications for animated transitions in data visualization.
Construct-A-Vis: Exploring the Free-Form Visualization Processes of Children
Authors: Fearn Bishop, Johannes Zagermann, Ulrike Pfeil, Gemma Sanderson, Harald Reiterer, Uta Hinrichs
Published at InfoVis, 2019

Building data analysis skills is part of modern elementary school curricula. Recent research has explored how to facilitate children's understanding of visual data representations through completion exercises which highlight links between concrete and abstract mappings. This approach scaffolds visualization activities by presenting a target visualization to children. But how can we engage children in more free-form visual data mapping exercises that are driven by their own mapping ideas? How can we scaffold a creative exploration of visualization techniques and mapping possibilities? We present Construct-A-Vis, a tablet-based tool designed to explore the feasibility of free-form and constructive visualization activities with elementary school children. Construct-A-Vis provides adjustable levels of scaffolding visual mapping processes. It can be used by children individually or as part of collaborative activities. Findings from a study with elementary school children using Construct-A-Vis individually and in pairs highlight the potential of this free-form constructive approach, as visible in children's diverse visualization outcomes and their critical engagement with the data and mapping processes. Based on our study findings we contribute insights into the design of free-form visualization tools for children, including the role of tool-based scaffolding mechanisms and shared interactions to guide visualization activities with children.
Criteria for Rigor in Visualization Design Study
Authors: Miriah D. Meyer, Jason Dykes
Published at InfoVis, 2019

We develop a new perspective on research conducted through visualization design study that emphasizes design as a method of inquiry and the broad range of knowledge-contributions achieved through it as multiple, subjective, and socially constructed. From this interpretivist position we explore the nature of visualization design study and develop six criteria for rigor. We propose that rigor is established and judged according to the extent to which visualization design study research and its reporting are INFORMED, REFLEXIVE, ABUNDANT, PLAUSIBLE, RESONANT, and TRANSPARENT. This perspective and the criteria were constructed through a four-year engagement with the discourse around rigor and the nature of knowledge in social science, information systems, and design. We suggest methods from cognate disciplines that can support visualization researchers in meeting these criteria during the planning, execution, and reporting of design study. Through a series of deliberately provocative questions, we explore implications of this new perspective for design study research in visualization, concluding that as a discipline, visualization is not yet well positioned to embrace, nurture, and fully benefit from a rigorous, interpretivist approach to design study. The perspective and criteria we present are intended to stimulate dialogue and debate around the nature of visualization design study and the broader underpinnings of the discipline.
Critical Reflections on Visualization Authoring Systems
Authors: Arvind Satyanarayan, Bongshin Lee, Donghao Ren, Jeffrey Heer, John T. Stasko, John Thompson 0002, Matthew Brehmer, Zhicheng Liu
Published at InfoVis, 2019

An emerging generation of visualization authoring systems support expressive information visualization without textual programming. As they vary in their visualization models, system architectures, and user interfaces, it is challenging to directly compare these systems using traditional evaluative methods. Recognizing the value of contextualizing our decisions in the broader design space, we present critical reflections on three systems we developed —Lyra, Data Illustrator, and Charticulator. This paper surfaces knowledge that would have been daunting within the constituent papers of these three systems. We compare and contrast their (previously unmentioned) limitations and trade-offs between expressivity and learnability. We also reflect on common assumptions that we made during the development of our systems, thereby informing future research directions in visualization authoring systems.
Data by Proxy — Material Traces as Autographic Visualizations
Authors: Dietmar Offenhuber
Published at InfoVis, 2019

Information visualization limits itself, per definition, to the domain of symbolic information. This paper discusses arguments why the field should also consider forms of data that are not symbolically encoded, including physical traces and material indicators. Continuing a provocation presented by Pat Hanrahan in his 2004 IEEE Vis capstone address, this paper compares physical traces to visualizations and describes the techniques and visual practices for producing, revealing, and interpreting them. By contrasting information visualization with a speculative counter model of autographic visualization, this paper examines the design principles for material data. Autographic visualization addresses limitations of information visualization, such as the inability to directly reflect the material circumstances of data generation. The comparison between the two models allows probing the epistemic assumptions behind information visualization and uncovers linkages with the rich history of scientific visualization and trace reading. The paper begins by discussing the gap between data visualizations and their corresponding phenomena and proceeds by investigating how material visualizations can bridge this gap. It contextualizes autographic visualization with paradigms such as data physicalization and indexical visualization and grounds it in the broader theoretical literature of semiotics, science and technology studies (STS), and the history of scientific representation. The main section of the paper proposes a foundational design vocabulary for autographic visualization and offers examples of how citizen scientists already use autographic principles in their displays, which seem to violate the canonical principles of information visualization but succeed at fulfilling other rhetorical purposes in evidence construction. The paper concludes with a discussion of the limitations of autographic visualization, a roadmap for the empirical investigation of trace perception, and thoughts about how information visualization and autographic visualization techniques can contribute to each other.