DevLan/vispubs huggingface dataset:
Measures of the Benefit of Direct Encoding of Data Deltas for Data Pair Relation Perception
Authors: Christine Nothelfer, Steven Franconeri
Published at InfoVis, 2019

The power of data visualization is not to convey absolute values of individual data points, but to allow the exploration of relations (increases or decreases in a data value) among them. One approach to highlighting these relations is to explicitly encode the numeric differences (deltas) between data values. Because this approach removes the context of the individual data values, it is important to measure how much of a performance improvement it actually offers, especially across differences in encodings and tasks, to ensure that it is worth adding to a visualization design. Across 3 different tasks, we measured the increase in visual processing efficiency for judging the relations between pairs of data values, from when only the values were shown, to when the deltas between the values were explicitly encoded, across position and length visual feature encodings (and slope encodings in Experiments 1 & 2). In Experiment 1, the participant's task was to locate a pair of data values with a given relation (e.g., Find the ‘small bar to the left of a tall bar’ pair) among pairs of the opposite relation, and we measured processing efficiency from the increase in response times as the number of pairs increased. In Experiment 2, the task was to judge which of two relation types was more prevalent in a briefly presented display of 10 data pairs (e.g., Are there more ‘small bar to the left of a tall bar’ pairs or more ‘tall bar to the left of a small bar’ pairs?). In the final experiment, the task was to estimate the average delta within a briefly presented display of 6 data pairs (e.g., What is the average bar height difference across all ‘small bar to the left of a tall bar’ pairs?). Across all three experiments, visual processing of relations between data value pairs was significantly better when directly encoded as deltas rather than implicitly between individual data points, and varied substantially depending on the task (improvement ranged from 25% to 95%). Considering the ubiquity of bar charts and dot plots, relation perception for individual data values is highly inefficient, and confirms the need for alternative designs that provide not only absolute values, but also direct encoding of critical relationships between those values.
OntoPlot: A Novel Visualisation for Non-hierarchical Associations in Large Ontologies
Authors: Ying Yang, Michael Wybrow, Yuan-Fang Li, Tobias Czauderna, Yongqun He
Published at InfoVis, 2019

Ontologies are formal representations of concepts and complex relationships among them. They have been widely used to capture comprehensive domain knowledge in areas such as biology and medicine, where large and complex ontologies can contain hundreds of thousands of concepts. Especially due to the large size of ontologies, visualisation is useful for authoring, exploring and understanding their underlying data. Existing ontology visualisation tools generally focus on the hierarchical structure, giving much less emphasis to non-hierarchical associations. In this paper we present OntoPlot, a novel visualisation specifically designed to facilitate the exploration of all concept associations whilst still showing an ontology's large hierarchical structure. This hybrid visualisation combines icicle plots, visual compression techniques and interactivity, improving space-efficiency and reducing visual structural complexity. We conducted a user study with domain experts to evaluate the usability of OntoPlot, comparing it with the de facto ontology editor Protégé. The results confirm that OntoPlot attains our design goals for association-related tasks and is strongly favoured by domain experts.
P5: Portable Progressive Parallel Processing Pipelines for Interactive Data Analysis and Visualization
Authors: Jianping Kelvin Li, Kwan-Liu Ma
Published at InfoVis, 2019

We present P5, a web-based visualization toolkit that combines declarative visualization grammar and GPU computing for progressive data analysis and visualization. To interactively analyze and explore big data, progressive analytics and visualization methods have recently emerged. Progressive visualizations of incrementally refining results have the advantages of allowing users to steer the analysis process and make early decisions. P5 leverages declarative grammar for specifying visualization designs and exploits GPU computing to accelerate progressive data processing and rendering. The declarative specifications can be modified during progressive processing to create different visualizations for analyzing the intermediate results. To enable user interactions for progressive data analysis, P5 utilizes the GPU to automatically aggregate and index data based on declarative interaction specifications to facilitate effective interactive visualization. We demonstrate the effectiveness and usefulness of P5 through a variety of example applications and several performance benchmark tests.
Pattern-Driven Navigation in 2D Multiscale Visualizations with Scalable Insets
Authors: Fritz Lekschas, Michael Behrisch 0001, Benjamin Bach, Peter Kerpedjiev, Nils Gehlenborg, Hanspeter Pfister
Published at InfoVis, 2019

We present Scalable Insets, a technique for interactively exploring and navigating large numbers of annotated patterns in multiscale visualizations such as gigapixel images, matrices, or maps. Exploration of many but sparsely-distributed patterns in multiscale visualizations is challenging as visual representations change across zoom levels, context and navigational cues get lost upon zooming, and navigation is time consuming. Our technique visualizes annotated patterns too small to be identifiable at certain zoom levels using insets, i.e., magnified thumbnail views of the annotated patterns. Insets support users in searching, comparing, and contextualizing patterns while reducing the amount of navigation needed. They are dynamically placed either within the viewport or along the boundary of the viewport to offer a compromise between locality and context preservation. Annotated patterns are interactively clustered by location and type. They are visually represented as an aggregated inset to provide scalable exploration within a single viewport. In a controlled user study with 18 participants, we found that Scalable Insets can speed up visual search and improve the accuracy of pattern comparison at the cost of slower frequency estimation compared to a baseline technique. A second study with 6 experts in the field of genomics showed that Scalable Insets is easy to learn and provides first insights into how Scalable Insets can be applied in an open-ended data exploration scenario.
Persistent Homology Guided Force-Directed Graph Layouts
Authors: Ashley Suh, Mustafa Hajij, Bei Wang 0001, Carlos Scheidegger, Paul Rosen 0001
Published at InfoVis, 2019

Graphs are commonly used to encode relationships among entities, yet their abstractness makes them difficult to analyze. Node-link diagrams are popular for drawing graphs, and force-directed layouts provide a flexible method for node arrangements that use local relationships in an attempt to reveal the global shape of the graph. However, clutter and overlap of unrelated structures can lead to confusing graph visualizations. This paper leverages the persistent homology features of an undirected graph as derived information for interactive manipulation of force-directed layouts. We first discuss how to efficiently extract 0-dimensional persistent homology features from both weighted and unweighted undirected graphs. We then introduce the interactive persistence barcode used to manipulate the force-directed graph layout. In particular, the user adds and removes contracting and repulsing forces generated by the persistent homology features, eventually selecting the set of persistent homology features that most improve the layout. Finally, we demonstrate the utility of our approach across a variety of synthetic and real datasets.
RSATree: Distribution-Aware Data Representation of Large-Scale Tabular Datasets for Flexible Visual Query
Authors: Honghui Mei, Wei Chen 0001, Yating Wei, Yuanzhe Hu, Shuyue Zhou, Bingru Lin, Ying Zhao 0001, Jiazhi Xia
Published at InfoVis, 2019

Analysts commonly investigate the data distributions derived from statistical aggregations of data that are represented by charts, such as histograms and binned scatterplots, to visualize and analyze a large-scale dataset. Aggregate queries are implicitly executed through such a process. Datasets are constantly extremely large; thus, the response time should be accelerated by calculating predefined data cubes. However, the queries are limited to the predefined binning schema of preprocessed data cubes. Such limitation hinders analysts' flexible adjustment of visual specifications to investigate the implicit patterns in the data effectively. Particularly, RSATree enables arbitrary queries and flexible binning strategies by leveraging three schemes, namely, an R-tree-based space partitioning scheme to catch the data distribution, a locality-sensitive hashing technique to achieve locality-preserving random access to data items, and a summed area table scheme to support interactive query of aggregated values with a linear computational complexity. This study presents and implements a web-based visual query system that supports visual specification, query, and exploration of large-scale tabular data with user-adjustable granularities. We demonstrate the efficiency and utility of our approach by performing various experiments on real-world datasets and analyzing time and space complexity.
Searching the Visual Style and Structure of D3 Visualizations
Authors: Enamul Hoque, Maneesh Agrawala
Published at InfoVis, 2019

We present a search engine for D3 visualizations that allows queries based on their visual style and underlying structure. To build the engine we crawl a collection of 7860 D3 visualizations from the Web and deconstruct each one to recover its data, its data-encoding marks and the encodings describing how the data is mapped to visual attributes of the marks. We also extract axes and other non-data-encoding attributes of marks (e.g., typeface, background color). Our search engine indexes this style and structure information as well as metadata about the webpage containing the chart. We show how visualization developers can search the collection to find visualizations that exhibit specific design characteristics and thereby explore the space of possible designs. We also demonstrate how researchers can use the search engine to identify commonly used visual design patterns and we perform such a demographic design analysis across our collection of D3 charts. A user study reveals that visualization developers found our style and structure based search engine to be significantly more useful and satisfying for finding different designs of D3 charts, than a baseline search engine that only allows keyword search over the webpage containing a chart.
Separating the Wheat from the Chaff: Comparative Visual Cues for Transparent Diagnostics of Competing Models
Authors: Aritra Dasgupta, Hong Wang, Nancy O'Brien, Susannah Burrows
Published at InfoVis, 2019

Experts in data and physical sciences have to regularly grapple with the problem of competing models. Be it analytical or physics-based models, a cross-cutting challenge for experts is to reliably diagnose which model outcomes appropriately predict or simulate real-world phenomena. Expert judgment involves reconciling information across many, and often, conflicting criteria that describe the quality of model outcomes. In this paper, through a design study with climate scientists, we develop a deeper understanding of the problem and solution space of model diagnostics, resulting in the following contributions: i) a problem and task characterization using which we map experts' model diagnostics goals to multi-way visual comparison tasks, ii) a design space of comparative visual cues for letting experts quickly understand the degree of disagreement among competing models and gauge the degree of stability of model outputs with respect to alternative criteria, and iii) design and evaluation of MyriadCues, an interactive visualization interface for exploring alternative hypotheses and insights about good and bad models by leveraging comparative visual cues. We present case studies and subjective feedback by experts, which validate how MyriadCues enables more transparent model diagnostic mechanisms, as compared to the state of the art.
ShapeWordle: Tailoring Wordles using Shape-aware Archimedean Spirals
Authors: Yunhai Wang, Xiaowei Chu, Kaiyi Zhang, Chen Bao, Xiaotong Li, Jian Zhang 0070, Chi-Wing Fu, Christophe Hurter, Bongshin Lee, Oliver Deussen
Published at InfoVis, 2019

We present a new technique to enable the creation of shape-bounded Wordles, we call ShapeWordle, in which we fit words to form a given shape. To guide word placement within a shape, we extend the traditional Archimedean spirals to be shape-aware by formulating the spirals in a differential form using the distance field of the shape. To handle non-convex shapes, we introduce a multi-centric Wordle layout method that segments the shape into parts for our shape-aware spirals to adaptively fill the space and generate word placements. In addition, we offer a set of editing interactions to facilitate the creation of semantically-meaningful Wordles. Lastly, we present three evaluations: a comprehensive comparison of our results against the state-of-the-art technique (WordArt), case studies with 14 users, and a gallery to showcase the coverage of our technique.
SmartCube: An Adaptive Data Management Architecture for the Real-Time Visualization of Spatiotemporal Datasets
Authors: Can Liu 0004, Cong Wu 0004, Hanning Shao, Xiaoru Yuan
Published at InfoVis, 2019

Interactive visualization and exploration of large spatiotemporal data sets is difficult without carefully-designed data pre-processing and management tools. We propose a novel architecture for spatiotemporal data management. The architecture can dynamically update itself based on user queries. Datasets is stored in a tree-like structure to support memory sharing among cuboids in a logical structure of data cubes. An update mechanism is designed to create or remove cuboids on it, according to the analysis of the user queries, with the consideration of memory size limitation. Data structure is dynamically optimized according to different user queries. During a query process, user queries are recorded to predict the performance increment of the new cuboid. The creation or deletion of a cuboid is determined by performance increment. Experiment results show that our prototype system deliveries good performance towards user queries on different spatiotemporal datasets, which costing small memory size with comparable performance compared with other state-of-the-art algorithms.
Text-to-Viz: Automatic Generation of Infographics from Proportion-Related Natural Language Statements
Authors: Weiwei Cui, Xiaoyu Zhang, Yun Wang 0012, He Huang, Bei Chen, Lei Fang 0004, Haidong Zhang, Jian-Guang Lou, Dongmei Zhang 0001
Published at InfoVis, 2019

Combining data content with visual embellishments, infographics can effectively deliver messages in an engaging and memorable manner. Various authoring tools have been proposed to facilitate the creation of infographics. However, creating a professional infographic with these authoring tools is still not an easy task, requiring much time and design expertise. Therefore, these tools are generally not attractive to casual users, who are either unwilling to take time to learn the tools or lacking in proper design expertise to create a professional infographic. In this paper, we explore an alternative approach: to automatically generate infographics from natural language statements. We first conducted a preliminary study to explore the design space of infographics. Based on the preliminary study, we built a proof-of-concept system that automatically converts statements about simple proportion-related statistics to a set of infographics with pre-designed styles. Finally, we demonstrated the usability and usefulness of the system through sample results, exhibits, and expert reviews.
The Impact of Immersion on Cluster Identification Tasks
Authors: Matthias Kraus, Niklas Weiler, Daniela Oelke, Johannes Kehrer, Daniel A. Keim, Johannes Fuchs 0001
Published at InfoVis, 2019

Recent developments in technology encourage the use of head-mounted displays (HMDs) as a medium to explore visualizations in virtual realities (VRs). VR environments (VREs) enable new, more immersive visualization design spaces compared to traditional computer screens. Previous studies in different domains, such as medicine, psychology, and geology, report a positive effect of immersion, e.g., on learning performance or phobia treatment effectiveness. Our work presented in this paper assesses the applicability of those findings to a common task from the information visualization (InfoVis) domain. We conducted a quantitative user study to investigate the impact of immersion on cluster identification tasks in scatterplot visualizations. The main experiment was carried out with 18 participants in a within-subjects setting using four different visualizations, (1) a 2D scatterplot matrix on a screen, (2) a 3D scatterplot on a screen, (3) a 3D scatterplot miniature in a VRE and (4) a fully immersive 3D scatterplot in a VRE. The four visualization design spaces vary in their level of immersion, as shown in a supplementary study. The results of our main study indicate that task performance differs between the investigated visualization design spaces in terms of accuracy, efficiency, memorability, sense of orientation, and user preference. In particular, the 2D visualization on the screen performed worse compared to the 3D visualizations with regard to the measured variables. The study shows that an increased level of immersion can be a substantial benefit in the context of 3D data and cluster detection.
The Perceptual Proxies of Visual Comparison
Authors: Nicole Jardine, Brian D. Ondov, Niklas Elmqvist, Steven Franconeri
Published at InfoVis, 2019

Perceptual tasks in visualizations often involve comparisons. Of two sets of values depicted in two charts, which set had values that were the highest overall? Which had the widest range? Prior empirical work found that the performance on different visual comparison tasks (e.g., “biggest delta”, “biggest correlation”) varied widely across different combinations of marks and spatial arrangements. In this paper, we expand upon these combinations in an empirical evaluation of two new comparison tasks: the “biggest mean” and “biggest range” between two sets of values. We used a staircase procedure to titrate the difficulty of the data comparison to assess which arrangements produced the most precise comparisons for each task. We find visual comparisons of biggest mean and biggest range are supported by some chart arrangements more than others, and that this pattern is substantially different from the pattern for other tasks. To synthesize these dissonant findings, we argue that we must understand which features of a visualization are actually used by the human visual system to solve a given task. We call these perceptual proxies. For example, when comparing the means of two bar charts, the visual system might use a “Mean length” proxy that isolates the actual lengths of the bars and then constructs a true average across these lengths. Alternatively, it might use a “Hull Area” proxy that perceives an implied hull bounded by the bars of each chart and then compares the areas of these hulls. We propose a series of potential proxies across different tasks, marks, and spatial arrangements. Simple models of these proxies can be empirically evaluated for their explanatory power by matching their performance to human performance across these marks, arrangements, and tasks. We use this process to highlight candidates for perceptual proxies that might scale more broadly to explain performance in visual comparison.
The Role of Latency and Task Complexity in Predicting Visual Search Behavior
Authors: Leilani Battle, R. Jordan Crouser, Audace Nakeshimana, Ananda Montoly, Remco Chang, Michael Stonebraker
Published at InfoVis, 2019

Latency in a visualization system is widely believed to affect user behavior in measurable ways, such as requiring the user to wait for the visualization system to respond, leading to interruption of the analytic flow. While this effect is frequently observed and widely accepted, precisely how latency affects different analysis scenarios is less well understood. In this paper, we examine the role of latency in the context of visual search, an essential task in data foraging and exploration using visualization. We conduct a series of studies on Amazon Mechanical Turk and find that under certain conditions, latency is a statistically significant predictor of visual search behavior, which is consistent with previous studies. However, our results also suggest that task type, task complexity, and other factors can modulate the effect of latency, in some cases rendering latency statistically insignificant in predicting user behavior. This suggests a more nuanced view of the role of latency than previously reported. Building on these results and the findings of prior studies, we propose design guidelines for measuring and interpreting the effects of latency when evaluating performance on visual search tasks.
There Is No Spoon: Evaluating Performance, Space Use, and Presence with Expert Domain Users in Immersive Analytics
Authors: Andrea Batch, Andrew Cunningham, Maxime Cordeil, Niklas Elmqvist, Tim Dwyer, Bruce H. Thomas, Kim Marriott
Published at InfoVis, 2019

Immersive analytics turns the very space surrounding the user into a canvas for data analysis, supporting human cognitive abilities in myriad ways. We present the results of a design study, contextual inquiry, and longitudinal evaluation involving professional economists using a Virtual Reality (VR) system for multidimensional visualization to explore actual economic data. Results from our preregistered evaluation highlight the varied use of space depending on context (exploration vs. presentation), the organization of space to support work, and the impact of immersion on navigation and orientation in the 3D analysis space.