IEEE VIS Publication Dataset

In Human-Computer Interaction (HCI), experts seek to evaluate and compare the performance and ergonomics of user interfaces. Recently, a novel cost-efficient method for estimating physical ergonomics and performance has been introduced to HCI. It is based on optical motion capture and biomechanical simulation. It provides a rich source for analyzing human movements summarized in a multidimensional data set. Existing visualization tools do not sufficiently support the HCI experts in analyzing this data. We identified two shortcomings. First, appropriate visual encodings are missing particularly for the biomechanical aspects of the data. Second, the physical setup of the user interface cannot be incorporated explicitly into existing tools. We present MovExp, a versatile visualization tool that supports the evaluation of user interfaces. In particular, it can be easily adapted by the HCI experts to include the physical setup that is being evaluated, and visualize the data on top of it. Furthermore, it provides a variety of visual encodings to communicate muscular loads, movement directions, and other specifics of HCI studies that employ motion capture and biomechanical simulation. In this design study, we follow a problem-driven research approach. Based on a formalization of the visualization needs and the data structure, we formulate technical requirements for the visualization tool and present novel solutions to the analysis needs of the HCI experts. We show the utility of our tool with four case studies from the daily work of our HCI experts.

We reflect on a four-year engagement with transport authorities and others involving a large dataset describing the use of a public bicycle-sharing scheme. We describe the role visualization of these data played in fostering engagement with policy makers, transport operators, the transport research community, the museum and gallery sector and the general public. We identify each of these as `channels'-evolving relationships between producers and consumers of visualization-where traditional roles of the visualization expert and domain expert are blurred. In each case, we identify the different design decisions that were required to support each of these channels and the role played by the visualization process. Using chauffeured interaction with a flexible visual analytics system we demonstrate how insight was gained by policy makers into gendered spatio-temporal cycle behaviors, how this led to further insight into workplace commuting activity, group cycling behavior and explanations for street navigation choice. We demonstrate how this supported, and was supported by, the seemingly unrelated development of narrative-driven visualization via TEDx, of the creation and the setting of an art installation and the curating of digital and physical artefacts. We assert that existing models of visualization design, of tool/technique development and of insight generation do not adequately capture the richness of parallel engagement via these multiple channels of communication. We argue that developing multiple channels in parallel opens up opportunities for visualization design and analysis by building trust and authority and supporting creativity. This rich, non-sequential approach to visualization design is likely to foster serendipity, deepen insight and increase impact.

Network data is ubiquitous; e-mail traffic between persons, telecommunication, transport and financial networks are some examples. Often these networks are large and multivariate, besides the topological structure of the network, multivariate data on the nodes and links is available. Currently, exploration and analysis methods are focused on a single aspect; the network topology or the multivariate data. In addition, tools and techniques are highly domain specific and require expert knowledge. We focus on the non-expert user and propose a novel solution for multivariate network exploration and analysis that tightly couples structural and multivariate analysis. In short, we go from Detail to Overview via Selections and Aggregations (DOSA): users are enabled to gain insights through the creation of selections of interest (manually or automatically), and producing high-level, infographic-style overviews simultaneously. Finally, we present example explorations on real-world datasets that demonstrate the effectiveness of our method for the exploration and understanding of multivariate networks where presentation of findings comes for free.

We present NeuroLines, a novel visualization technique designed for scalable detailed analysis of neuronal connectivity at the nanoscale level. The topology of 3D brain tissue data is abstracted into a multi-scale, relative distance-preserving subway map visualization that allows domain scientists to conduct an interactive analysis of neurons and their connectivity. Nanoscale connectomics aims at reverse-engineering the wiring of the brain. Reconstructing and analyzing the detailed connectivity of neurons and neurites (axons, dendrites) will be crucial for understanding the brain and its development and diseases. However, the enormous scale and complexity of nanoscale neuronal connectivity pose big challenges to existing visualization techniques in terms of scalability. NeuroLines offers a scalable visualization framework that can interactively render thousands of neurites, and that supports the detailed analysis of neuronal structures and their connectivity. We describe and analyze the design of NeuroLines based on two real-world use-cases of our collaborators in developmental neuroscience, and investigate its scalability to large-scale neuronal connectivity data.

Space-filling techniques seek to use as much as possible the visual space to represent a dataset, splitting it into regions that represent the data elements. Amongst those techniques, Treemaps have received wide attention due to its simplicity, reduced visual complexity, and compact use of the available space. Several different Treemap algorithms have been proposed, however the core idea is the same, to divide the visual space into rectangles with areas proportional to some data attribute or weight. Although pleasant layouts can be effectively produced by the existing techniques, most of them do not take into account relationships that might exist between different data elements when partitioning the visual space. This violates the distance-similarity metaphor, that is, close rectangles do not necessarily represent similar data elements. In this paper, we propose a novel approach, called Neighborhood Treemap (Nmap), that seeks to solve this limitation by employing a slice and scale strategy where the visual space is successively bisected on the horizontal or vertical directions and the bisections are scaled until one rectangle is defined per data element. Compared to the current techniques with the same similarity preservation goal, our approach presents the best results while being two to three orders of magnitude faster. The usefulness of Nmap is shown by two applications involving the organization of document collections and the construction of cartograms illustrating its effectiveness on different scenarios.

Effectively showing the relationships between objects in a dataset is one of the main tasks in information visualization. Typically there is a well-defined notion of distance between pairs of objects, and traditional approaches such as principal component analysis or multi-dimensional scaling are used to place the objects as points in 2D space, so that similar objects are close to each other. In another typical setting, the dataset is visualized as a network graph, where related nodes are connected by links. More recently, datasets are also visualized as maps, where in addition to nodes and links, there is an explicit representation of groups and clusters. We consider these three Techniques, characterized by a progressive increase of the amount of encoded information: node diagrams, node-link diagrams and node-link-group diagrams. We assess these three types of diagrams with a controlled experiment that covers nine different tasks falling broadly in three categories: node-based tasks, network-based tasks and group-based tasks. Our findings indicate that adding links, or links and group representations, does not negatively impact performance (time and accuracy) of node-based tasks. Similarly, adding group representations does not negatively impact the performance of network-based tasks. Node-link-group diagrams outperform the others on group-based tasks. These conclusions contradict results in other studies, in similar but subtly different settings. Taken together, however, such results can have significant implications for the design of standard and domain snecific visualizations tools.

Visualizing sets to reveal relationships between constituent elements is a complex representational problem. Recent research presents several automated placement and grouping techniques to highlight connections between set elements. However, these techniques do not scale well for sets with cardinality greater than one hundred elements. We present OnSet, an interactive, scalable visualization technique for representing large-scale binary set data. The visualization technique defines a single, combined domain of elements for all sets, and models each set by the elements that it both contains and does not contain. OnSet employs direct manipulation interaction and visual highlighting to support easy identification of commonalities and differences as well as membership patterns across different sets of elements. We present case studies to illustrate how the technique can be successfully applied across different domains such as bio-chemical metabolomics and task and event scheduling.

In this paper we introduce Order of Magnitude Markers (OOMMs) as a new technique for number representation. The motivation for this work is that many data sets require the depiction and comparison of numbers that have varying orders of magnitude. Existing techniques for representation use bar charts, plots and colour on linear or logarithmic scales. These all suffer from related problems. There is a limit to the dynamic range available for plotting numbers, and so the required dynamic range of the plot can exceed that of the depiction method. When that occurs, resolving, comparing and relating values across the display becomes problematical or even impossible for the user. With this in mind, we present an empirical study in which we compare logarithmic, linear, scale-stack bars and our new markers for 11 different stimuli grouped into 4 different tasks across all 8 marker types.

This paper presents a new approach to flow mapping that extracts inherent patterns from massive geographic mobility data and constructs effective visual representations of the data for the understanding of complex flow trends. This approach involves a new method for origin-destination flow density estimation and a new method for flow map generalization, which together can remove spurious data variance, normalize flows with control population, and detect high-level patterns that are not discernable with existing approaches. The approach achieves three main objectives in addressing the challenges for analyzing and mapping massive flow data. First, it removes the effect of size differences among spatial units via kernel-based density estimation, which produces a measurement of flow volume between each pair of origin and destination. Second, it extracts major flow patterns in massive flow data through a new flow sampling method, which filters out duplicate information in the smoothed flows. Third, it enables effective flow mapping and allows intuitive perception of flow patterns among origins and destinations without bundling or altering flow paths. The approach can work with both point-based flow data (such as taxi trips with GPS locations) and area-based flow data (such as county-to-county migration). Moreover, the approach can be used to detect and compare flow patterns at different scales or in relatively sparse flow datasets, such as migration for each age group. We evaluate and demonstrate the new approach with case studies of U.S. migration data and experiments with synthetic data.

For an investigative journalist, a large collection of documents obtained from a Freedom of Information Act request or a leak is both a blessing and a curse: such material may contain multiple newsworthy stories, but it can be difficult and time consuming to find relevant documents. Standard text search is useful, but even if the search target is known it may not be possible to formulate an effective query. In addition, summarization is an important non-search task. We present Overview, an application for the systematic analysis of large document collections based on document clustering, visualization, and tagging. This work contributes to the small set of design studies which evaluate a visualization system ÔÇ£in the wildÔÇØ, and we report on six case studies where Overview was voluntarily used by self-initiated journalists to produce published stories. We find that the frequently-used language of ÔÇ£exploringÔÇØ a document collection is both too vague and too narrow to capture how journalists actually used our application. Our iterative process, including multiple rounds of deployment and observations of real world usage, led to a much more specific characterization of tasks. We analyze and justify the visual encoding and interaction techniques used in Overview's design with respect to our final task abstractions, and propose generalizable lessons for visualization design methodology.

Interactively exploring multidimensional datasets requires frequent switching among a range of distinct but inter-related tasks (e.g., producing different visuals based on different column sets, calculating new variables, and observing the interactions between sets of data). Existing approaches either target specific different problem domains (e.g., data-transformation or data-presentation) or expose only limited aspects of the general exploratory process; in either case, users are forced to adopt coping strategies (e.g., arranging windows or using undo as a mechanism for comparison instead of using side-by-side displays) to compensate for the lack of an integrated suite of exploratory tools. PanoramicData (PD) addresses these problems by unifying a comprehensive set of tools for visual data exploration into a hybrid pen and touch system designed to exploit the visualization advantages of large interactive displays. PD goes beyond just familiar visualizations by including direct UI support for data transformation and aggregation, filtering and brushing. Leveraging an unbounded whiteboard metaphor, users can combine these tools like building blocks to create detailed interactive visual display networks in which each visualization can act as a filter for others. Further, by operating directly on relational-databases, PD provides an approachable visual language that exposes a broad set of the expressive power of SQL including functionally complete logic filtering, computation of aggregates and natural table joins. To understand the implications of this novel approach, we conducted a formative user study with both data and visualization experts. The results indicated that the system provided a fluid and natural user experience for probing multi-dimensional data and was able to cover the full range of queries that the users wanted to pose.

Despite years of research yielding systems and guidelines to aid visualization design, practitioners still face the challenge of identifying the best visualization for a given dataset and task. One promising approach to circumvent this problem is to leverage perceptual laws to quantitatively evaluate the effectiveness of a visualization design. Following previously established methodologies, we conduct a large scale (n = 1687) crowdsourced experiment to investigate whether the perception of correlation in nine commonly used visualizations can be modeled using Weber's law. The results of this experiment contribute to our understanding of information visualization by establishing that: (1) for all tested visualizations, the precision of correlation judgment could be modeled by Weber's law, (2) correlation judgment precision showed striking variation between negatively and positively correlated data, and (3) Weber models provide a concise means to quantify, compare, and rank the perceptual precision afforded by a visualization.

In his book Multimedia Learning [7], Richard Mayer asserts that viewers learn best from imagery that provides them with cues to help them organize new information into the correct knowledge structures. Designers have long been exploiting the Gestalt laws of visual grouping to deliver viewers those cues using visual hierarchy, often communicating structures much more complex than the simple organizations studied in psychological research. Unfortunately, designers are largely practical in their work, and have not paused to build a complex theory of structural communication. If we are to build a tool to help novices create effective and well structured visuals, we need a better understanding of how to create them. Our work takes a first step toward addressing this lack, studying how five of the many grouping cues (proximity, color similarity, common region, connectivity, and alignment) can be effectively combined to communicate structured text and imagery from real world examples. To measure the effectiveness of this structural communication, we applied a digital version of card sorting, a method widely used in anthropology and cognitive science to extract cognitive structures. We then used tree edit distance to measure the difference between perceived and communicated structures. Our most significant findings are: 1) with careful design, complex structure can be communicated clearly; 2) communicating complex structure is best done with multiple reinforcing grouping cues; 3) common region (use of containers such as boxes) is particularly effective at communicating structure; and 4) alignment is a weak structural communicator.

We present Bertifier, a web app for rapidly creating tabular visualizations from spreadsheets. Bertifier draws from Jacques Bertin's matrix analysis method, whose goal was to ÔÇ£simplify without destroyingÔÇØ by encoding cell values visually and grouping similar rows and columns. Although there were several attempts to bring this method to computers, no implementation exists today that is both exhaustive and accessible to a large audience. Bertifier remains faithful to Bertin's method while leveraging the power of today's interactive computers. Tables are formatted and manipulated through crossets, a new interaction technique for rapidly applying operations on rows and columns. We also introduce visual reordering, a semi-interactive reordering approach that lets users apply and tune automatic reordering algorithms in a WYSIWYG manner. Sessions with eight users from different backgrounds suggest that Bertifier has the potential to bring Bertin's method to a wider audience of both technical and non-technical users, and empower them with data analysis and communication tools that were so far only accessible to a handful of specialists.COMPUTER

We address some of the challenges in representing spatial data with a novel form of geometric abstraction-the stenomap. The stenomap comprises a series of smoothly curving linear glyphs that each represent both the boundary and the area of a polygon. We present an efficient algorithm to automatically generate these open, C1-continuous splines from a set of input polygons. Feature points of the input polygons are detected using the medial axis to maintain important shape properties. We use dynamic programming to compute a planar non-intersecting spline representing each polygon's base shape. The results are stylised glyphs whose appearance may be parameterised and that offer new possibilities in the 'cartographic design space'. We compare our glyphs with existing forms of geometric schematisation and discuss their relative merits and shortcomings. We describe several use cases including the depiction of uncertain model data in the form of hurricane track forecasting; minimal ink thematic mapping; and the depiction of continuous statistical data.

Existing research efforts into tennis visualization have primarily focused on using ball and player tracking data to enhance professional tennis broadcasts and to aid coaches in helping their students. Gathering and analyzing this data typically requires the use of an array of synchronized cameras, which are expensive for non-professional tennis matches. In this paper, we propose TenniVis, a novel tennis match visualization system that relies entirely on data that can be easily collected, such as score, point outcomes, point lengths, service information, and match videos that can be captured by one consumer-level camera. It provides two new visualizations to allow tennis coaches and players to quickly gain insights into match performance. It also provides rich interactions to support ad hoc hypothesis development and testing. We first demonstrate the usefulness of the system by analyzing the 2007 Australian Open men's singles final. We then validate its usability by two pilot user studies where two college tennis coaches analyzed the matches of their own players. The results indicate that useful insights can quickly be discovered and ad hoc hypotheses based on these insights can conveniently be tested through linked match videos.

To support effective exploration, it is often stated that interactive visualizations should provide rapid response times. However, the effects of interactive latency on the process and outcomes of exploratory visual analysis have not been systematically studied. We present an experiment measuring user behavior and knowledge discovery with interactive visualizations under varying latency conditions. We observe that an additional delay of 500ms incurs significant costs, decreasing user activity and data set coverage. Analyzing verbal data from think-aloud protocols, we find that increased latency reduces the rate at which users make observations, draw generalizations and generate hypotheses. Moreover, we note interaction effects in which initial exposure to higher latencies leads to subsequently reduced performance in a low-latency setting. Overall, increased latency causes users to shift exploration strategy, in turn affecting performance. We discuss how these results can inform the design of interactive analysis tools.

We conducted three experiments to investigate the effects of contours on the detection of data similarity with star glyph variations. A star glyph is a small, compact, data graphic that represents a multi-dimensional data point. Star glyphs are often used in small-multiple settings, to represent data points in tables, on maps, or as overlays on other types of data graphics. In these settings, an important task is the visual comparison of the data points encoded in the star glyph, for example to find other similar data points or outliers. We hypothesized that for data comparisons, the overall shape of a star glyph-enhanced through contour lines-would aid the viewer in making accurate similarity judgments. To test this hypothesis, we conducted three experiments. In our first experiment, we explored how the use of contours influenced how visualization experts and trained novices chose glyphs with similar data values. Our results showed that glyphs without contours make the detection of data similarity easier. Given these results, we conducted a second study to understand intuitive notions of similarity. Star glyphs without contours most intuitively supported the detection of data similarity. In a third experiment, we tested the effect of star glyph reference structures (i.e., tickmarks and gridlines) on the detection of similarity. Surprisingly, our results show that adding reference structures does improve the correctness of similarity judgments for star glyphs with contours, but not for the standard star glyph. As a result of these experiments, we conclude that the simple star glyph without contours performs best under several criteria, reinforcing its practice and popularity in the literature. Contours seem to enhance the detection of other types of similarity, e. g., shape similarity and are distracting when data similarity has to be judged. Based on these findings we provide design considerations regarding the use of contours and reference structures on star glyp- s.

Interactive visual applications often rely on animation to transition from one display state to another. There are multiple animation techniques to choose from, and it is not always clear which should produce the best visual correspondences between display elements. One major factor is whether the animation relies on staggering-an incremental delay in start times across the moving elements. It has been suggested that staggering may reduce occlusion, while also reducing display complexity and producing less overwhelming animations, though no empirical evidence has demonstrated these advantages. Work in perceptual psychology does show that reducing occlusion, and reducing inter-object proximity (crowding) more generally, improves performance in multiple object tracking. We ran simulations confirming that staggering can in some cases reduce crowding in animated transitions involving dot clouds (as found in, e.g., animated 2D scatterplots). We empirically evaluated the effect of two staggering techniques on tracking tasks, focusing on cases that should most favour staggering. We found that introducing staggering has a negligible, or even negative, impact on multiple object tracking performance. The potential benefits of staggering may be outweighed by strong costs: a loss of common-motion grouping information about which objects travel in similar paths, and less predictability about when any specific object would begin to move. Staggering may be beneficial in some conditions, but they have yet to be demonstrated. The present results are a significant step toward a better understanding of animation pacing, and provide direction for further research.

Data visualization has been used extensively to inform users. However, little research has been done to examine the effects of data visualization in influencing users or in making a message more persuasive. In this study, we present experimental research to fill this gap and present an evidence-based analysis of persuasive visualization. We built on persuasion research from psychology and user interfaces literature in order to explore the persuasive effects of visualization. In this experimental study we define the circumstances under which data visualization can make a message more persuasive, propose hypotheses, and perform quantitative and qualitative analyses on studies conducted to test these hypotheses. We compare visual treatments with data presented through barcharts and linecharts on the one hand, treatments with data presented through tables on the other, and then evaluate their persuasiveness. The findings represent a first step in exploring the effectiveness of persuasive visualization.