IEEE VIS Publication Dataset

Statistical data associated with geographic regions is nowadays globally available in large amounts and hence automated methods to visually display these data are in high demand. There are several well-established thematic map types for quantitative data on the ratio-scale associated with regions: choropleth maps, cartograms, and proportional symbol maps. However, all these maps suffer from limitations, especially if large data values are associated with small regions. To overcome these limitations, we propose a novel type of quantitative thematic map, the necklace map. In a necklace map, the regions of the underlying two-dimensional map are projected onto intervals on a one-dimensional curve (the necklace) that surrounds the map regions. Symbols are scaled such that their area corresponds to the data of their region and placed without overlap inside the corresponding interval on the necklace. Necklace maps appear clear and uncluttered and allow for comparatively large symbol sizes. They visualize data sets well which are not proportional to region sizes. The linear ordering of the symbols along the necklace facilitates an easy comparison of symbol sizes. One map can contain several nested or disjoint necklaces to visualize clustered data. The advantages of necklace maps come at a price: the association between a symbol and its region is weaker than with other types of maps. Interactivity can help to strengthen this association if necessary. We present an automated approach to generate necklace maps which allows the user to interactively control the final symbol placement. We validate our approach with experiments using various data sets and maps.

The rapid development of Web technology has resulted in an increasing number of hotel customers sharing their opinions on the hotel services. Effective visual analysis of online customer opinions is needed, as it has a significant impact on building a successful business. In this paper, we present OpinionSeer, an interactive visualization system that could visually analyze a large collection of online hotel customer reviews. The system is built on a new visualization-centric opinion mining technique that considers uncertainty for faithfully modeling and analyzing customer opinions. A new visual representation is developed to convey customer opinions by augmenting well-established scatterplots and radial visualization. To provide multiple-level exploration, we introduce subjective logic to handle and organize subjective opinions with degrees of uncertainty. Several case studies illustrate the effectiveness and usefulness of OpinionSeer on analyzing relationships among multiple data dimensions and comparing opinions of different groups. Aside from data on hotel customer feedback, OpinionSeer could also be applied to visually analyze customer opinions on other products or services.

Interactive visualization requires the translation of data into a screen space of limited resolution. While currently ignored by most visualization models, this translation entails a loss of information and the introduction of a number of artifacts that can be useful, (e.g., aggregation, structures) or distracting (e.g., over-plotting, clutter) for the analysis. This phenomenon is observed in parallel coordinates, where overlapping lines between adjacent axes form distinct patterns, representing the relation between variables they connect. However, even for a small number of dimensions, the challenge is to effectively convey the relationships for all combinations of dimensions. The size of the dataset and a large number of dimensions only add to the complexity of this problem. To address these issues, we propose Pargnostics, parallel coordinates diagnostics, a model based on screen-space metrics that quantify the different visual structures. Pargnostics metrics are calculated for pairs of axes and take into account the resolution of the display as well as potential axis inversions. Metrics include the number of line crossings, crossing angles, convergence, overplotting, etc. To construct a visualization view, the user can pick from a ranked display showing pairs of coordinate axes and the structures between them, or examine all possible combinations of axes at once in a matrix display. Picking the best axes layout is an NP-complete problem in general, but we provide a way of automatically optimizing the display according to the user's preferences based on our metrics and model.

Public genealogical databases are becoming increasingly populated with historical data and records of the current population's ancestors. As this increasing amount of available information is used to link individuals to their ancestors, the resulting trees become deeper and more dense, which justifies the need for using organized, space-efficient layouts to display the data. Existing layouts are often only able to show a small subset of the data at a time. As a result, it is easy to become lost when navigating through the data or to lose sight of the overall tree structure. On the contrary, leaving space for unknown ancestors allows one to better understand the tree's structure, but leaving this space becomes expensive and allows fewer generations to be displayed at a time. In this work, we propose that the H-tree based layout be used in genealogical software to display ancestral trees. We will show that this layout presents an increase in the number of displayable generations, provides a nicely arranged, symmetrical, intuitive and organized fractal structure, increases the user's ability to understand and navigate through the data, and accounts for the visualization requirements necessary for displaying such trees. Finally, user-study results indicate potential for user acceptance of the new layout.

Treemaps are space-filling visualizations that make efficient use of limited display space to depict large amounts of hierarchical data. Creating perceptually effective treemaps requires carefully managing a number of design parameters including the aspect ratio and luminance of rectangles. Moreover, treemaps encode values using area, which has been found to be less accurate than judgments of other visual encodings, such as length. We conduct a series of controlled experiments aimed at producing a set of design guidelines for creating effective rectangular treemaps. We find no evidence that luminance affects area judgments, but observe that aspect ratio does have an effect. Specifically, we find that the accuracy of area comparisons suffers when the compared rectangles have extreme aspect ratios or when both are squares. Contrary to common assumptions, the optimal distribution of rectangle aspect ratios within a treemap should include non-squares, but should avoid extremes. We then compare treemaps with hierarchical bar chart displays to identify the data densities at which length-encoded bar charts become less effective than area-encoded treemaps. We report the transition points at which treemaps exhibit judgment accuracy on par with bar charts for both leaf and non-leaf tree nodes. We also find that even at relatively low data densities treemaps result in faster comparisons than bar charts. Based on these results, we present a set of guidelines for the effective use of treemaps and suggest alternate approaches for treemap layout.

This design paper presents new guidance for creating map legends in a dynamic environment. Our contribution is a set ofguidelines for legend design in a visualization context and a series of illustrative themes through which they may be expressed. Theseare demonstrated in an applications context through interactive software prototypes. The guidelines are derived from cartographicliterature and in liaison with EDINA who provide digital mapping services for UK tertiary education. They enhance approaches tolegend design that have evolved for static media with visualization by considering: selection, layout, symbols, position, dynamismand design and process. Broad visualization legend themes include: The Ground Truth Legend, The Legend as Statistical Graphicand The Map is the Legend. Together, these concepts enable us to augment legends with dynamic properties that address specificneeds, rethink their nature and role and contribute to a wider re-evaluation of maps as artifacts of usage rather than statements offact. EDINA has acquired funding to enhance their clients with visualization legends that use these concepts as a consequence ofthis work. The guidance applies to the design of a wide range of legends and keys used in cartography and information visualization.

Electronic test and measurement systems are becoming increasingly sophisticated in order to match the increased complexity and ultra-high speed of the devices under test. A key feature in many such instruments is a vastly increased capacity for storage of digital signals. Storage of 109 time points or more is now possible. At the same time, the typical screens on such measurement devices are relatively small. Therefore, these instruments can only render an extremely small fraction of the complete signal at any time. SignalLens uses a Focus+Context approach to provide a means of navigating to and inspecting low-level signal details in the context of the entire signal trace. This approach provides a compact visualization suitable for embedding into the small displays typically provided by electronic measurement instruments. We further augment this display with computed tracks which display time-aligned computed properties of the signal. By combining and filtering these computed tracks it is possible to easily and quickly find computationally detected features in the data which are often obscured by the visual compression required to render the large data sets on a small screen. Further, these tracks can be viewed in the context of the entire signal trace as well as visible high-level signal features. Several examples using real-world electronic measurement data are presented, which demonstrate typical use cases and the effectiveness of the design.

Tag clouds have proliferated over the web over the last decade. They provide a visual summary of a collection of texts by visually depicting the tag frequency by font size. In use, tag clouds can evolve as the associated data source changes over time. Interesting discussions around tag clouds often include a series of tag clouds and consider how they evolve over time. However, since tag clouds do not explicitly represent trends or support comparisons, the cognitive demands placed on the person for perceiving trends in multiple tag clouds are high. In this paper, we introduce SparkClouds, which integrate sparklines into a tag cloud to convey trends between multiple tag clouds. We present results from a controlled study that compares SparkClouds with two traditional trend visualizations-multiple line graphs and stacked bar charts-as well as Parallel Tag Clouds. Results show that SparkClouds' ability to show trends compares favourably to the alternative visualizations.

An ongoing challenge for information visualization is how to deal with over-plotting forced by ties or the relatively limited visual field of display devices. A popular solution is to represent local data density with area (bubble plots, treemaps), color(heatmaps), or aggregation (histograms, kernel densities, pixel displays). All of these methods have at least one of three deficiencies:1) magnitude judgments are biased because area and color have convex downward perceptual functions, 2) area, hue, and brightnesshave relatively restricted ranges of perceptual intensity compared to length representations, and/or 3) it is difficult to brush or link toindividual cases when viewing aggregations. In this paper, we introduce a new technique for visualizing and interacting with datasets that preserves density information by stacking overlapping cases. The overlapping data can be points or lines or other geometric elements, depending on the type of plot. We show real-dataset applications of this stacking paradigm and compare them to other techniques that deal with over-plotting in high-dimensional displays.

A standard approach for visualizing multivariate networks is to use one or more multidimensional views (for example, scatterplots) for selecting nodes by various metrics, possibly coordinated with a node-link view of the network. In this paper, we present three novel approaches for achieving a tighter integration of these views through hybrid techniques for multidimensional visualization, graph selection and layout. First, we present the FlowVizMenu, a radial menu containing a scatterplot that can be popped up transiently and manipulated with rapid, fluid gestures to select and modify the axes of its scatterplot. Second, the FlowVizMenu can be used to steer an attribute-driven layout of the network, causing certain nodes of a node-link diagram to move toward their corresponding positions in a scatterplot while others can be positioned manually or by force-directed layout. Third, we describe a novel hybrid approach that combines a scatterplot matrix (SPLOM) and parallel coordinates called the Parallel Scatterplot Matrix (P-SPLOM), which can be used to visualize and select features within the network. We also describe a novel arrangement of scatterplots called the Scatterplot Staircase (SPLOS) that requires less space than a traditional scatterplot matrix. Initial user feedback is reported.

The choices we take when listening to music are expressions of our personal taste and character. Storing and accessing our listening histories is trivial due to services like Last.fm, but learning from them and understanding them is not. Existing solutions operate at a very abstract level and only produce statistics. By applying techniques from information visualization to this problem, we were able to provide average people with a detailed and powerful tool for accessing their own musical past. LastHistory is an interactive visualization for displaying music listening histories, along with contextual information from personal photos and calendar entries. Its two main user tasks are (1) analysis, with an emphasis on temporal patterns and hypotheses related to musical genre and sequences, and (2) reminiscing, where listening histories and context represent part of one's past. In this design study paper we give an overview of the field of music listening histories and explain their unique characteristics as a type of personal data. We then describe the design rationale, data and view transformations of LastHistory and present the results from both a laband a large-scale online study. We also put listening histories in contrast to other lifelogging data. The resonant and enthusiastic feedback that we received from average users shows a need for making their personal data accessible. We hope to stimulate such developments through this research.

Radial visualizations play an important role in the information visualization community. But the decision to choose a radial coordinate system is rather based on intuition than on scientific foundations. The empirical approach presented in this paper aims at uncovering strengths and weaknesses of radial visualizations by comparing them to equivalent ones in Cartesian coordinate systems. We identified memorizing positions of visual elements as a generic task when working with visualizations. A first study with 674 participants provides a broad data spectrum for exploring differences between the two visualization types. A second, complementing study with fewer participants focuses on further questions raised by the first study. Our findings document that Cartesian visualizations tend to outperform their radial counterparts especially with respect to answer times. Nonetheless, radial visualization seem to be more appropriate for focusing on a particular data dimension.

In many common data analysis scenarios the data elements are logically grouped into sets. Venn and Euler style diagrams are a common visual representation of such set membership where the data elements are represented by labels or glyphs and sets are indicated by boundaries surrounding their members. Generating such diagrams automatically such that set regions do not intersect unless the corresponding sets have a non-empty intersection is a difficult problem. Further, it may be impossible in some cases if regions are required to be continuous and convex. Several approaches exist to draw such set regions using more complex shapes, however, the resulting diagrams can be difficult to interpret. In this paper we present two novel approaches for simplifying a complex collection of intersecting sets into a strict hierarchy that can be more easily automatically arranged and drawn (Figure 1). In the first approach, we use compact rectangular shapes for drawing each set, attempting to improve the readability of the set intersections. In the second approach, we avoid drawing intersecting set regions by duplicating elements belonging to multiple sets. We compared both of our techniques to the traditional non-convex region technique using five readability tasks. Our results show that the compact rectangular shapes technique was often preferred by experimental subjects even though the use of duplications dramatically improves the accuracy and performance time for most of our tasks. In addition to general set representation our techniques are also applicable to visualization of networks with intersecting clusters of nodes.

Understanding the diversity of a set of multivariate objects is an important problem in many domains, including ecology, college admissions, investing, machine learning, and others. However, to date, very little work has been done to help users achieve this kind of understanding. Visual representation is especially appealing for this task because it offers the potential to allow users to efficiently observe the objects of interest in a direct and holistic way. Thus, in this paper, we attempt to formalize the problem of visualizing the diversity of a large (more than 1000 objects), multivariate (more than 5 attributes) data set as one worth deeper investigation by the information visualization community. In doing so, we contribute a precise definition of diversity, a set of requirements for diversity visualizations based on this definition, and a formal user study design intended to evaluate the capacity of a visual representation for communicating diversity information. Our primary contribution, however, is a visual representation, called the Diversity Map, for visualizing diversity. An evaluation of the Diversity Map using our study design shows that users can judge elements of diversity consistently and as or more accurately than when using the only other representation specifically designed to visualize diversity.

Graphs are a versatile structure and abstraction for binary relationships between objects. To gain insight into such relationships, their corresponding graph can be visualized. In the past, many classes of graphs have been defined, e.g. trees, planar graphs, directed acyclic graphs, and visualization algorithms were proposed for these classes. Although many graphs may only be classified as "general" graphs, they can contain substructures that belong to a certain class. Archambault proposed the TopoLayout framework: rather than draw any arbitrary graph using one method, split the graph into components that are homogeneous with respect to one graph class and then draw each component with an algorithm best suited for this class. Graph products constitute a class that arises frequently in graph theory, but for which no visualization algorithm has been proposed until now. In this paper, we present an algorithm for drawing graph products and the aesthetic criterion graph product's drawings are subject to. We show that the popular High-Dimensional Embedder approach applied to cartesian products already respects this aestetic criterion, but has disadvantages. We also present how our method is integrated as a new component into the TopoLayout framework. Our implementation is used for further research of graph products in a biological context.

In order to use new visualizations, most toolkits require application developers to rebuild their applications and distribute new versions to users. The WebCharts Framework take a different approach by hosting Javascript from within an application and providing a standard data and events interchange.. In this way, applications can be extended dynamically, with a wide variety of visualizations. We discuss the benefits of this architectural approach, contrast it to existing techniques, and give a variety of examples and extensions of the basic system.

This paper highlights the important role that record-keeping (i.e. taking notes and saving charts) plays in collaborative data analysis within the business domain. The discussion of record-keeping is based on observations from a user study in which co-located teams worked on collaborative visual analytics tasks using large interactive wall and tabletop displays. Part of our findings is a collaborative data analysis framework that encompasses note taking as one of the main activities. We observed that record-keeping was a critical activity within the analysis process. Based on our observations, we characterize notes according to their content, scope, and usage, and describe how they fit into a process of collaborative data analysis. We then discuss suggestions for the design of collaborative visual analytics tools.

Since its inception, the field of visual analytics has undergone tremendous growth in understanding how to create interactive visual tools to solve analytical problems. However, with few exceptions, most of these tools have been designed for single users in desktop environments. While often effective on their own, most single-user systems do not reflect the collaborative nature of solving real-world analytical tasks. Many intelligence analysts, for example, have been observed to switch repeatedly between working alone and collaborating with members of a small team. In this paper, we propose that a complete visual analytical system designed for solving real-world tasks ought to have two integrated components: a single-user desktop system and a mirroring system suitable for a collaborative environment.

The visual analysis of video content is an important research topic due to the huge amount of video data that is generated every day. Annotating this data will become a major problem since the amount of videos further increases. With this work we introduce a system that combines a visualization tool with automatic video segmentation techniques and a characteristic key-frame extraction. A summary of the content of a whole video in one view is realized. Furthermore, the user can interactively browse through the video via our visualization interface to get more detailed information. The system is adapted to two application scenarios and a third application is discussed for future work.

Predicting protein structures has long been a grand-challenge problem. Fine-grained computational simulation of folding events from a protein's synthesis to its final stable structure remains computationally intractable. Therefore, methods which derive constraints from other sources are attractive. To date, constraints derived from known structures have proven to be highly successful. However, these cannot be applied to molecules with no identifiable neighbors having already-determined structures. For such molecules, structural constraints must be derived in other ways. One popular approach has been the statistical analysis of large families of proteins, with the hope that residues that ÔÇ£change togetherÔÇØ (co-evolve) imply that those residues are in contact. Unfortunately, despite repeated attempts to use this data to deduce structural constraints, this approach has met with minimal success. The consensus of current literature concludes that there is simply too little information contained within the correlated mutations of many protein families to reliably and generally predict structural constraints. Recent work in my laboratory challenges this conclusion. For some time we have been developing methods (MAVL/StickWRLD) to visualize the pattern of co-evolved mutations within sequence families. While our analysis of individual correlations agrees with the literature consensus, we have recently discovered that the visualized pattern of correlations is highly suggestive of structural relationships. In our preliminary test cases, human researchers can unambiguously determine many positive structural constraints by visual analysis of statistical sequence information alone, often with no training on interpretation of the visualization results. Herein we report the visualization design that supports this Visual Analytics approach to identifying high-confidence hypotheses about protein folding from protein sequence, and illustrate preliminary results from th- - is research. Our approach entails a higher-dimensional extension of parallel coordinates which illuminates distant shared sub-tuples of the vectors representing each protein sequence when these sub-tuples occur with an over abundance compared to expectations. It simultaneously eliminates all representations of tuples which occur with frequency near the expected norm. The result is a minimally-occluded representation of outlier, and only outlier co-occurrences within the sequence families.