Information Visualization

Weather maps commonly display several variables at once, usually a subset of the following: atmospheric pressure, surface wind speed and direction, surface temperature, cloud cover, and precipitation. Most often, a single variable is mapped separately and occasionally two are shown together. But sometimes there is an attempt to show three or four variables with a result that is difficult to interpret because of visual interference between the graphical elements. As a design exercise, we set the goal of finding out if it is possible to show three variables (two 2D scalar fields and one 2D vector field) simultaneously so that values can be accurately read using keys for all variables, a reasonable level of detail is shown, and important meteorological features stand out clearly. Our solution involves employing three perceptual “channels”: a color channel, a texture channel, and a motion channel in order to perceptually separate the variables and make them independently readable. We describe a set of interactive weather displays, which enable users to view two meteorological scalar fields of various kinds and a field showing wind patterns. To evaluate the method, we implemented three alternative representations each simultaneously showing temperature, atmospheric pressure, wind speed, and direction. Both animated and static variants of our new design were compared to a conventional solution and a glyph-based solution. The evaluation tested the abilities of participants both to read values using a key and to see meteorological patterns in the data. Our new scheme was superior, especially in the representation of wind patterns using the motion channel. It also performed well enough in the representation of pressure using the texture channel to suggest it as a viable design alternative.

Automatic viewpoint selection algorithms try to optimize the view of a data set to best show its features. They are often based on information theoretic frameworks. Although many algorithms have shown useful results, they often take several seconds to produce a result because they render the scene from a variety of viewpoints and analyze the result. In this article, we propose a new algorithm for volume data sets that dramatically reduces the running time. Our entire algorithm takes less than a second, which allows it to be integrated into real-time volume-rendering applications. The interactive performance is achieved by solving a maximization problem with a small sample of the data set, instead of rendering it from a variety of directions. We compare performance results of our algorithm to state-of-the-art approaches and show that our algorithm achieves comparable results for the resulting viewpoints. Furthermore, we apply our algorithm to multichannel volume data sets.

Self-animating image of flow through repeated asymmetric patterns (RAPs) is an innovative approach for creating illusory motion using a single image. In this paper, we present a smooth cyclic variable-speed RAP animation model that emulates orthogonal advancing waves from a geometry-based flow representation. It enables dense, accurate visualization of complex real-world flows using animated streamlines of an elegant placement coupled with visually appealing orthogonal advancing waves. The animation model first performs velocity (magnitude) integral luminance transition on individual streamlines. Then, inter-streamline synchronization in luminance varying along the tangential direction is imposed. Next, tangential flow streaks are constructed using evenly spaced hue differing in the orthogonal direction. In addition, an energy-decreasing strategy is proposed that adopts an iterative yet efficient procedure for determining the luminance phase and hue of each streamline in HSL (hue, saturation, and lightness or brightness) color space. To increase the contrast between flow streaks, adaptive luminance interleaving in the direction perpendicular to the flow is further applied. We demonstrate the effectiveness of the animation model using some synthetic and real flows. Color figures, images, and accompanying animations are available at http://graphics.csie.ncku.edu.tw/flowvis.

Large manufacturing companies frequently receive thousands of web surveys every day. People share their thoughts regarding a wide range of products, their features, and the service they received. In addition, more than 190 million tweets (small text Web posts) are generated daily. Both survey feedback and tweets are underutilized as a source for understanding customer sentiments. To explore high-volume customer feedback streams, in this article, we introduce four time series visual analysis techniques: (1) feature-based sentiment analysis that extracts, measures, and maps customer feedback; (2) a novel way of determining term associations that identify attributes, verbs, and adjectives frequently occurring together; (3) a self-organizing term association map and a pixel cell–based sentiment calendar to identify co-occurring and influential opinion; and (4) a new geo-based term association technique providing a key term geo map to enable the user to inspect the statistical significance and the sentiment distribution of individual key terms. We have used and evaluated these techniques and combined them into a well-fitted solution for an effective analysis of large customer feedback streams such as web surveys (from product buyers) and Twitter (e.g. from Kung-Fu Panda movie reviewers).

Ontologies and hierarchical clustering are both important tools in biology and medicine to study high-throughput data such as transcriptomics and metabolomics data. Enrichment of ontology terms in the data is used to identify statistically overrepresented ontology terms, giving insight into relevant biological processes or functional modules. Hierarchical clustering is a standard method to analyze and visualize data to find relatively homogeneous clusters of experimental data points. Both methods support the analysis of the same data set but are usually considered independently. However, often a combined view is desired: visualizing a large data set in the context of an ontology under consideration of a clustering of the data. This article proposes new visualization methods for this task. They allow for interactive selection and navigation to explore the data under consideration as well as visual analysis of mappings between ontology- and cluster-based space-filling representations. In this context, we discuss our approach together with specific properties of the biological input data and identify features that make our approach easily usable for domain experts.

Online news sources produce thousands of news articles every day, reporting on local and global real-world events. New information quickly replaces the old, making it difficult for readers to put current events in the context of the past. The stories about these events have complex relationships and characteristics that are difficult to model: they can be weakly or strongly related or they can merge or split over time. In this article, we present a visual analytics system for temporal analysis of news stories in dynamic information streams, which combines interactive visualization and text mining techniques to facilitate the analysis of similar topics that split and merge over time. Text clustering algorithms extract stories from online news streams in consecutive time windows and identify similar stories from the past. The stories are displayed in a visualization, which (1) sorts the stories by minimizing clutter and overlap from edge crossings, (2) shows their temporal characteristics in different time frames with different levels of detail, and (3) allows incremental updates of the display without recalculating the past data. Stories can be interactively filtered by their duration and connectivity in order to be explored in full detail. To demonstrate the system’s capabilities for detailed dynamic text stream exploration, we present a use case with real news data about the Arabic Uprising in 2011.

Many algorithms for graph layout have been devised over the last 30 years spanning both the graph drawing and information visualisation communities. This article first reviews the advances made in the field of graph drawing that have then often been applied by the information visualisation community. There then follows a discussion of a range of techniques developed specifically for graph visualisations. Graph drawing algorithms are categorised into the following approaches: force-directed layouts, the use of dimension reduction in graph layout and computational improvements including multi-level techniques. Methods developed specifically for graph visualisation often make use of node-attributes and are categorised based on whether the attributes are used to introduce constraints to the layout, provide a clustered view or define an explicit representation in two-dimensional space. The similarities and distinctions between these techniques are examined and the aim is to provide a detailed assessment of currently available graph layout techniques, specifically how they can be used by visualisation practitioners, and to motivate further research in the area.