Visualizing large-scale uncertainty in astrophysical data

Visualization of uncertainty or error in astrophysical data is seldom available in simulations of astronomical phenomena, and yet almost all rendered attributes possess some degree of uncertainty due to observational error. Uncertainties associated with spatial location typically vary signicantly with scale and thus introduce further complexity in the interpretation of a given visualization. This paper introduces effective techniques for visualizing uncertainty in large-scale virtual astrophysical environments. Building upon our previous transparently scalable visualization architecture, we develop tools that enhance the perception and comprehension of uncertainty across wide scale ranges. Our methods include a unified color-coding scheme for representing log-scale distances and percentage errors, an ellipsoid model to represent positional uncertainty, an ellipsoid envelope model to expose trajectory uncertainty, and a magic-glass design supporting the selection of ranges of log-scale distance and uncertainty parameters, as well as an overview mode and a scalable WIM tool for exposing the magnitudes of spatial context and uncertainty.     

Visualizing network data

Networks are critical to modern society, and a thorough understanding of how they behave is crucial to their efficient operation. Fortunately, data on networks is plentiful; by visualizing this data, it is possible to greatly improve our understanding. Our focus is on visualizing the data associated with a network and not on simply visualizing the structure of the network itself. We begin with three static network displays; two of these use geographical relationships, while the third is a matrix arrangement that gives equal emphasis to all network links. Static displays can be swamped with large amounts of data; hence we introduce direct manipulation techniques that permit the graphs to continue to reveal relationships in the context of much more data. In effect, the static displays are parameterized so that interesting views may easily be discovered interactively. The software to carry out this network visualization is called SeeNet     

Visualizing oceanographic data

The use of visualization to study both field-collected and simulated oceanographic data is discussed. An overview is presented of three programs in which visualization has provided important insight or technology. The diverse technology used-animation, computer vision, and synthesis of signal processing, image processing, and display-illustrate the variety of methodologies that are increasing knowledge about the ocean environment     

Visualizing production planning data

Visualization of production planning data for decision making in manufacturing is nearly nonexistent. The planning problems at the Electronic Card Assembly and Test plant (ECAT) at IBM Austin, Texas, prompted the research project reported in this article. The project studied the characteristics of this type of managerial data, then developed special visualization techniques for constructing visual representations to support planners in developing superior production plans. A visualization prototype called VIZ planner was designed, implemented and empirically evaluated in a laboratory setting. Using this tool, hundreds of products, thousands of components, and many other factors can be visualized to provide planners with production planning insight     

Visualizing spatial multivalue data

We introduce multivalue data as a new data type in the context of scientific visualization. While this data type has existed in other fields, the visualization community has largely ignored it. Formally, a multivalue datum is a collection of values about a single variable. Multivalue data sets can be defined for multiple dimensions. A spatial multivalue data set consists of a multivalue datum at each physical location in the domain. The time dimension is equally valid. This leads to spatio-temporal multivalue data sets where there is time varying, multidimensional data with a multivalue datum at each location and time. The spatial multivalue data type captures multiple instances of the same variable at each location in space. Visualizing spatial multivalue data sets is a new challenge.     

Visualizing sparse gridded data sets

Gridded data sets with many missing values pose a problem because most visualization algorithms fail when presented with incomplete cells. We discuss visualization methods that handle this problem. Our primary interest is developing 3D images for Next-Generation Radar (Nexrad), a weather radar that makes a series of conical scans. Most of the time it has extremely sparse returns. Current visualization techniques for Nexrad simply discard the 3D nature of the data set and provide 2D plots of the grid's lowest layer, leaving the missing data colored black or transparent. Most standard visualization packages either fail or give incorrect visualizations in 3D because of the unusual nature of the data set     

Visualizing and modeling unstructured data

Scientific data are often sampled at unstructured spatial locations because of physical constraints, yet most visualization software applies only to gridded or regular data. We discuss several effective techniques for representing scalar and vector-valued functions that interpolate to irregularly located data. Special attention is given to the situations in which the sampling domain is a 2D plane, a 3D volume, or a closed 3D surface. The interpolants can be evaluated on a fine regular grid and they can then be visualized with conventional techniques. Instead of giving a comprehensive survey of this subject, we concentrate on several methods that were developed in the last couple of years.     

Visualizing large telecommunication data sets

Global telecommunication services create an enormous volume of real time data. Long distance voice networks, for example, can complete more than 250 million calls a day; wide area data networks can support many hundreds of thousands of virtual circuits and millions of Internet protocol (IP) flows and Web server sessions. Unlike terabyte databases, which typically contain images or multimedia streams, telecommunication databases mainly contain numerous small records describing transactions and network status events. The data processing involved therefore differs markedly, both in the number of records and the data items interpreted. To efficiently configure and operate these networks, as well as manage performance and reliability for the user, these vast data sets must be understandable. Increasingly, visualization proves key to achieving this goal. AT&T Infolab is an interdisciplinary project created in 1996 to explore how software, data management and analysis, and visualization can combine to attack information problems involving large scale networks. The data Infolab collects daily reaches tens of gigabytes. The Infolab project Swift-3D uses interactive 3D maps with statistical widgets, topology diagrams, and pixel oriented displays to abstract network data and let users interact with it. We have implemented a full scale Swift-3D prototype, which generated the examples presented     

Visualizing business data with generalized treemaps

Business data is often presented using simple business graphics. These familiar visualizations are effective for providing overviews, but fall short for the presentation of large amounts of detailed information. Treemaps can provide such detail, but are often not easy to understand. We present how standard treemap algorithms can be adapted such that the results mimic familiar business graphics. Specifically, we present the use of different layout algorithms per level, a number of variations of the squarified algorithm, the use of variable borders, and the use of non-rectangular shapes. The combined use of these leads to histograms, pie charts and a variety of other styles     

Visualizing environmental data at the EPA

Several environmental visualization projects being pursued by the US Environmental Protection Agency are discussed. One group develops models for the transport and deposition of airborne pollutants. Another group evaluates the positions of air quality monitoring sites with respect to the distribution of pollutants. Other researchers collaborate with investigators at NASA Langley and Lawrence Livermore National Labs. to examine global climate change. Still others study water quality and sedimentation in the Great Lakes region, electrical properties of carcinogens, subsurface contamination of waste disposal sites, and the air flow through and around buildings. The visualization requirements, software configurations, and general strategies for the projects are described     

Visualizing incomplete and partially ranked data

Ranking data, which result from m raters ranking n items, are difficult to visualize due to their discrete algebraic structure, and the computational difficulties associated with them when n is large. This problem becomes worse when raters provide tied rankings or not all items are ranked. We develop an approach for the visualization of ranking data for large n which is intuitive, easy to use, and computationally efficient. The approach overcomes the structural and computational difficulties by utilizing a natural measure of dissimilarity for raters, and projecting the raters into a low dimensional vector space where they are viewed. The visualization techniques are demonstrated using voting data, jokes, and movie preferences.     

Visualizing changes of hierarchical data using treemaps

While the treemap is a popular method for visualizing hierarchical data, it is often difficult for users to track layout and attribute changes when the data evolve over time. When viewing the treemaps side by side or back and forth, there exist several problems that can prevent viewers from performing effective comparisons. Those problems include abrupt layout changes, a lack of prominent visual patterns to represent layouts, and a lack of direct contrast to highlight differences. In this paper, we present strategies to visualize changes of hierarchical data using treemaps. A new treemap layout algorithm is presented to reduce abrupt layout changes and produce consistent visual patterns. Techniques are proposed to effectively visualize the difference and contrast between two treemap snapshots in terms of the map items' colors, sizes, and positions. Experimental data show that our algorithm can achieve a good balance in maintaining a treemap's stability, continuity, readability, and average aspect ratio. A software tool is created to compare treemaps and generate the visualizations. User studies show that the users can better understand the changes in the hierarchy and layout, and more quickly notice the color and size differences using our method.     

Visualizing meteorological data for a lay audience

The Triton system for visualizing meteorological data is discussed. Triton supports visualization of satellite data showing weather development in the past, scalar data such as temperature, snow height, or ozone concentration, vector data such as wind direction and strength, data related to cloud formations, contextual information such as geographical data, and additional information. A television weather forecast must present a lot of this information in a very short time, possibly resulting in information overload. Triton uses intuitive visualization to help viewers understand the information     

Visualizing dimensionality reduction of systems biology data

One of the challenges in analyzing high-dimensional expression data is the detection of important biological signals. A common approach is to apply a dimension reduction method, such as principal component analysis. Typically, after application of such a method the data is projected and visualized in the new coordinate system, using scatter plots or profile plots. These methods provide good results if the data have certain properties which become visible in the new coordinate system but which were hard to detect in the original coordinate system. Often however, the application of only one method does not suffice to capture all important signals. Therefore several methods addressing different aspects of the data need to be applied. We have developed a framework for linear and non-linear dimension reduction methods within our visual analytics pipeline SpRay. This includes measures that assist the interpretation of the factorization result. Different visualizations of these measures can be combined with functional annotations that support the interpretation of the results. We show an application to high-resolution time series microarray data in the antibiotic-producing organism Streptomyces coelicolor as well as to microarray data measuring expression of cells with normal karyotype and cells with trisomies of human chromosomes 13 and 21.     

Visualizing and animating the winged-edge data structure

The winged- and half-edge data structures are commonly used representations for polyhedron models. Due to the complexity, students in an introductory course to computer graphics usually have difficulty in handling these data structures and developing applications. This paper describes the authors’ effort in the development of a visualization and animation tool for teaching and learning these data structures. This tool also includes a simple pseudo-code-like language for algorithm design. Instructors may employ this tool for presentation and demonstration purposes. Students may use the simple language to develop and experiment with new algorithms before their actual implementation. The visualization and animation system may be used to explore and understand the relationship among mesh elements and algorithm execution.     

Visualizing and segmenting large volumetric data sets

Current systems for segmenting and visualizing volumetric data sets characteristically require the user to possess a technical sophistication in volume visualization techniques, thus restricting the potential audience of users. As large volumetric data sets become more common, segmentation and visualization tools need to deemphasize the technical aspects of visualization and let users exploit their content knowledge of the data set. This proves especially critical in an educational setting. In anatomical education, data sets such as the Visible Human Project provide significant learning opportunities, but students must have tools that let them apply, refine, and build on their anatomical knowledge without technical obstacles. I describe a software environment that uses immersive virtual reality technology to let users immediately apply their expert knowledge to exploring and visualizing volumetric data sets     

Taupe: Visualizing and analyzing eye-tracking data

Program comprehension is an essential part of any maintenance activity. It allows developers to build mental models of the program before undertaking any change. It has been studied by the research community for many years with the aim to devise models and tools to understand and ease this activity. Recently, researchers have introduced the use of eye-tracking devices to gather and analyze data about the developers’ cognitive processes during program comprehension. However, eye-tracking devices are not completely reliable and, thus, recorded data sometimes must be processed, filtered, or corrected. Moreover, the analysis software tools packaged with eye-tracking devices are not open-source and do not always provide extension points to seamlessly integrate new sophisticated analyses. Consequently, we develop the Taupe software system to help researchers visualize, analyze, and edit the data recorded by eye-tracking devices. The two main objectives of Taupe are compatibility and extensibility so that researchers can easily: (1) apply the system on any eye-tracking data and (2) extend the system with their own analyses. To meet our objectives, we base the development of Taupe: (1) on well-known good practices, such as design patterns and a plug-in architecture using reflection, (2) on a thorough documentation, validation, and verification process, and (3) on lessons learned from existing analysis software systems. This paper describes the context of development of Taupe, the architectural and design choices made during its development, and its documentation, validation and verification process. It also illustrates the application of Taupe in three experiments on the use of design patterns by developers during program comprehension.     

Visualizing large data sets in the earth sciences

The authors describe the capabilities of McIDAS , an interactive visualization system that is vastly increasing the ability of earth scientists to manage and analyze data from remote sensing instruments and numerical simulation models. McIDAS provides animated three-dimensional images and highly interactive displays. The software can manage, analyze, and visualize large data sets that span many physical variables (such as temperature, pressure, humidity, and wind speed), as well as time and three spatial dimensions. The McIDAS system manages data from at least 100 different sources. The data management tools consist of data structures for storing different data types in files, libraries of routines for accessing these data structures, system commands for performing housekeeping functions on the data files, and reformatting programs for converting external data to the system's data structures. The McIDAS tools for three-dimensional visualization of meteorological data run on an IBM mainframe and can load up to 128-frame animation sequences into the workstations. A highly interactive version of the system can provide an interactive window into data sets containing tens of millions of points produced by numerical models and remote sensing instruments. The visualizations are being used for teaching as well as by scientists