Lloyd A. Treinish

A rule-based tool for assisting colormap selection

The paper presents an interactive approach for guiding the users select of colormaps in visualization. PRAVDAColor, implemented as a module in the IBM Visualization Data Explorer, provides the user a selection of appropriate colormaps given the data type and spatial frequency, the users task, and properties of the human perceptual system.

How not to lie with visualization

How data are represented visually has a powerful effect on how the structure in those data is perceived. For example, in Figure 1, four representations of an MRI scan of a human head are shown. The only difference between these images is the mapping of color to data values, yet, the four representations look very different. Furthermore, the inferences an analyst would draw from these representations would vary considerably. That is, variations in the method of representing the data can significantly influence the users perception and interpretation of the data. How NOT to Lie with Visualization http://www.research.ibm.com/dx/proceedings/pravda/truevis.htm

An architecture for rule-based visualization

In Rogowitz and Treinish (1993), we introduced an architecture for incorporating perceptual rules into the visualization process. In this architecture, higher-level descriptors of the data, metadata, flow to perceptual rules, which constrain visualization operations. In this paper, we develop a deeper analysis of the rules, the prerequisite metadata, and the system for enabling their operation.<<ETX>>

Task-specific visualization design

This case study in operational weather forecasting demonstrates the principles of task-specific visualization design: defining user needs, implementing that definition, and establishing techniques for different user goals. The current applications can generate visualizations for the Web after an intermediate step of migrating the products to a Web server. This proves advantageous in an operational environment because the forecaster has content control. However, direct generation within a Web browser, which requires a simplified user interface and content, will require further refinement of the task decomposition. The notion of task-driven customization of content and interface has succeeded in weather forecasting, but the idea also applies to other domains. The potential benefits should encourage visualization designers to adopt these principles in their application development.

Multi-resolution visualization techniques for nested weather models

Scaling of simulations challenges the effectiveness of conventional visualization methods. This problem becomes two-fold for mesoscale weather models that operate in near-real-time at cloud-scale resolution. For example, typical approaches to vector field visualization (e.g., wind) are based upon global methods, which may not illustrate detailed structure. In addition, such computations employ multi-resolution meshes to capture small-scale phenomena, which are not properly reflected in both vector and scalar realizations. To address the former critical point analysis and simple bandpass filtering of wind fields is employed for better seed point identification of streamline calculations. For the latter, an encapsulation of nested computational meshes is developed for general realization. It is then combined with the seed point calculation for an improved vector visualization of multi-resolution weather forecasting data.

Task-specific visualization design: a case study in operational weather forecasting

Efforts to create highly generic visualizations, both content and interface, often when applied to non research oriented or operational activities are composed of several goals. Although these goals may appear to be related, they are often composed of distinct tasks. Generic solutions, even if domain-specific, may lack sufficient focus to be effective for such purposes. The design of different visualization tools matched to a set of tasks but built on top of a common framework with a similar approach to content is a promising alternative. This hypothesis is tested in detail by application to a demanding problem-operational weather forecasting.

A statistical model for risk management of electric outage forecasts

Risk management of power outages caused by severe weather events, such as hurricanes, tornadoes, and thunderstorms, plays an important role in electric utility distribution operations. Damage prediction based on weather forecasts on an appropriate spatial scale can improve the efficiency of risk management by reducing the economic and societal costs associated with restoration efforts. We have developed a method of predicting the number of outages in a fashion that is suitable for use by electric utilities by using a Poisson regression model for spatial data in a Bayesian hierarchical framework. Particular attention is given to building models that incorporate uncertainty in the outage data from the perspective of multiple spatial resolutions and spatial correlation in the outage data. The outage-prediction model was developed using historical outage data from an electric utility company in the northeastern part of the United States. The model is being used by that company in the operations of its overhead electrical distribution system and emergency management operations. We discuss results to date and how the model is being applied. In addition to the damage forecasts, we have developed tools for risk visualization by displaying the uncertainty of the damage forecasts on geographic maps.

Object-oriented, dataflow visualization systems: a paradigm shift?

Discusses the breadth and the effectiveness of application visualization systems (AVSs). The current and future research areas involving AVSs, drawbacks and limitations of certain application areas, possible improvements to AVSs, and alternative analysis and visualization approaches are discussed.<<ETX>>

Visual data fusion for applications of high-resolution numerical weather prediction

Non-traditional applications of scientific data challenge the typical approaches to visualization. In particular popular scientific visualization strategies fail when the expertise of the data consumer is in a different field than the one that generated the data and data from the users domain must be utilized as well. This problem occurs when predictive weather simulations are used for a number of weather-sensitive applications. A data fusion approach is adopted for visualization design and utilized for specific example problems.

An interactive, discipline-independent data visualization system

Critical to the understanding of data is the ability to provide pictorial or visual representations of that data. Given the ability to access data via an appropriate, easy‐to‐use interface, a researcher must employ tools to visualize data as one important mechanism in the data analysis process. Visualization, then, is a method of computing that gives visual form to complex data utilizing graphics and imaging technology. To support correlative data analysis (i.e., working with data from a variety of sources to study a problem of scientific interest), there is an obvious focus on generic visualization via the development of discipline‐independent visualization techniques. The NSSDC (National Space Science Data Center) Graphics System (NGS) has been developed to provide an interactive discipline‐independent toolbox for nonprogrammers to achieve such visualization of data. The NGS supports displays of any arbitrary multidimensional subset of any data set by providing a large variety of different representation schemes, all of which are supported by implicit animation (i.e., slicing of a data set into sequences). In addition, the design of the NGS provides an open‐ended framework for data visualization, so that new capabilities can be added. New tools have been implemented as a result of NSSDC’s research in several areas of computer science, including novel computer graphics rendering techniques and data structures.