Development of a geovisual analytics environment using parallel coordinates with applications to tropical cyclone trend analysis

A global transformation is being fueled by unprecedented growth in the quality, quantity, and number of different parameters in environmental data through the convergence of several technological advances in data collection and modeling. Although these data hold great potential for helping us understand many complex and, in some cases, life-threatening environmental processes, our ability to generate such data is far outpacing our ability to analyze it. In particular, conventional environmental data analysis tools are inadequate for coping with the size and complexity of these data. As a result, users are forced to reduce the problem in order to adapt to the capabilities of the tools. To overcome these limitations, we must complement the power of computational methods with human knowledge, flexible thinking, imagination, and our capacity for insight by developing visual analysis tools that distill information into the actionable criteria needed for enhanced decision support. In light of said challenges, we have integrated automated statistical analysis capabilities with a highly interactive, multivariate visualization interface to produce a promising approach for visual environmental data analysis. By combining advanced interaction techniques such as dynamic axis scaling, conjunctive parallel coordinates, statistical indicators, and aerial perspective shading, we provide an enhanced variant of the classical parallel coordinates plot. Furthermore, the system facilitates statistical processes such as stepwise linear regression and correlation analysis to assist in the identification and quantification of the most significant predictors for a particular dependent variable. These capabilities are combined into a unique geovisual analytics system that is demonstrated via a pedagogical case study and three North Atlantic tropical cyclone climate studies using a systematic workflow. In addition to revealing several significant associations between environmental observations and tropical cyclone activity, this research corroborates the notion that enhanced parallel coordinates coupled with statistical analysis can be used for more effective knowledge discovery and confirmation in complex, real-world data sets

Development of a geovisual analytics environment using parallel coordinates with applications to tropical cyclone trend analysis

A global transformation is being fueled by unprecedented growth in the quality, quantity, and number of different parameters in environmental data through the convergence of several technological advances in data collection and modeling. Although these data hold great potential for helping us understand many complex and, in some cases, life-threatening environmental processes, our ability to generate such data is far outpacing our ability to analyze it. In particular, conventional environmental data analysis tools are inadequate for coping with the size and complexity of these data. As a result, users are forced to reduce the problem in order to adapt to the capabilities of the tools. To overcome these limitations, we must complement the power of computational methods with human knowledge, flexible thinking, imagination, and our capacity for insight by developing visual analysis tools that distill information into the actionable criteria needed for enhanced decision support. In light of said challenges, we have integrated automated statistical analysis capabilities with a highly interactive, multivariate visualization interface to produce a promising approach for visual environmental data analysis. By combining advanced interaction techniques such as dynamic axis scaling, conjunctive parallel coordinates, statistical indicators, and aerial perspective shading, we provide an enhanced variant of the classical parallel coordinates plot. Furthermore, the system facilitates statistical processes such as stepwise linear regression and correlation analysis to assist in the identification and quantification of the most significant predictors for a particular dependent variable. These capabilities are combined into a unique geovisual analytics system that is demonstrated via a pedagogical case study and three North Atlantic tropical cyclone climate studies using a systematic workflow. In addition to revealing several significant associations between environmental observations and tropical cyclone activity, this research corroborates the notion that enhanced parallel coordinates coupled with statistical analysis can be used for more effective knowledge discovery and confirmation in complex, real-world data sets

Polygenic Risk Modelling for Prediction of Epithelial Ovarian Cancer Risk

Funder: Funding details are provided in the Supplementary MaterialAbstractPolygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally-efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, “select and shrink for summary statistics” (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestry; 7,669 women of East Asian ancestry; 1,072 women of African ancestry, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestry. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38(95%CI:1.28–1.48,AUC:0.588) per unit standard deviation, in women of European ancestry; 1.14(95%CI:1.08–1.19,AUC:0.538) in women of East Asian ancestry; 1.38(95%CI:1.21-1.58,AUC:0.593) in women of African ancestry; hazard ratios of 1.37(95%CI:1.30–1.44,AUC:0.592) in BRCA1 pathogenic variant carriers and 1.51(95%CI:1.36-1.67,AUC:0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs.</jats:p

A Review of A Model and Framework for Visualization Exploration by Jankun-Kelly and Gertz Bibliographic Database Information: Label is JankunKellyTVCG2007.

• Three-part approach to capture and utilize information in visualization process. • Formal model of process to capture salient aspects

A Review of Geovisualization for Knowledge Construction and Decision Support by MacEachren et al. Bibliographic Database Information: Label is MacEachrenCGA2004. Category is Geographic Visualization. General

• We have a lot of georeferenced data today (e.g., satellite remote sensing readings, meteorological measurements, stream gauge readings,...). • Geovisualization is both a process for leveraging these data resources to meet scientific and societal needs and a research field that develops visual methods and tools to support a wide array of geospatial data applications. • Many challenges remain in geovisualization. • One area is supporting real-world knowledge construction and decision making. • Some aspects of this area involve distributed geovisualization (enabling visualization across software components, devices, people, and places). Integrating and Extending Perspectives • Geovisualization draws on cartographic and geographic information representation techniques and integrates these perspectives with more recent work in scivis and infovis, exploratory data analysis, and image analysis. • Geographic Visualizatio

Bibliographic Database Information: Label is SwanVR2003. Category is Human Factors and Geographic Visualization. Background

• Expanded upon a “sequential usability engineering process that is a cost-effective and scientificallyeffective progression.” • Evaluation component of process divided into three phases: – Heuristic evaluation- guidelines-based assessment performed by UI design expert. – Formative evaluation- user-based assessment with representative users; Purpose is to assess and improve a specific UI. – Summative evaluation- performed to statistically compare several UIs to see which is better. • Progression uses results of each phase through systematically refining the VE UI. • Follow-up to VR’99 paper. • Goals are to improve product (Dragon software) and process (summative evaluation). • Examination of tasks in real-world application context of battlefield visualization. • Summative evaluation appears time-consuming and expensive, but critical to validating science of VE design. • Key Point: “to improve and streamline summative evaluations for VEs, so that they are more cost-effective and efficient, helping others avoid some of the challenges we encountered. Software Application Used in Study • 3D map-based VE derived from Dragon system. • Dragon is a battlefield visualization system • Displays 3D map of battlespace, military entities (as models). • Allows user to navigate and view map and symbols. Users can also query and manipulate entities. 1 • Primary user interaction through a modified joystick (virtual laser-pointer metaphor). • Supported user interactions: – Pan and Zoom- movement of user’s eye point (x, y, or z). – Rotate- rotation of map around COI. – Tilt- rotation around COI which tilts or pitches the map. Tasks Performed by Subjects • Subjects perform series of 17 tasks (called a task set). • Task set questions had three categories: – Text tasks- searching for named items in map (either searching for terrain object to determine its name or looking for terrain object when given its name). – Map tasks- subject asked to place map in given position. – Geometric object tasks- subject asked to navigate relative to geometric solids. • Subjects started with a training set on a different map with similar features

Verification of Compartmental Epidemiological Models Using Metamorphic Testing, Model Checking and Visual Analytics

Abstract—Compartmental models in epidemiology are widely used as a means to model disease spread mechanisms and understand how one can best control the disease in case an outbreak of a widespread epidemic occurs. However, a signifi-cant challenge within the community is in the development of approaches that can be used to rigorously verify and validate these models. In this paper, we present an approach to rigorously examine and verify the behavioral properties of compartmen-tal epidemiological models under several common modeling scenarios including birth/death rates and multi-host/pathogen species. Using metamorphic testing, a novel visualization tool and model checking, we build a workflow that provides insights into the functionality of compartmental epidemiological models. Our initial results indicate that metamorphic testing can be used to verify the implementation of these models and provide insights into special conditions where these mathematical models may fail. The visualization front-end allows the end-user to scan through a variety of parameters commonly used in these models to elucidate the conditions under which an epidemic can occur. Further, specifying these models using a process algebra allows one to automatically construct behavioral properties that can be rigorously verified using model checking. Taken together, our approach allows for detecting implementation errors as well as handling conditions under which compartmental epidemiological models may fail to provide insights into disease spread dynamics

Guided analysis of hurricane trends using statistical processes integrated with interactive parallel coordinates

This paper demonstrates the promise of augmenting interactive multivariate representations with information from statistical processes in the domain of weather data analysis. Statistical regression, correlation analysis, and descriptive statistical calculations are integrated via graphical indicators into an enhanced parallel coordinates system, called the Multidimensional Data eXplorer (MDX). These statistical indicators, which highlight significant associations in the data, are complemented with interactive visual analysis capabilities. The resulting system allows a smooth, interactive, and highly visual workflow. The system’s utility is demonstrated with an extensive hurricane climate study that was conducted by a hurricane expert. In the study, the expert used a new data set of environmental weather data, composed of 28 independent variables, to predict annual hurricane activity. MDX shows the Atlantic Meridional Mode increases the explained variance of hurricane seasonal activity by 7-15 % and removes less significant variables used in earlier studies. The findings and feedback from the expert (1) validate the utility of the data set for hurricane prediction, and (2) indicate that the integration of statistical processes with interactive parallel coordinates, as implemented in MDX, addresses both deficiencies in traditional weather data analysis and exhibits some of the expected benefits of visual data analysis

Bibliographic Database Information: Label is HealeyTOG2004.

• In some situations, NPR rendering are more effective than an photograph. • SciVis researchers are looking a NPR techniques to improve expressiveness of data display. • For example, David Laidlaw extended Meier approach to visualize multi-dimensional data with painterly methods (LOOK AT Meier). • Success in scivis may indicate we can apply NPR techniques to information visualization. • Must ensure effective results. • NPR techniques are promising for building aesthetically pleasing visualizations by the viewers. Painting Styles • The authors restrict themselves to Impressionism art movement styles (IDEA: Consider other art movements like abstract, modern,...). Perceptual Properties • ”One of the most important lessons of the past twenty-five years is that human vision does not resemble the relatively faithful and largely passive process of modern photography.” • ”The goal of human vision is not to create a replica or image of the seen world in our heads.” • The author lists 5 important research findings in perception

Guided Text Search Using Adaptive Visual Analytics

This research demonstrates the promise of augmenting interactive visualizations with semi- supervised machine learning techniques to improve the discovery of significant associations and insights in the search and analysis of textual information. More specifically, we have developed a system called Gryffin that hosts a unique collection of techniques that facilitate individualized investigative search pertaining to an ever-changing set of analytical questions over an indexed collection of open-source documents related to critical national infrastructure. The Gryffin client hosts dynamic displays of the search results via focus+context record listings, temporal timelines, term-frequency views, and multiple coordinate views. Furthermore, as the analyst interacts with the display, the interactions are recorded and used to label the search records. These labeled records are then used to drive semi-supervised machine learning algorithms that re-rank the unlabeled search records such that potentially relevant records are moved to the top of the record listing. Gryffin is described in the context of the daily tasks encountered at the US Department of Homeland Security s Fusion Center, with whom we are collaborating in its development. The resulting system is capable of addressing the analysts information overload that can be directly attributed to the deluge of information that must be addressed in the search and investigative analysis of textual information