Understanding Geographic, Temporal, and Multi-Dimensional Trends Using Visualization in Health Care

This study sought to determine whether utilizing visualization in health care would allow a wider audience of health professionals to understand geographic, temporal, and multidimensional trends in health data. A visual analytics tool was developed in Tableau that allowed users to dynamically and interactively interact with the tool in order to understand the impact of ACA Medicaid expansion. Data from the County Health Rankings & Roadmap was used (Rankings Data). The tool was made available to 5 participants who all had a connection to health care. An evaluation of the tool was conducted to determine if a visual analytics approach was useful in understanding geographic, temporal, and multidimensional trends and communicating health analytics information through the form of a use case. This study concluded that visualization was in fact an effective means through which to help a variety of users to understand geographic, temporal, and multidimensional trends.Master of Science in Information Scienc

Challenging problems of geospatial visual analytics

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The Case for Visual Analytics of Arsenic Concentrations in Foods

Arsenic is a naturally occurring toxic metal and its presence in food could be a potential risk to the health of both humans and animals. Prolonged ingestion of arsenic contaminated water may result in manifestations of toxicity in all systems of the body. Visual Analytics is a multidisciplinary field that is defined as the science of analytical reasoning facilitated by interactive visual interfaces. The concentrations of arsenic vary in foods making it impractical and impossible to provide regulatory limit for each food. This review article presents a case for the use of visual analytics approaches to provide comparative assessment of arsenic in various foods. The topics covered include (i) metabolism of arsenic in the human body; (ii) arsenic concentrations in various foods; (ii) factors affecting arsenic uptake in plants; (ii) introduction to visual analytics; and (iv) benefits of visual analytics for comparative assessment of arsenic concentration in foods. Visual analytics can provide an information superstructure of arsenic in various foods to permit insightful comparative risk assessment of the diverse and continually expanding data on arsenic in food groups in the context of country of study or origin, year of study, method of analysis and arsenic species

La visualización como apoyo al proceso de atención de crisis

This article analyzes the visualization in crisis simulation as an technology alternative to enhance responsiveness to crises fire type. Other alternatives proposed in the article are improving the interoperability between GIS and agent-based systems and the incorporation of location-based services. However, the latter two support the solution to the problem of Visualization of the population densities by the simulator of crisis. To reach these conclusions begins with the process of crisis care kind fire in Colombia, the crisis care lead organizations are analyzed and the steps to care of crisis. Finally, this paper examines the possibility of implementing a visualization system in fire station of Usme using Android to support localization which implements location technologies and GIS.El tema central del artículo es analizar la visualización en simuladores de crisis como una alternativa tecnológica para mejorar la capacidad de respuesta a las crisis de tipo fuego. Otras alternativas propuestas en el artículo son mejorar la interoperabilidad entre SIG y sistemas basados en agentes y la incorporación de servicios basados en localización. Sin embargo, estas dos últimas, apoyan la solución al problema de visualización de densidades poblacionales por parte del simulador de crisis. Para llegar a estas conclusiones se parte de un panorama a cerca del proceso de atención de crisis tipo fuego en Colombia, en el que se analizan los principales organismos de atención de crisis y los pasos necesarios para atención de esta. Finalmente, se analiza la posibilidad de implementar un sistema de visualización en los bomberos de Usme usando android el cual implemente tecnologías de localización y GIS

Interactive maps: What we know and what we need to know

This article provides a review of the current state of science regarding cartographic interaction a complement to the traditional focus within cartography on cartographic representation. Cartographic interaction is defined as the dialog between a human and map mediated through a computing device and is essential to the research into interactive cartography geovisualization and geovisual analytics. The review is structured around six fundamental questions facing a science of cartographic interaction: (1) what is cartographic interaction (e.g. digital versus analog interactions interaction versus interfaces stages of interaction interactive maps versus mapping systems versus map mash-ups); (2) why provide cartographic interaction (e.g. visual thinking geographic insight the stages of science the cartographic problematic); (3) when should cartographic interaction be provided (e.g. static versus interactive maps interface complexity the productivity paradox flexibility versus constraint work versus enabling interactions); (4) who should be provided with cartographic interaction (e.g. user-centered design user ability expertise and motivation adaptive cartography and geocollaboration); (5) where should cartographic interaction be provided (e.g. input capabilities bandwidth and processing power display capabilities mobile mapping and location-based services); and (6) how should cartographic interaction be provided (e.g. interaction primitives objective-based versus operator-based versus operand-based taxonomies interface styles interface design)? The article concludes with a summary of research questions facing cartographic interaction and offers an outlook for cartography as a field of study moving forward

Iterative Visual Analytics and its Applications in Bioinformatics

Indiana University-Purdue University Indianapolis (IUPUI)You, Qian. Ph.D., Purdue University, December, 2010. Iterative Visual Analytics and its Applications in Bioinformatics. Major Professors: Shiaofen Fang and Luo Si. Visual Analytics is a new and developing field that addresses the challenges of knowledge discoveries from the massive amount of available data. It facilitates humans‘ reasoning capabilities with interactive visual interfaces for exploratory data analysis tasks, where automatic data mining methods fall short due to the lack of the pre-defined objective functions. Analyzing the large volume of data sets for biological discoveries raises similar challenges. The domain knowledge of biologists and bioinformaticians is critical in the hypothesis-driven discovery tasks. Yet developing visual analytics frameworks for bioinformatic applications is still in its infancy. In this dissertation, we propose a general visual analytics framework – Iterative Visual Analytics (IVA) – to address some of the challenges in the current research. The framework consists of three progressive steps to explore data sets with the increased complexity: Terrain Surface Multi-dimensional Data Visualization, a new multi-dimensional technique that highlights the global patterns from the profile of a large scale network. It can lead users‘ attention to characteristic regions for discovering otherwise hidden knowledge; Correlative Multi-level Terrain Surface Visualization, a new visual platform that provides the overview and boosts the major signals of the numeric correlations among nodes in interconnected networks of different contexts. It enables users to gain critical insights and perform data analytical tasks in the context of multiple correlated networks; and the Iterative Visual Refinement Model, an innovative process that treats users‘ perceptions as the objective functions, and guides the users to form the optimal hypothesis by improving the desired visual patterns. It is a formalized model for interactive explorations to converge to optimal solutions. We also showcase our approach with bio-molecular data sets and demonstrate its effectiveness in several biomarker discovery applications

Cognitive Foundations for Visual Analytics

In this report, we provide an overview of scientific/technical literature on information visualization and VA. Topics discussed include an update and overview of the extensive literature search conducted for this study, the nature and purpose of the field, major research thrusts, and scientific foundations. We review methodologies for evaluating and measuring the impact of VA technologies as well as taxonomies that have been proposed for various purposes to support the VA community. A cognitive science perspective underlies each of these discussions