Dynamic visualization of geographic networks using surface deformations /

Visualization techniques for geographic data show vast variations which are well-developed over centuries. While most of the known techniques are sound for low dimensional data sets, few techniques exist for visualization of high dimensional data within the geographic framework. This thesis investigates visualization of temporal. high dimensional network data within the geographic context. The resulting visualization system employs network visualization techniques in conjunction with cartographic visualization methods for providing a qualitative feel for the data, while conventional methods are employed for detailed examination. In turn, the visualization facilitates comprehension of non-spatial variables with respect to the geographic context

Everything on the Table: Tabular, Graphic, and Interactive Approaches for Interpreting and Presenting Monte Carlo Simulation Data

Abstract Monte Carlo simulation studies (MCSS) form a cornerstone for quantitative methods research. They are frequently used to evaluate and compare the properties of statistical methods and inform both future research and current best practices. However, the presentation of results from MCSS often leaves much to be desired, with findings typically conveyed via a series of elaborate tables from which readers are expected to derive meaning. The goal of this dissertation is to explore, summarize, and describe a framework for the presentation of MCSS, and show how modern computing and visualization techniques improve their interpretability. Chapter One describes this problem by introducing the logic of MCSS, how they are conducted, what findings typically look like, and current practices for their presentation. Chapter Two demonstrates methods for improving the display of static tabular data, specifically via formatting, effects ordering, and rotation. Chapter Three delves into semi-graphic and graphical approaches for aiding the presentation of tabular data via shaded tables, and extensions to the tableplot and the hypothesis-error plot frameworks. Chapter Four describes the use of interactive computing applets to aid the exploration of complex tabular data, and why this is an ideal approach. Throughout this work, emphasis is placed on how such techniques improve our understanding of a particular dataset or model. Claims are supported with applied demonstrations. Implementation of the ideas from each chapter have been coded within the R language for statistical computing and are available for adoption by other researchers in a dedicated package (SimDisplay). It is hoped that these ideas might enhance our understanding of how to best present MCSS findings and be drawn upon in both applied and academic environments

Small Multiples with Gaps

Small multiples enable comparison by providing different views of a single data set in a dense and aligned manner. A common frame defines each view, which varies based upon values of a conditioning variable. An increasingly popular use of this technique is to project two-dimensional locations into a gridded space (e.g. grid maps), using the underlying distribution both as the conditioning variable and to determine the grid layout. Using whitespace in this layout has the potential to carry information, especially in a geographic context. Yet, the effects of doing so on the spatial properties of the original units are not understood. We explore the design space offered by such small multiples with gaps. We do so by constructing a comprehensive suite of metrics that capture properties of the layout used to arrange the small multiples for comparison (e.g. compactness and alignment) and the preservation of the original data (e.g. distance, topology and shape). We study these metrics in geographic data sets with varying properties and numbers of gaps. We use simulated annealing to optimize for each metric and measure the effects on the others. To explore these effects systematically, we take a new approach, developing a system to visualize this design space using a set of interactive matrices. We find that adding small amounts of whitespace to small multiple arrays improves some of the characteristics of 2D layouts, such as shape, distance and direction. This comes at the cost of other metrics, such as the retention of topology. Effects vary according to the input maps, with degree of variation in size of input regions found to be a factor. Optima exist for particular metrics in many cases, but at different amounts of whitespace for different maps. We suggest multiple metrics be used in optimized layouts, finding topology to be a primary factor in existing manually-crafted solutions, followed by a trade-off between shape and displacement. But the rich range of possible optimized layouts leads us to challenge single-solution thinking; we suggest to consider alternative optimized layouts for small multiples with gaps. Key to our work is the systematic, quantified and visual approach to exploring design spaces when facing a trade-off between many competing criteria—an approach likely to be of value to the analysis of other design spaces

A Pattern Approach to Examine the Design Space of Spatiotemporal Visualization

Pattern language has been widely used in the development of visualization systems. This dissertation applies a pattern language approach to explore the design space of spatiotemporal visualization. The study provides a framework for both designers and novices to communicate, develop, evaluate, and share spatiotemporal visualization design on an abstract level. The touchstone of the work is a pattern language consisting of fifteen design patterns and four categories. In order to validate the design patterns, the researcher created two visualization systems with this framework in mind. The first system displayed the daily routine of human beings via a polygon-based visualization. The second system showed the spatiotemporal patterns of co-occurring hashtags with a spiral map, sunburst diagram, and small multiples. The evaluation results demonstrated the effectiveness of the proposed design patterns to guide design thinking and create novel visualization practices

Information Visualization

Orientador : Prof. Dr. Cassyano Januário CorrerDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências da Saúde, Programa de Pós-Graduação em Ciências Farmacêuticas. Defesa: Curitiba, 10/02/2015Inclui referências : f. [84-99]Área de concentração: Insumos, medicamentos e correlatosResumo: Antecedentes: A farmacoterapia antiobesidade ainda é alvo de amplo debate científico e político; não por acaso, já que há pouca confiabilidade tanto no âmbito dos estudos primários, quanto secundários, seja pelo reduzido tamanho amostral, elevada heterogeneidade e/ou baixa qualidade metodológica. Uma avaliação completa entre literatura existente sobre estudos primários e secundários é capaz de levantar recomendações para futuras pesquisas e também de fornecer resultados confiáveis relativos à eficácia e segurança. Objetivos: Avaliar a eficácia, segurança e relação risco-benefício de anfepramona (dietilpropiona), femproporex e mazindol. Métodos: Para isso revisão sistemática de estudos primários, seguida de metanálises diretas, de múltiplos tratamentos (MTC) e análise multicritério de risco-benefício foram conduzidas. Medline (via Pubmed), SCOPUS, Scielo e Directory of Open Access Journals foram pesquisadas desde a data de inserção até março de 2016. Modelo de efeitos randômicos foi escolhido para realização da metanálise direta e heterogeneidade foi avaliada pelo método do I2, associado ao valor de p. Para as comparações de múltiplos tratamentos, modelo de efeitos randômicos bayesiano foi utilizado, sendo fixado o placebo como comparador. Análises multicritério de risco-benefício utilizaram modelo de simulações de Monte Carlo, sendo realizadas segundo modelo estocástico. Resultados: De 739 publicações identificadas, 25 foram incluídas nas metanálises. A avaliação global da Cochrane resultou em 19 estudos com alto risco de viés e seis com risco incerto. Devido à falta de informação em estudos primários, metanálise direta só foi possível para avaliação de anfepramona, mazindol, comparados ao placebo. Anfepramona apresentou redução de peso corporal maior do que placebo tanto para tratamento de curta-duração (< 180 dias) diferença entre médias (DM) -1.281 kg (IC 95%: -1.538; -1.024), I2: 0.0% (p = 0.379), quanto para tratamentos de longa-duração (? 180 dias) DM de -6.518 kg (IC 95%: -8.419; -4.617), I2: 0.0% (p = 0.719). Apenas estudos de longa-duração reportaram eficácia segundo redução de circunferência abdominal, redução de 5% e 10% do peso corporal, confirmando a eficácia de anfepramona superior ao placebo. Mazindol apresentou redução de peso corporal superior ao placebo DM -1.721 kg (IC 95%: -2.164; -1.278), I2: 0.9% (p = 0.388) em tratamento de curta-duração. Desfechos metabólicos foram pobremente reportados, inviabilizando as metanálises. Segundo análise qualitativa, reações adversas graves foram identificadas apenas nos relatos de caso, em detrimento dos ensaios clínicos. MTC corroboram com metanálises diretas referentes à superioridade de eficácia para anfepramona em tratamento de longa-duração e anfepramona e mazindol em tratamentos de curta-duração. Análises de risco-benefício variaram a depender da duração do tratamento e do conjunto de desfechos escolhidos. Conclusões: Tanto pelo predomínio de alto risco de viés e ausência de desfechos importantes para a avaliação da terapia antiobesidade, os medicamentos avaliados não apresentaram evidências suficientes para confirmar sua eficácia para o tratamento da obesidade. No entanto, não foram identificados dados de segurança robustos que corroborem com sua retirada do mercado. Ensaios clínicos randomizados futuros poderiam realizar mais análises do tipo fármaco contra fármaco, com reporte dos desfechos mudança de circunferência abdominal, mudança de peso corporal, participantes com 5% e 10% de redução de peso e biomarcadores metabólicos (pressão arterial, lipídios e glicídios), ao longo de 3, 6, 9 e 12 meses. Palavras-chave: Obesidade; Perda de peso; Resultado do Tratamento; Prática Clínica Baseada em Evidências.Abstract: Background: Anti-obesity pharmacotherapy remains the main subject of disagreement among specialists, not only in the scientific field but also in the regulatory market. This is probably due to small sample size, high level of heterogeneity and low methodological quality. A thorough assessment of the existing literature (primary and secondary studies) can generate recommendations for future research and also provide reliable findings related to efficacy and safety. Objectives: To evaluate efficacy, safety and risk-benefit ratio of anfepramone (diethylpropion), femproporex and mazindol. Methods: We systematically reviewed primary studies and followed our review with direct meta-analysis, mixed treatment comparison (MTC), and multi-criteria benefit-risk assessment. Medline (via Pubmed), SCOPUS, Scielo and Directory of Open Access Journals were searched until Mar 2016. Random effect models were chosen to perform direct meta-analysis and heterogeneity was explored through I2 associated to p-value. For MTC, a random effect model was used, specifically fixed placebo as baseline treatment. Multi-criteria assessments were run through the Markov Chain Monte Carlo (stochastic model) method. Results: It was identified 739 publications, being 25 included in meta-analysis. The global evaluation of Cochrane resulted in 19 studies with high level of bias and six with uncertain risk. Due to lack of information in primary studies, direct meta-analysis was conducted only to diethylpropion and mazindol, both compared to placebo. Diethylpropion showed higher loss of weight compared to placebo in the short-term (< 180 days) mean difference (MD) -1.281 kg (CI 95%: -1.538; -1.024), I2: 0.0% (p = 0.379) and long-term (? 180 days) MD -6.518 kg (CI 95%: -8.419; -4.617), I2: 0.0% (p = 0.719). Only studies with long-term follow-up reported efficacy in terms of abdominal circumference and reductions of 5% and 10% of body weight. Their results corroborated diethylpropion efficacy greater than placebo. Mazindol showed a loss of weight greater than placebo MD -1.721 kg (CI 95%: -2.164; -1.278), I2: 0.9% (p = 0.388)) in the short term; metabolic outcomes were poorly described, preventing meta-analysis. According to qualitative assessment, major adverse reactions were reported only in case reports, not being described in clinical trials, what in turns presented only moderate and minor adverse reactions. MTC corroborated the direct meta-analysis concerning the superiority of efficacy for diethylpropion in long-term and diethylpropion and mazindol in short-term. Risk-benefit assessment results varied according to treatment duration and outcomes chosen. Conclusions: Considering the high level of risk of bias and absence of important outcomes for anti-obesity therapy assessment, there is not enough evidence to support the effectiveness of the drugs evaluated do not have enough evidence to support their effectiveness on the treatment of obesity. However, the data do not justify their withdrawal from the market. Future randomized clinical trials should explore comparisons among active drugs (head-to-head), reporting changes in abdominal circumference, body weight change, loss of 5 and 10% of body weight and also in metabolic biomarkers (blood pressure, lipids and glucose) throughout 3, 6, 9 and 12 months. Keywords: Obesity; Weight loss; Treatment Outcome; Evidence-Based Practice

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