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This dissertation introduces FESM (Flood Event Simulation Model), a Geographic Information System (GIS) tool designed for use on gaged river systems that can be used to guide logistic support during disaster events. FESM rapidly generates flood predictions using elevation data from real-world sensors or generated by other models. Verification and validation data for FESM are provided. In order to construct a visualization system for interacting with FESM outputs, single buffer and dual buffer techniques for moving massive datasets to the GPU for processing using OpenCL were rigorously tested and timed, and an analysis of the costs/benefits of using buffers or images was conducted. Finally, DRO (Dynamic Raster Overlay), a visualization system for analysis of datasets composed of multiple overlapping flood maps is introduced, and expert feedback is provided on the effectiveness of DRO with selected case studies.

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Arsenic is a universal environmental toxicant associated mostly with skin related diseases in people exposed to low doses over a long term. Low dose arsenic trioxide (ATO) with long exposure will lead to chronic exposure. Experiments were performed to provide new knowledge on the incompletely understood mechanisms of action of chronic low dose inorganic arsenic in keratinocytes. Cytotoxicity patterns of ATO on long-term cultures of HaCaT cells on collagen IV was studied over a time course of 14 days. DNA damage was also assessed. The percentages of viable cells after exposure were measured on Day 2, Day 5, Day 8, and Day 14. Statistical and visual analytics approaches were used for data analysis. In the result, a biphasic toxicity response was observed at a 5 μg/ml dose with cell viability peaking on Day 8 in both chronic and acute exposures. Furthermore, a low dose of 1 μg/ml ATO enhanced HaCaT keratinocyte proliferation but also caused DNA damage. Global gene expression study using microarray technique demonstrated differential expressions of genes in HaCaT cell exposed to 0.5 μg/ml dose of ATO up to 22 passages. Four of the up-regulated and 1 down-regulated genes were selected and confirmed with qRT-PCR technique. These include; Aldo-Keto Reductase family 1, member C3 (AKR1C3), Insulin Growth Factor-Like family member 1 (IGFL1), Interleukin 1 Receptor, type 2 (IL1R2) and Tumour Necrosis Factor [ligand] Super-Family, member 18 (TNFSF18), and down-regulated Regulator of G-protein Signalling 2 (RGS2). The decline in growth inhibiting gene (RGS2) and increase in AKR1C3 may be the contributory path to chronic inflammation leading to metaplasia. This pathway is proposed to be a mechanism leading to carcinogenesis in skin keratinocytes. The observed over expression of IGFL1 may be a means of triggering carcinogenesis in HaCaT keratinocytes. In conclusion, it was established that at very low doses, arsenic is genotoxic and induces aberrations in gene expression though it may appear to enhance cell proliferation. The expression of two genes encoding membrane proteins IL1R2 and TNFSF18 may serve as possible biomarkers of skin keratinocytes intoxication due to arsenic exposure. This research provides insights into previously unknown gene markers that may explain the mechanisms of arsenic-induced dermal disorders including skin cancer / Environmental Sciences / D. Phil. (Environmental science)

Visual Analytics is a multi-disciplinary field that uses interactive visualisations to promote and assist the analytic reasoning and generate insights. Understanding the perceptual and cognitive factors is key to the progress in this field. This research focuses on understanding the benefits of interaction in terms of insight generation Moreover, this investigation explores the compounding effects individual differences have with interaction when analysing data to generate insights. This study investigated the individual differences in two sets; psychometric set measures, and a sensorial preferences multimodal learning style. Interaction was analysed from an information visualisation perspective, exploring the Visual Mapping and View Transformation interaction, by isolating interaction as an independent variable. Moreover, the View Transformation experiment used two different visual representations 2D and 3D. Additionally, the individual differences were analysed using the aptitude-by-treatment interaction (ATI) methodology. The ATI approach enabled the assessment of the performance gains in terms of insight generation according to pre-defined set levels of individual differences measures. This thesis confirms the benefits of interaction in generating more insights and increasing their accuracy, whilst facilitating the generation of insights requiring lower mental effort. Further, the results show significant conjoint effects between interaction and individual differences. Furthermore this research revealed a performance difference between 2D and 3D visual representation in the serious game problem solving context. Overall, this thesis provides tangible proof that both visual mapping and view transformation interaction are beneficial to visual analytics in generating insights. Strengthening the view that interaction with the problem-set improves understanding, and the number of insights gleaned into the problem and that more research into the use of individual differences, as a performance predictor in Visual Analytics is beneficial.

Neuroscientists are increasingly overwhelmed by new recordings of the nervous system. These recordings are significantly increasing in size due to new electrophysiological techniques, such as multi-electrode arrays. These techniques can simultaneously record the electrical activity (or spike trains) from thousands of neurons. These new datasets are larger than the traditional datasets recorded from single electrodes where fewer than ten spike trains are usually recorded. Consequently, new tools are now required to effectively analyse these new datasets. This thesis describes how techniques from the field of Visual Analytics can be applied to detect specific patterns in spike train data. These techniques are realised in a software tool called Neurigma. Neurigma is a collection of visual representations of spike train data that are unified to provide a coordinated representation of the data. The visual representations within Neurigma include: an interactive raster plot, an improved correlation grid, a novel representation called the correlation plot (which includes a novel coupling estimation algorithm), and a novel network diagram. These views provide insight into spike train data, and particularly, they identify correlated patterns, called functional connectivity. Within this thesis Neurigma is used to analyse synthetically generated datasets and experimental recordings. Three main findings are presented. First, propagating spiral patterns are identified within recordings from the neonatal mouse retina. Second, functional connectivity is identified within the cat visual cortex. Finally, the functional connectivity of a large synthetic dataset, of 1000 spike trains, is accurately classified into direct, indirect and common input coupling.

Cette thèse présente une nouvelle méthodologie pour analyser des réseaux. Nous développons l'intrication d'un réseau multiplex, qui se matérialise sous forme d'une mesure d'intensité et d'homogénéité, et d'une abstraction, le réseau d'interaction des catalyseurs, auxquels sont associés des indices d'intrication. Nous présentons ensuite la mise en place d'outils spécifiques pour l'analyse visuelle des réseaux complexes qui tirent profit de cette méthodologie. Ces outils présente une vue double de deux réseaux,qui inclue une un algorithme de dessin, une interaction associant brossage d'une sélection et de multiples liens pré-attentifs. Nous terminons ce document par la présentation détaillée d'applications dans de multiples domaines. / When it comes to comprehension of complex phenomena, humans need to understand what interactions lie within them.These interactions are often captured with complex networks. However, the interaction pluralism is often shallowed by traditional network models. We propose a new way to look at these phenomena through the lens of multiplex networks, in which catalysts are drivers of the interaction through substrates. To study the entanglement of a multiplex network is to study how edges intertwine, in other words, how catalysts interact. Our entanglement analysis results in a full set of new objects which completes traditional network approaches: the entanglement homogeneity and intensity of the multiplex network, and the catalyst interaction network, with for each catalyst, an entanglement index. These objects are very suitable for embedment in a visual analytics framework, to enable comprehension of a complex structure. We thus propose of visual setting with coordinated multiple views. We take advantage of mental mapping and visual linking to present simultaneous information of a multiplex network at three different levels of abstraction. We complete brushing and linking with a leapfrog interaction that mimics the back-and-forth process involved in users' comprehension. The method is validated and enriched through multiple applications including assessing group cohesion in document collections, and identification of particular associations in social networks.

Representações visuais têm sido adotadas na exploração de conjuntos de documentos, auxiliando a extração de conhecimento sem que seja necessária a análise individual de milhares de textos. Mapas de documentos, em particular, apresentam documentos individualmente representados espalhados em um espaço visual, refletindo suas relações de similaridade ou conexões. A construção destes mapas de documentos inclui, entre outras tarefas, o posicionamento dos textos e a identificação automática de áreas temáticas. Um desafio é a visualização de conjuntos dinâmicos de documentos. Na visualização de informação, é comum que alterações no conjunto de dados tenham um forte impacto na organização do espaço visual, dificultando a manutenção, por parte do usuário, de um mapa mental que o auxilie na interpretação dos dados apresentados e no acompanhamento das mudanças sofridas pelo conjunto de dados. Esta tese introduz um algoritmo para a construção dinâmica de mapas de documentos, capaz de manter uma disposição coerente à medida que elementos são adicionados ou removidos. O processo, inerentemente incremental e de baixa complexidade, utiliza um espaço bidimensional dividido em células, análogo a um tabuleiro de xadrez. Resultados consistentes foram alcançados em comparação com técnicas não incrementais de projeção de dados multidimensionais, tendo sido a técnica aplicada também em outros domínios, além de conjuntos de documentos. A visualização resultante não está sujeita a problemas de oclusão. A identificação de áreas temáticas é alcançada com técnicas de extração de regras de associação representativas para a identificação automática de tópicos. A combinação da extração de tópicos com a projeção incremental de dados em um processo integrado de mineração visual de textos compõe um espaço visual em que tópicos e áreas de interesse são destacados e atualizados à medida que o conjunto de dados é modificado / Visual representations are often adopted to explore document collections, assisting in knowledge extraction, and avoiding the thorough analysis of thousands of documents. Document maps present individual documents in visual spaces in such a way that their placement reflects similarity relations or connections between them. Building these maps requires, among other tasks, placing each document and identifying interesting areas or subsets. A current challenge is to visualize dynamic data sets. In Information Visualization, adding and removing data elements can strongly impact the underlying visual space. That can prevent a user from preserving a mental map that could assist her/him on understanding the content of a growing collection of documents or tracking changes on the underlying data set. This thesis presents a novel algorithm to create dynamic document maps, capable of maintaining a coherent disposition of elements, even for completely renewed sets. The process is inherently incremental, has low complexity and places elements on a 2D grid, analogous to a chess board. Consistent results were obtained as compared to (non-incremental) multidimensional scaling solutions, even when applied to visualizing domains other than document collections. Moreover, the corresponding visualization is not susceptible to occlusion. To assist users in indentifying interesting subsets, a topic extraction technique based on association rule mining was also developed. Together, they create a visual space where topics and interesting subsets are highlighted and constantly updated as the data set changes