18,267 research outputs found

    Uncertain Flow Visualization using LIC

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    In this paper we look at the Line Integral Convolution method for flow visualization and ways in which this can be applied to the visualization of two dimensional, steady flow fields in the presence of uncertainty. To achieve this, we start by studying the method and reviewing the history of modifications other authors have made to it in order to improve its efficiency or capabilities, and using these as a base for the visualization of uncertain flow fields. Finally, we apply our methodology to a case study from the field of oceanography

    Modelling and visualizing sustainability assessment in urban environments

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    Major urban development projects extend over prolonged timescales (up to 25 years in the case of major regeneration projects), involve a large number of stakeholders, and necessitate complex decision making. Comprehensive assessment of critical information will involve a number of domains, such as social, economic and environmental, and input from a wide a range of stakeholders. This makes rigorous and holistic decision making, with respect to sustainability, exceptionally difficult without access to appropriate decision support tools. Assessing and communicating the key aspects of sustainability and often conflicting information remains a major hurdle to be overcome if sustainable development is to be achieved. We investigate the use of an integrated simulation and visualization engine and will test if it is effective in: 1) presenting a physical representation of the urban environment, 2) modelling sustainability of the urban development using a subset of indicators, here the modelling and the visualization need to be integrated seamlessly in order to achieve real time updates of the sustainability models in the 3D urban representation, 3) conveying the sustainability information to a range of stakeholders making the assessment of sustainability more accessible. In this paper we explore the first two objectives. The prototype interactive simulation and visualization platform (S-City VT) integrates and communicates complex multivariate information to diverse stakeholder groups. This platform uses the latest 3D graphical rendering techniques to generate a realistic urban development and novel visualization techniques to present sustainability data that emerge from the underlying computational model. The underlying computational model consists of two parts: traditional multicriteria evaluation methods and indicator models that represent the temporal changes of indicators. These models are informed from collected data and/or existing literature. The platform is interactive and allows real time movements of buildings and/or material properties and the sustainability assessment is updated immediately. This allows relative comparisons of contrasting planning and urban layouts. Preliminary usability results show that the tool provides a realistic representation of a real development and is effective at conveying the sustainability assessment information to a range of stakeholders. S-City VT is a novel tool for calculating and communicating sustainability assessment. It therefore begins to open up the decision making process to more stakeholders, reducing the reliance on expert decision makers

    Uncertainty in phylogenetic tree estimates

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    Estimating phylogenetic trees is an important problem in evolutionary biology, environmental policy and medicine. Although trees are estimated, their uncertainties are discarded by mathematicians working in tree space. Here we explicitly model the multivariate uncertainty of tree estimates. We consider both the cases where uncertainty information arises extrinsically (through covariate information) and intrinsically (through the tree estimates themselves). The importance of accounting for tree uncertainty in tree space is demonstrated in two case studies. In the first instance, differences between gene trees are small relative to their uncertainties, while in the second, the differences are relatively large. Our main goal is visualization of tree uncertainty, and we demonstrate advantages of our method with respect to reproducibility, speed and preservation of topological differences compared to visualization based on multidimensional scaling. The proposal highlights that phylogenetic trees are estimated in an extremely high-dimensional space, resulting in uncertainty information that cannot be discarded. Most importantly, it is a method that allows biologists to diagnose whether differences between gene trees are biologically meaningful, or due to uncertainty in estimation.Comment: Final version accepted to Journal of Computational and Graphical Statistic
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