245 research outputs found

    VMap: An Interactive Rectangular Space-filling Visualization for Map-like Vertex-centric Graph Exploration

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    We present VMap, a map-like rectangular space-filling visualization, to perform vertex-centric graph exploration. Existing visualizations have limited support for quality optimization among rectangular aspect ratios, vertex-edge intersection, and data encoding accuracy. To tackle this problem, VMap integrates three novel components: (1) a desired-aspect-ratio (DAR) rectangular partitioning algorithm, (2) a two-stage rectangle adjustment algorithm, and (3) a simulated annealing based heuristic optimizer. First, to generate a rectangular space-filling layout of an input graph, we subdivide the 2D embedding of the graph into rectangles with optimization of rectangles' aspect ratios toward a desired aspect ratio. Second, to route graph edges between rectangles without vertex-edge occlusion, we devise a two-stage algorithm to adjust a rectangular layout to insert border space between rectangles. Third, to produce and arrange rectangles by considering multiple visual criteria, we design a simulated annealing based heuristic optimization to adjust vertices' 2D embedding to support trade-offs among aspect ratio quality and the encoding accuracy of vertices' weights and adjacency. We evaluated the effectiveness of VMap on both synthetic and application datasets. The resulting rectangular layout has better aspect ratio quality on synthetic data compared with the existing method for the rectangular partitioning of 2D points. On three real-world datasets, VMap achieved better encoding accuracy and attained faster generation speed compared with existing methods on graphs' rectangular layout generation. We further illustrate the usefulness of VMap for vertex-centric graph exploration through three case studies on visualizing social networks, representing academic communities, and displaying geographic information.Comment: Submitted to IEEE Visualization Conference (IEEE VIS) 2019 and 202

    Steeped in place : encountering Scotland in paintings of the sea

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    Art is analysed and understood most often in terms of who (the artist was) and when (the work was created). Where (the creative act occurred) is seldom considered as significant. However, human understanding of the world depends on spatiality as well as temporality. This thesis investigates spatiality in paintings of Scotland’s sea and coast. The purpose of the research was threefold: firstly, to develop a conceptual framework of spatiality that could describe any painting; secondly to develop a suite of methods that situated painter, painting and geography; thirdly to apply the framework and methodology to the Scottish paintings of one artist, American Jon Schueler. Three spatial concepts for analysing the Scottishness of paintings of the sea were characterised: space, place and scape. Interviews with six contemporary painters revealed geography’s phenomenological underpinning. With paintings by Joan Eardley and William McTaggart, methods were developed to situate any artwork. These included well-proven visual analysis techniques, augmented by an original programme that extracted a colour palette from the painting’s image. With works by Janette Kerr and Will Maclean, methods were established to situate the artist. These included identifying which facets of place were incorporated into each painting, and how the artist’s discourse revealed a spatial understanding. Finally, McTaggart paintings were explored to situate their geography. This included using site visits to interpret a painting and compiling deep maps to compare and contrast the spatiality of paintings of Eastern Carnoustie with Western Machrihanish. The concepts and methodology were then employed to scrutinise the complete inventory of Jon Schueler’s extant works. By situating paintings, artist and the geography of Mallaig using the developed methodology, Schueler was demonstrated to be strongly influenced by spatiality. The unique configuration of a Scottish place illuminated and clarified what he perceived in nature and how he responded to it. The case study suggested that with a sense of place, a painting of the sea provides an encounter with Scotland

    LIPIcs, Volume 277, GIScience 2023, Complete Volume

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    LIPIcs, Volume 277, GIScience 2023, Complete Volum

    12th International Conference on Geographic Information Science: GIScience 2023, September 12–15, 2023, Leeds, UK

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    Space Partitioning Schemes and Algorithms for Generating Regular and Spiral Treemaps

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    Treemaps have been widely applied to the visualization of hierarchical data. A treemap takes a weighted tree and visualizes its leaves in a nested planar geometric shape, with sub-regions partitioned such that each sub-region has an area proportional to the weight of its associated leaf nodes. Efficiently generating visually appealing treemaps that also satisfy other quality criteria is an interesting problem that has been tackled from many directions. We present an optimization model and five new algorithms for this problem, including two divide and conquer approaches and three spiral treemap algorithms. Our optimization model is able to generate superior treemaps that could serve as a benchmark for comparing the quality of more computationally efficient algorithms. Our divide and conquer and spiral algorithms either improve the performance of their existing counterparts with respect to aspect ratio and stability or perform competitively. Our spiral algorithms also expand their applicability to a wider range of input scenarios. Four of these algorithms are computationally efficient as well with quasilinear running times and the last algorithm achieves a cubic running time. A full version of this paper with all appendices, data, and source codes is available at \anonymizeOSF{\OSFSupplementText}

    Visualization of dynamic multidimensional and hierarchical datasets

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    When it comes to tools and techniques designed to help understanding complex abstract data, visualization methods play a prominent role. They enable human operators to lever age their pattern finding, outlier detection, and questioning abilities to visually reason about a given dataset. Many methods exist that create suitable and useful visual represen tations of static abstract, non-spatial, data. However, for temporal abstract, non-spatial, datasets, in which the data changes and evolves through time, far fewer visualization tech niques exist. This thesis focuses on the particular cases of temporal hierarchical data representation via dynamic treemaps, and temporal high-dimensional data visualization via dynamic projec tions. We tackle the joint question of how to extend projections and treemaps to stably, accurately, and scalably handle temporal multivariate and hierarchical data. The literature for static visualization techniques is rich and the state-of-the-art methods have proven to be valuable tools in data analysis. Their temporal/dynamic counterparts, however, are not as well studied, and, until recently, there were few hierarchical and high-dimensional methods that explicitly took into consideration the temporal aspect of the data. In addi tion, there are few or no metrics to assess the quality of these temporal mappings, and even fewer comprehensive benchmarks to compare these methods. This thesis addresses the abovementioned shortcomings. For both dynamic treemaps and dynamic projections, we propose ways to accurately measure temporal stability; we eval uate existing methods considering the tradeoff between stability and visual quality; and we propose new methods that strike a better balance between stability and visual quality than existing state-of-the-art techniques. We demonstrate our methods with a wide range of real-world data, including an application of our new dynamic projection methods to support the analysis and classification of hyperkinetic movement disorder data.Quando se trata de ferramentas e técnicas projetadas para ajudar na compreensão dados abstratos complexos, métodos de visualização desempenham um papel proeminente. Eles permitem que os operadores humanos alavanquem suas habilidades de descoberta de padrões, detecção de valores discrepantes, e questionamento visual para a raciocinar sobre um determinado conjunto de dados. Existem muitos métodos que criam representações visuais adequadas e úteis de para dados estáticos, abstratos, e não-espaciais. No entanto, para dados temporais, abstratos, e não-espaciais, isto é, dados que mudam e evoluem no tempo, existem poucas técnicas apropriadas. Esta tese concentra-se nos casos específicos de representação temporal de dados hierárquicos por meio de treemaps dinâmicos, e visualização temporal de dados de alta dimen sionalidade via projeções dinâmicas. Nós abordar a questão conjunta de como estender projeções e treemaps de forma estável, precisa e escalável para lidar com conjuntos de dados hierárquico-temporais e multivariado-temporais. Em ambos os casos, a literatura para técnicas estáticas é rica e os métodos estado da arte provam ser ferramentas valiosas em análise de dados. Suas contrapartes temporais/dinâmicas, no entanto, não são tão bem estudadas e, até recentemente, existiam poucos métodos hierárquicos e de alta dimensão que explicitamente levavam em consideração o aspecto temporal dos dados. Além disso, existiam poucas métricas para avaliar a qualidade desses mapeamentos visuais temporais, e ainda menos benchmarks abrangentes para comparação esses métodos. Esta tese aborda as deficiências acima mencionadas para treemaps dinâmicos e projeções dinâmicas. Propomos maneiras de medir com precisão a estabilidade temporal; avalia mos os métodos existentes, considerando o compromisso entre estabilidade e qualidade visual; e propomos novos métodos que atingem um melhor equilíbrio entre estabilidade e a qualidade visual do que as técnicas estado da arte atuais. Demonstramos nossos mé todos com uma ampla gama de dados do mundo real, incluindo uma aplicação de nossos novos métodos de projeção dinâmica para apoiar a análise e classificação dos dados de transtorno de movimentos

    New Approaches For Understanding Vehicle Emissions Using Remote Sensing

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    Despite improvements in technology and increasingly strict legislation, road transport remains a key source of air quality pollutants. Accurate emission estimates are important for informing policy to combat the deleterious effects of exhaust species on human health. A key advantage of vehicle emission remote sensing is it can rapidly measure and characterise hundreds of thousands of vehicles. However, to fully realise the potential of remote sensing and gain a comprehensive assessment of emissions, new developments in calculating emission factors are needed. A recurring theme of this thesis is the calculation of emission-engine power models, which allow remote sensing to be used to address more facets of vehicle emissions than it is typically able. A method is developed to calculate distance-specific emission factors, which is validated using portable emission measurement system data. Distance-specific emissions can be compared with other measurement techniques and legislation, and can be used in emission inventory development. A remote sensing-based inventory is directly compared to the UK National Atmospheric Emissions Inventory, achieving excellent carbon balance (within 1%) but revealing that NOx emissions may be being under-reported by up to 32% at a national level. Remote sensing data are also combined with a large driving activity database to address the effects of driver behaviour and challenge the COPERT approach for emission factor calculation. A robust statistical framework is used to assess emission deterioration, and it is shown that older gasoline cars show a skewed rate of deterioration whereas modern gasoline and diesel emissions are well controlled. A key conclusion is the importance of the differences between manufacturers, which are significant for individual vehicles, for emission deterioration, and in inventory development. This analysis shows that accounting for manufacturers in inventory calculations results in a 13.4% range in total NOx emissions, an influence not currently reflected in European emission inventories

    Visual exploration of semantic-web-based knowledge structures

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    Humans have a curious nature and seek a better understanding of the world. Data, in- formation, and knowledge became assets of our modern society through the information technology revolution in the form of the internet. However, with the growing size of accumulated data, new challenges emerge, such as searching and navigating in these large collections of data, information, and knowledge. The current developments in academic and industrial contexts target the corresponding challenges using Semantic Web techno- logies. The Semantic Web is an extension of the Web and provides machine-readable representations of knowledge for various domains. These machine-readable representations allow intelligent machine agents to understand the meaning of the data and information; and enable additional inference of new knowledge. Generally, the Semantic Web is designed for information exchange and its processing and does not focus on presenting such semantically enriched data to humans. Visualizations support exploration, navigation, and understanding of data by exploiting humans’ ability to comprehend complex data through visual representations. In the context of Semantic- Web-Based knowledge structures, various visualization methods and tools are available, and new ones are being developed every year. However, suitable visualizations are highly dependent on individual use cases and targeted user groups. In this thesis, we investigate visual exploration techniques for Semantic-Web-Based knowledge structures by addressing the following challenges: i) how to engage various user groups in modeling such semantic representations; ii) how to facilitate understanding using customizable visual representations; and iii) how to ease the creation of visualizations for various data sources and different use cases. The achieved results indicate that visual modeling techniques facilitate the engagement of various user groups in ontology modeling. Customizable visualizations enable users to adjust visualizations to the current needs and provide different views on the data. Additionally, customizable visualization pipelines enable rapid visualization generation for various use cases, data sources, and user group
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