<p>Visualizing multidimensional spatial data is an essential visual analysis strategy, it helps us interpret and communicate how different variables correlate to geographical information. In this study, we proposed an abstract contextual visualization that encodes data on the boundaries of spatial distributions and developed a new algorithm, AuroraMap. AuroraMap projects the spatial data to the boundaries of the distributions and color-encodes the densities continuously. We further conducted the user experiments, and the results show users can detect the relative locations and scopes of the clusters. Furthermore, users can quantitatively determine the peak value of each cluster’s density. The method provides three contributions: (1) freeing up and saving the graphical visualization space; (2) assisting the users to quantitatively estimate the clusters inside distributions; (3) facilitating the visual comparisons for multiple and multivariate spatial distributions. In the end, we demonstrated two applications with real-world religious infrastructural data by AuroraMap to visualize geospatial data within complex boundaries and compare multiple variables in one graph.</p><p> </p>

[pt] Um dos problemas mais antigos da computação gráfica tem sido a determinação de visibilidade. Vários algoritmos têm sido desenvolvidos para viabilizar modelos cada vez maiores e detalhados. Dentre estes algoritmos, destaca-se o frustum culling, cujo papel é remover objetos que não sejam visíveis ao observador. Esse algoritmo, muito comum em várias aplicações, vem sofrendo melhorias ao longo dos anos, a fim de acelerar ainda mais a sua execução. Apesar de ser tratado como um problema bem resolvido na computação gráfica, alguns pontos ainda podem ser aperfeiçoados, e novas formas de descarte desenvolvidas. No que se refere aos modelos massivos, necessita-se de algoritmos de alta performance, pois a quantidade de cálculos aumenta significativamente. Este trabalho objetiva avaliar o algoritmo de frustum culling e suas otimizações, com o propósito de obter o melhor algoritmo possível implementado em CPU, além de analisar a influência de cada uma de suas partes em modelos massivos. Com base nessa análise, novas técnicas de frustum culling serão desenvolvidas, utilizando o poder computacional da GPU (Graphics Processing Unit), e comparadas com o resultado obtido apenas pela CPU. Como resultado, será proposta uma forma de frustum culling híbrido, que tentará aproveitar o melhor da CPU e da GPU. / [en] The definition of visibility is a classical problem in Computer Graphics. Several algorithms have been developed to enable the visualization of huge and complex models. Among these algorithms, the frustum culling, which plays an important role in this area, is used to remove invisible objects by the observer. Besides being very usual in applications, this algorithm has been improved in order to accelerate its execution. Although being treated as a well-solved problem in Computer Graphics, some points can be enhanced yet, and new forms of culling may be disclosed as well. In massive models, for example, algorithms of high performance are required, since the calculus arises considerably. This work analyses the frustum culling algorithm and its optimizations, aiming to obtain the state-of-the-art algorithm implemented in CPU, as well as explains the influence of each of its steps in massive models. Based on this analysis, new GPU (Graphics Processing Unit) based frustum culling techniques will be developed and compared with the ones using only CPU. As a result, a hybrid frustum culling will be proposed, in order to achieve the best of CPU and GPU processing.

We propose a real-time system to visualize multi-viewpoint visibility information for terrains, supporting flight path optimization for view coverage or vehicle exposure to ground. A volume rendered display and a haptic interface assist the user in selecting, assessing, and refining the computed flight path. We construct a three-dimensional scalar field representing the visibility of a point above the terrain, describe an efficient algorithm to compute visibility, and develop visual and haptic schemes to interact with the visibility field. Given the origin and destination, the desired flight path is computed using an efficient simulation of an articulated rope under the influence of the visibility gradient. The simulation framework also accepts user input, via the haptic interface, thereby allowing manual refinement of the flight path.