EnvirVis16

Permanent URI for this collection

https://diglib.eg.org/handle/10.2312/15277

Browse

Browsing EnvirVis16 by Subject "Applications"

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    A Data-Driven Approach to Categorize Climatic Microenvironments
    (The Eurographics Association, 2016) Häb, Kathrin; Middel, Ariane; Ruddell, Benjamin L.; Hagen, Hans; Karsten Rink and Ariane Middel and Dirk Zeckzer
    In urban climatology, identifying areas of similar microclimatic conditions helps to relate fine-scale urban morphology variations to their impact on atmospheric surroundings. Mobile transect measurements yield high-resolution microclimate data that allow for the delineation of these areas at a fine scale. However, the resulting spatio-temporal multivariate data is complicated and requires careful analysis and visualization to identify the emergent climatic microenvironments. Our previous work used a glyph-based visualization to comprehensively visualize spatially aggregated multivariate data from mobile measurements over diverse routes. This aggregation was conducted over a regular grid, and the utilized glyphs encoded multivariate relationships, average wind direction during data collection, number of transects traversing a grid cell, and grid cell size. In this paper, we reduce the visual complexity of the resulting map by coloring the background of the grid cells based on a comparison of the glyphs. The result is a gridded map that visually emphasizes spatial zones of similar multivariate relationships and that takes the information encoded by the glyphs into account. A preliminary evaluation shows that the described approach yields zones that line up with the physical structure of the study site.
  • Thumbnail Image
    Item
    Towards Visual Analytics for Multi-Sensor Analysis of Remote Sensing Archives
    (The Eurographics Association, 2016) Eggert, Daniel; Sips, Mike; Köthur, Patrick; Karsten Rink and Ariane Middel and Dirk Zeckzer
    To better detect and study processes on the Earth's surface, scientists want to combine various satellite data and extract potentially interesting patterns from the combined data. This analysis approach is called multi-sensor analysis. In this paper, we present an interactive visual exploration solution for the first important step of multi-sensor analysis: the assessment and selection of remote sensing scenes. This solution is the first step towards a larger Visual Analytics (VA) approach that turns multisensor analysis into a transparent and interactive analysis method. We conduct our research in the context of GeoMultiSens, which is an interdisciplinary research project between remote sensing, computer science and VA experts. To demonstrate the utility of our visual exploration solution, we use a real-world scenario: the assessment and selection of scenes in order to study the change of forest cover in Europe. The application example indicates that interactive visual exploration facilitates a structured assessment of the quantity and quality of remote sensing scenes and enables scientists to exclude low-quality scenes from subsequent multi-sensor analysis.
  • Thumbnail Image
    Item
    Visual Analysis of Reservoir Simulation Ensembles
    (The Eurographics Association, 2016) Höllt, Thomas; Ravanelli, Fabio Miguel de Matos; Hadwiger, Markus; Hoteit, Ibrahim; Karsten Rink and Ariane Middel and Dirk Zeckzer
    Hydrocarbon reservoir simulation models produce large amounts of heterogeneous data, combining multiple variables of different dimensionality, such as two or three-dimensional geospatial estimates with abstract estimates simulated for the complete field or different wells. In addition these simulations are nowadays often run as so-called ensemble simulations, to capture uncertainty of the model, as well as boundary conditions as variation in the output. The (visual) analysis of such data is a challenging process, due to the size and complexity of the data. In this paper we present an integrated system for the visual analysis of ensemble reservoir simulation data. We provide tools to inspect forecasts for multiple variables of complete fields, as well as different wells. Finally, we present a case study highlighting the effectiveness of the presented system.
  • Thumbnail Image
    Item
    Visualizing Malaria Spread Under Climate Variability
    (The Eurographics Association, 2016) Liang, Xing; Aggarwal, Rajat; Cherif, Alhaji; Gumel, Abba; Mascaro, Giuseppe; Maciejewski, Ross; Karsten Rink and Ariane Middel and Dirk Zeckzer
    In order to better control and prevent the infectious diseases, measures of vulnerability and risk to increased infectious disease outbreaks have been explored. Research investigating possible links between variations in climate and transmission of infectious diseases has led to a variety of predictive models for estimating the future impact of infectious disease under projected climate change. Underlying all of these approaches is the connection of multiple data sources and the need for computational models that can capture the spatio-temporal dynamics of emerging infectious diseases and climate variability, especially as the impact of climate variability on the land surface is becoming increasingly critical in predicting the geo-temporal evolution of infectious disease outbreaks. This paper presents an initial visualization prototype that combines data from population and climate simulations as inputs to a patch-based mosquito spread model for analyzing potential disease spread vectors and their relationship to climate variability.