EnvirVis19

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https://diglib.eg.org/handle/10.2312/2632782

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Browsing EnvirVis19 by Subject "Environmental sciences"

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    On Inconvenient Images: Exploring the Design Space of Engaging Climate Change Visualizations for Public Audiences
    (The Eurographics Association, 2019) Windhager, Florian; Schreder, Günther; Mayr, Eva; Bujack, Roxana and Feige, Kathrin and Rink, Karsten and Zeckzer, Dirk
    If there ever was a model theme for information visualization, climate change arguably checks all the boxes. Omnipresent and relevant, yet abstract and statistical by nature, as well as invisible for the naked eye - climate change is a subject matter in need for perception and cognition support par excellence. Consequently, a large number of data journalists and science communicators utilize visual representations of climate change data to provide (a) information, and to (b) raise consciousness and encourage behavioral adaptation. Multiple design strategies have been developed to make the complex (non-)phenomenon accessible for visual perception and reasoning of public audiences. Despite of its obvious societal relevance, the visualization community has not had a systematic look at this nascent application field until now. With this paper we aim to close this gap and survey climate change visualizations to explore their design space. With specific regard to visualizations geared to inform non-expert users in the context of journalism and science communication, we analyze a sample of representations to document design choices and communication strategies, including options of persuasive and engaging design.
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    Topology-based Feature Detection in Climate Data
    (The Eurographics Association, 2019) Kappe, Christopher P.; Böttinger, Michael; Leitte, Heike; Bujack, Roxana and Feige, Kathrin and Rink, Karsten and Zeckzer, Dirk
    The weather and climate research community needs to analyze increasingly large datasets, mostly obtained by observations or produced by simulations. Ensemble simulation techniques, which are used to capture uncertainty, add a further dimension to the multivariate time-dependent 3D data, even tightening the challenge of finding relevant information in the data for answering the respective research questions. In this paper we propose a topology-based method to support the visual analysis of climate data by detecting regions with particularly strong local minima or maxima and highlighting them with colored contours. Combined with preceding clustering of the data fields, typical spatial patterns characterizing the climate variability are detected and visualized. We demonstrate the utility of our method with a study of global temperature anomalies of a 150-years ensemble simulation consisting of 100 members.