Knowledge about visualization tasks plays an important role in choosing or building suitable visual representations to pursue them. Yet, tasks are a multi-faceted concept and it is thus not surprising that the many existing task taxonomies and models all describe different aspects of tasks, depending on what these task descriptions aim to capture. This results in a clear need to bring these different aspects together under the common hood of a general design space of visualization tasks, which we propose in this paper. Our design space consists of five design dimensions that characterize the main aspects of tasks and that have so far been distributed across different task descriptions. We exemplify its concrete use by applying our design space in the domain of climate impact research. To this end, we propose interfaces to our design space for different user roles (developers, authors, and end users) that allow users of different levels of expertise to work with it.

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Abstract. Network analysis has become an important approach in studying complex spatiotemporal behaviour within geophysical observation and simulation data. This new field produces increasing numbers of large geo-referenced networks to be analysed. Particular focus lies currently on the network analysis of the complex statistical interrelationship structure within climatological fields. The standard procedure for such network analyses is the extraction of network measures in combination with static standard visualisation methods. Existing interactive visualisation methods and tools for geo-referenced network exploration are often either not known to the analyst or their potential is not fully exploited. To fill this gap, we illustrate how interactive visual analytics methods in combination with geovisualisation can be tailored for visual climate network investigation. Therefore, the paper provides a problem analysis relating the multiple visualisation challenges to a survey undertaken with network analysts from the research fields of climate and complex systems science. Then, as an overview for the interested practitioner, we review the state-of-the-art in climate network visualisation and provide an overview of existing tools. As a further contribution, we introduce the visual network analytics tools CGV and GTX, providing tailored solutions for climate network analysis, including alternative geographic projections, edge bundling, and 3-D network support. Using these tools, the paper illustrates the application potentials of visual analytics for climate networks based on several use cases including examples from global, regional, and multi-layered climate networks.

Visualization, Cluster Analysis, Climate Impact Research,

No abstract

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This article is guided by the thesis that color is both -a rational way to structure and encode data visually, and a place where emotions like concern, fear and alarm can connect -and thus cultural readings can start from. This becomes particularly clear in visual climate communication where the colors blue and red are used in global future temperature maps and scenario graphs. Here, red colors are used to mark maximum values, temperature increases, great risk, anomalies and worst case scenarios like the RCP8.5 scenario, whereas blue colors denote cold temperatures but also illustrate best case scenarios.In order to analyze the different cultural layers that get triggered by color, the paper presents results derived by different methodologies. On the one hand image analysis methods of picture theory are used; on the other hand, qualitative interviews performed with a small group of recipients evaluate the thesis derived from theory. For this purpose, the author team, which is formed by a cultural/media scholar and a scholar from computer graphics, has experimentally altered the color scheme of one of the most important figures of the latest Intergovernmental Panel on Climate Change (IPCC) report: the map of historic temperature increases from the IPCC AR5 WGI (figure SPM.1). Testing six different color schemes the team investigated how the perception, emotional reaction and understanding is altered if the scheme employing blue, bright red and purple is replaced by other color schemes. Besides the original IPCC color scheme, the team tested blue-grey-black, green-purple and purple-green as well as less dazzling shades of red. With their study, the authors are able to indicate how the understanding and credibility of climate change visualization is influenced by color, and how different color spectrums significantly change the emotional and associative reaction of the visualization in relation to the recipient group. The outcome of the research provides a guidance to estimate the impact of color in respect to the aim of visually communicating the risks of climate change and convincing different recipient groups about the gravity of the issue.

Visualization has become an important ingredient of data analysis, supporting users in exploring data and confirming hypotheses. At the beginning of a visual data analysis process, data characteristics are often assessed in an initial data profiling step. These include, for example, statistical properties of the data and information on the data’s well-formedness, which can be used during the subsequent analysis to adequately parametrize views and to highlight or exclude data items. We term this information data descriptors, which can span such diverse aspects as the data’s provenance, its storage schema, or its uncertainties. Gathered descriptors encapsulate basic knowledge about the data and can thus be used as objective starting points for the visual analysis process. In this article, we bring together these different aspects in a systematic form that describes the data itself (e.g. its content and context) and its relation to the larger data gathering and visual analysis process (e.g. its provenance and its utility). Once established in general, we further detail the concept of data descriptors specifically for tabular data as the most common form of structured data today. Finally, we utilize these data descriptors for tabular data to capture domain-specific data characteristics in the field of climate impact research. This procedure from the general concept via the concrete data type to the specific application domain effectively provides a blueprint for instantiating data descriptors for other data types and domains in the future.