Doris Dransch

Assessing the quality of geoscientific simulation models with visual analytics methods-a design study

Simulation models are essential means of scientific knowledge building and also the basis for decision-making. Because of their relevance, they have to be assessed thoroughly with respect to their quality. Simulation model assessment comprises two challenges: (a) modelers have to create a comprehensive mental image of the models quality despite the massive multidimensional, multivariate, and often heterogeneous data; and (b) the model assessment process should be as efficient as possible. We face these challenges with a visual analytics approach. We aim at developing interactive visual representations which, in combination with present computational analysis methods, support the scientists reasoning process to enhance the assessment of simulation models. In a design study, we analyzed two exemplary reasoning processes which cover the main model assessment procedures: the evaluation of the internal coherence of the models structure and behavior and the assessment of its empirical validity. The analysis was conducted by means of a user- and task-centered approach which combines several knowledge elicitation techniques and task analysis concepts. We derived domain tasks as well as cognitive actions and developed and implemented interactive visualization components which supplement the statistical analysis methods already used. An informal qualitative user study shows that our visual analytics approach and tools help gain a more detailed mental image and hence a better understanding of the data and the underlying simulation model and allow for a faster and more comprehensive assessment of the simulation model.

Visual analytics for correlation-based comparison of time series ensembles

An established approach to studying interrelations between two non‐stationary time series is to compute the ‘windowed’ cross‐correlation (WCC). The time series are divided into intervals and the cross‐correlation between corresponding intervals is calculated. The outcome is a matrix that describes the correlation between two time series for different intervals and varying time lags. This important technique can only be used to compare two single time series. However, many applications require the comparison of ensembles of time series. Therefore, we propose a visual analytics approach that extends the WCC to support a correlation‐based comparison of two ensembles of time series. We compute the pairwise WCC between all time series from the two ensembles, which results in hundreds of thousands of WCC matrices. Statistical measures are used to derive a concise description of the time‐varying correlations between the ensembles as well as the uncertainty of the correlation values. We further introduce a visually scalable overview visualization of the computed correlation and uncertainty information. These components are combined with multiple linked views into a visual analytics system to support configuration of the WCC as well as detailed analysis of correlation patterns between two ensembles. Two use cases from very different domains, cognitive science and paleoclimatology, demonstrate the utility of our approach.

Visualization of Geospatial Time Series from Environmental Modeling Output

Environmental models produce geospatial time series containing many spatio-temporal patterns. Scientists need to understand these patterns to analyze the behavior of the simulated environmental systems. We combine clustering and visualization to generate an intuitive visual summary of geospatial time series that captures the data’s prominent spatio-temporal information. As a first step, we evaluated our approach with well-understood observational data. Our visualization depicted all prominent features of these data suggesting that our method is readily applicable to environmental model output.

Maps in the Natural Disasters Networking Platform (NaDiNe) — Meeting the users’ needs: from static images to highly interactive real-time information integration

Maps are one of the most important information sources for people working in the context of natural disasters. Recently established concepts in information technology and Geoinformatics allow for sophisticated mapping architectures based on distributed static and real-time data. Within the Natural Disasters Networking Platform (NaDiNe) established by the Helmholtz Association of German Research Centres, several concepts have been established to exploit the full use of the Internet for providing maps in the context of natural disasters. In different use cases, an overview of current developments in the framework of NaDiNe is given. Starting with simple user-adapted maps that are linked via HTML, WebMapServices are integrated in order to provide interactivity with the user. With respect to data sources, approaches to integrate simulation results, real-time measurements and rapid mapping results are sketched. In a further use case it is shown that the Internet can also be used to collect data from distributed sources. For earthquakes in Germany, a web-based architecture that gathers intensity data via the Internet has been developed.

Towards Interactive Definition of Fast Surrogate Models for Geochemical Simulations Using Visual Analysis

Geochemical models serve wide-ranging geoscientific applications for underground resource exploitation, aquifer remediation, gas storage and similar problems. Such models are time consuming to calculate. Replacing the fullcapability simulation model, for each element of spatial discretization, with a fast surrogate is a promising acceleration approach for this problem. The balancing of speed and accuracy trade-off inherent to the surrogate modeling approach requires expert involvement and is best supported interactively. In this paper we argue that Visual Analysis has a prominent role to play for facilitating this process. It allows to involve expert knowledge regarding the specific characteristics of application scenario as part of the surrogate creation process. We describe the core problem of accelerating geochemical simulation by surrogate models and outline a strategy for approaching it with Visual Analysis.

Improving the Reusability of Spatiotemporal Simulation Models: Using MDE to Implement Cellular Automata

Numerous modeling and simulation tools, frameworks, and environments support domain experts with the implementation of spatiotemporal simulation models. The implemented models are usually bound to specific tools, because specific modeling languages, simulation engines, or processing platforms have to be used. To improve model reusability, we propose an implementation approach that applies Model Driven Engineering (MDE). In this approach, a simulation model is described on three different levels of abstraction. Starting from an abstract description of a simulation model by the modeler, this model is automatically transformed through all levels into executable code. In contrast to common implementation technologies, the intermediate steps of the transformation are clearly and formally defined by metamodels. For model execution, existing general purpose simulation and spatial data processing frameworks may be used. In this paper, the three-level approach and its application to the modeling of cellular automata are described. Partial metamodels and transformations are presented for two of the three levels. The MDE-approach provides means to enhance model reusability and promotes transparency in simulation modeling. Moreover, a tight integration of simulation and spatial data processing can be realized by synthesizing executable software which is composed of generic spatial data processing and simulation functionality.