Magnus Heitzler

A Design Space of Visualization Tasks

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.

GPU-Accelerated Rendering Methods to Visually Analyze Large-Scale Disaster Simulation Data

Emerging methodologies for natural hazard risk assessments involve the execution of a multitude of different interacting simulation models that produce vast amounts of spatio-temporal datasets. This data pool is further enlarged when such simulation results are post-processed using GIS operations, for example to derive information for decision-making. The novel approach presented in this paper makes use of the GPU-accelerated rendering pipeline to perform such operations on-the-fly without storing any results on secondary memory and thus saving large amounts of storage space. Particularly, algorithms for three frequently used geospatial analysis methods are provided, namely for the computation of difference maps using map algebra and overlay operations, distance maps and buffers as examples for proximity analyses as well as kernel density estimation and inverse distance weighting as examples for statistical surfaces. In addition, a visualization tool is presented that integrates these methods using a node-based data flow architecture. The application of this visualization tool to the results of a real-world risk assessment methodology used in civil engineering shows that the memory footprint of post-processing datasets can be reduced at the order of terabytes. Although the technique has several limitations, most notably the reduced interoperability with conventional analysis tools, it can be beneficial for other use cases. When integrated into desktop GIS applications, for example, it can be used to quickly generate a preview of the results of complex analysis chains or it can reduce the amount of data to be transferred to web or mobile GIS applications.

Towards Better WMS Maps Through the Use of the Styled Layer Descriptor and Cartographic Conflict Resolution for Linear Features

The Open Geospatial Consortium (OGC) Web Map Service (WMS) and Styled Layer Descriptor (SLD) standards define a way of dynamically producing maps from vector data. However, this dynamic process often results in maps that are not easily readable when the underlying data are automatically represented at smaller scales than the original data were intended for. Fortunately, the SLD rules can be decoded, the symbolization rules translated into geometrical features, and cartographic conflicts detected and partially solved. The conflicting features can be identified based on the use of few basic geospatial analysis functions. After a solution that minimizes these conflicts emerges, new SLD rules are generated that attempt to visually solve the cartographic conflicts. The new SLD rules can then be applied on-demand by a cartographic proxy server that rewrites the incoming GetMap requests to use the new SLD rules. The process for improving the WMS cartographic output has several stages, grouped into preparatory steps (basic automatic generalization methods, rough scale-dependent SLD symbolization) and real-time processing steps (detection of cartographic conflicts, conflicts solution and generation of new SLD rules). The entire process of detecting and solving cartographic conflicts in the maps produced by WMS through the use of overriding SLD rules is described in detail. Furthermore, it is conceivable to transform the conflicting features into spatial objects containing methods for discovering appropriate SLD values that minimize conflicts. Such approaches can bring the performance of automatic detection and correction of cartographic conflicts above the threshold required for interactive visualization, thus, making the process of dynamically solving cartographic conflicts in WMS servers a viable solution.

A systematic view on data descriptors for the visual analysis of tabular data

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.

Modelling the functional capacity losses of networks exposed to hazards

Abstract The quantification of probable network-related consequences resulting from the occurrence of (natural) hazard events supports network managers in determining the most suitable interventions to execute. This assessment should include the modelling of consequences related to the use of the network and the levels of service. A method is presented to quantify the temporal functional capacity losses of the individual objects of which networks are comprised due to hazard events to ultimately support the estimation of consequences at a network level. The method is designed to handle the effects of (i) time-varying hazards, (ii) multiple hazards, (iii) functional capacity losses that are independent of (structural) physical capacity losses and (iv) functional capacity losses that demand preventive interventions. An example demonstrates how the functional capacity losses of objects in a road network change over time as rainfall-triggered flood and mudflow events, inspection events and restoration events occur.

Stress tests for a road network using fragility functions and functional capacity loss functions

A quantitative approach to conduct a specific type of stress test on road networks is presented in this article. The objective is to help network managers determine whether their networks would perform adequately during and after the occurrence of hazard events. Conducting a stress test requires (i) modifying an existing risk model (i.e., a model to estimate the probable consequences of hazard events) by representing at least one uncertainty in the model with values that are considerably worse than median or mean values, and (ii) developing criteria to conclude if the network has an adequate post-hazard performance. Specifically, the stress test conducted in this work is focused on the uncertain behavior of individual objects that are part of a network when these are subjected to hazard loads. Here, the relationships between object behavior and hazard load are modeled using fragility functions and functional capacity loss functions. To illustrate the quantitative approach, a stress test is conducted for an example road network in Switzerland, which is affected by floods and rainfall-triggered mudflows. Beyond the focus of the stress test, this work highlights the importance of using a probabilistic approach when conducting stress tests for temporal and spatially distributed networks.

A process for the assessment of infrastructure related risk due to natural hazards

The determination of network related risks for transport infrastructure systems, such as road or railway networks, is a challenging task. Due to such complex systems, it is generally impossible to abstract the global behavior from the analysis of single components, especially under conditions such as failures or damages. People who manage infrastructure have to handle these risks. The proposed overarching risk assessment process is constructed in a way so that computational support can be constructed in modules. This allows to couple the process with detailed sub-processes to achieve varying levels of detail in the risk assessment. The use of the overarching risk assessment process is demonstrated by using it to evaluate infrastructure related risk due to natural hazards for an example region in Switzerland.