Visual Parameter Space Exploration in Time and Space

Abstract

Computational models, such as simulations, are central to a wide range of fields in science and industry. Those models take input parameters and produce some output. To fully exploit their utility, relations between parameters and outputs must be understood. These include, for example, which parameter setting produces the best result (optimization) or which ranges of parameter settings produce a wide variety of results (sensitivity). Such tasks are often difficult to achieve for various reasons, for example, the size of the parameter space, and supported with visual analytics. In this paper, we survey visual parameter space exploration (VPSE) systems involving spatial and temporal data. We focus on interactive visualizations and user interfaces. Through thematic analysis of the surveyed papers, we identify common workflow steps and approaches to support them. We also identify topics for future work that will help enable VPSE on a greater variety of computational models.

Data Type Agnostic Visual Sensitivity Analysis

“…Sedlmair et al [59] provided a common data flow model and a task taxonomy, such as optimization, uncertainty, or SA. Piccolotto et al [50] surveyed user interfaces and visualizations that support visual parameter space exploration. Several examples of VPA for multivariate parameters can be found in the literature [5,15,22,32,48,72].…”

“…However, these approaches do not apply to SBSS parameters. Many approaches have been used when it comes to visualizing parameter-output relations [50]. When parameters are multivariate, visualizations that show correlations and trends can be used to carry out SA, such as histograms, scatterplots, or PCPs [4,14,66].…”

“…We anticipate our user base to eventually include domain experts, e.g., from geochemistry. We conducted an extensive literature review [50] to understand how visual VPA and, consequently, visual SA work in other contexts. Based on that, we distilled generic SA subtasks to enable SA on the SBSS-specific complex parameters with our clustering-based approach (T1-T5).…”

“…SBSS is interesting for the visualization community primarily because of the mentioned affordances of its parameters and outputs: Parameter settings and outputs are spatial objects or otherwise complex in a way that a multivariate representation does not do them justice. While the literature contains many examples of visual parameter space exploration [50,59], to the best of our knowledge, none of them support complex parameters and outputs without resorting to multivariate representation or feature derivation (Section 2). However, these requirements are not specific to SBSS, as many examples exist for models with complex parameters and outputs.…”