Data Type Agnostic Visual Sensitivity Analysis

TL;DR

This paper introduces a visual sensitivity analysis approach that is data type agnostic, enabling better understanding of spatial data models like SBSS by using dissimilarity measures, and demonstrates its transferability to microclimate simulations.

Contribution

We developed a novel visual sensitivity analysis method that requires only dissimilarity measures, making it applicable to complex spatial data models like SBSS and beyond.

Findings

Participants confirmed known parameter-output relations.

Participants identified surprising associations.

The approach is transferable to microclimate simulations.

Abstract

Modern science and industry rely on computational models for simulation, prediction, and data analysis. Spatial blind source separation (SBSS) is a model used to analyze spatial data. Designed explicitly for spatial data analysis, it is superior to popular non-spatial methods, like PCA. However, a challenge to its practical use is setting two complex tuning parameters, which requires parameter space analysis. In this paper, we focus on sensitivity analysis (SA). SBSS parameters and outputs are spatial data, which makes SA difficult as few SA approaches in the literature assume such complex data on both sides of the model. Based on the requirements in our design study with statistics experts, we developed a visual analytics prototype for data type agnostic visual sensitivity analysis that fits SBSS and other contexts. The main advantage of our approach is that it requires only dissimilarity measures for parameter settings and outputs. We evaluated the prototype heuristically with visualization experts and through interviews with two SBSS experts. In addition, we show the transferability of our approach by applying it to microclimate simulations. Study participants could confirm suspected and known parameter-output relations, find surprising associations, and identify parameter subspaces to examine in the future. During our design study and evaluation, we identified challenging future research opportunities.