  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.
