  Analysis of spatial multivariate data, i.e., measurements at
irregularly-spaced locations, is a challenging topic in visualization and
statistics alike. Such data are integral to many domains, e.g., indicators of
valuable minerals are measured for mine prospecting. Popular analysis methods,
like PCA, often by design do not account for the spatial nature of the data.
Thus they, together with their spatial variants, must be employed very
carefully. Clearly, it is preferable to use methods that were specifically
designed for such data, like spatial blind source separation (SBSS). However,
SBSS requires two tuning parameters, which are themselves complex spatial
objects. Setting these parameters involves navigating two large and
interdependent parameter spaces, while also taking into account prior knowledge
of the physical reality represented by the data. To support analysts in this
process, we developed a visual analytics prototype. We evaluated it with
experts in visualization, SBSS, and geochemistry. Our evaluations show that our
interactive prototype allows to define complex and realistic parameter settings
efficiently, which was so far impractical. Settings identified by a non-expert
led to remarkable and surprising insights for a domain expert. Therefore, this
paper presents important first steps to enable the use of a promising analysis
method for spatial multivariate data.
