Motivation and Goal: Only recently have large compilations of typological data been homogenized and made freely available to the public. Such information usually comes in the form of a matrix containing some four to six hundred categorical (i.e. multinomial) linguistic features for several hundred global languages (i.e. large-p/small-n, with n actually not being so small). One of the overarching questions that might be answered with such data is: What is the global relatedness of languages and which historic linguistic theory does it support? However, the challenges in using the data for this purpose are, for instance, its categorical character, its large-p, its uneven spatial distribution, and the many NA values, etc. For this reason, we introduced a procedure that reduces dimensionality while still reflecting the impact of individual linguistic features and NA values in particular. Additionally, our approach accounts for the spatial character of the data and thus combines dimension reduction with spatial analysis.