This paper presents an endmember estimation and representation approach for human geography data cubes. Human-related factors that can be mapped for a geographic region include factors relating to population, age, religion, education, medical access and others. Given these hundreds (or even thousands) of factors mapped over a region, it is extremely difficult for an analyst to summarize and understand the interactions between all of these factors. In this paper, a method to provide a compact representation and visualization of hundreds of human geography layers is presented. These are large data cubes containing a range of human geographic information including some represented using fuzzy values. Results on a human geography data cube compiled for the state of Missouri, USA is presented.