Abstract:

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.

Links:

Citation:

A. Buck, A. Zare, J. Keller, and M. Popescu, “Endmember representation of human geography layers,” in IEEE Symp. Computational Intelligence in Big Data (CIBD), 2014. 

@InProceedings{buck2014endmember,
Title = {Endmember representation of human geography layers},
Author = {Andrew Buck and Alina Zare and James Keller and Mihail Popescu},
Booktitle = {IEEE Symp. Computational Intelligence in Big Data (CIBD)},
Year = {2014},
Month = {Dec.},
Doi = {10.1109/CIBD.2014.7011520},
}