Abstract:

Human geography is a concept used to indicate the augmentation of standard geographic layers of information about an area with behavioral variations of the people in the area. In particular, the actions of people can be attributed to both local and regional variations in physical (i.e., terrain) and human (e.g., income, political, cultural) variables. In this paper, we study the utility of a human geographic data cube coupled with computational intelligence as a means to predict conditions across a geographic area. This becomes a Big data problem. In this sense, we are using genotype information to predict phenotype states. We demonstrate the approach on the prediction of medically underserved areas in Missouri.

Links:

Citation:

J. Keller, A. Buck, A. Zare, and M. Popescu, “A human geospatial predictive analytics framework with application to finding medically underserved areas,” in IEEE Symp. Computational Intelligence in Big Data (CIBD), 2014.

@InProceedings{keller2014human,
Title = {A human geospatial predictive analytics framework with application to finding medically underserved areas},
Author = {James Keller and Andrew Buck and Alina Zare and Mihail Popescu},
Booktitle = {IEEE Symp. Computational Intelligence in Big Data (CIBD)},
Year = {2014},
Month = {Dec.},
Doi = {10.1109/CIBD.2014.7011525},
}