Computational models of deep brain stimulation (DBS) have played a key role in investigating the mechanisms of action of DBS therapies. By estimating a volume of tissue directly modulated by DBS, one can relate the pathways within those volumes to the therapeutic efficacy of a particular DBS setting. With the advent of higher-density DBS electrode arrays, there is a growing need for a systematic method to quantify the morphology of the modulated volumes within the brain. In this study, we applied the tools of spherical statistics to quantify such morphologies through the application of a computational model of a directionally segmented DBS array. The same statistical techniques have broad applications to characterizing distributions of in-vivo electrophysiological recordings and histological labeling of neurons.