Date of Award

8-2014

Degree Type

Thesis

Degree Name

Master of Science

Major

Geography

Major Professor

Robert N. Stewart

Committee Members

Nicholas N. Nagle, Ronald Foresta

Abstract

Understanding specific multi-dimensional demographics of populations in the United States at high resolutions is made difficult by the restriction of data released by the Census Bureau because of privacy concerns. Efforts to model these subpopulations have been increasing in recent years. These modeled populations have applications in decision making at all levels of government as well as in academia and the private sector. Two models have shown promising techniques for incorporating multiple levels of data to model sub populations in a meaningful way. These models, the Copula Model by Kao et al. (2012) and the Penalized Maximum Entropy Model by Nagle et al. (2014), have been applied in different study areas using different attributes. This paper provides a direct comparison which is needed to understand the strengths and weakness of each model as well as to assess the possibility of expanding their application nationally.

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