Date of Award

Spring 5-4-2022

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Dr. Anu Bourgeois

Second Advisor

Dr. Chetan Tiwari

Third Advisor

Dr. Suhasini Ramisetty-Mikler

Abstract

Exploratory spatial data analysis (ESDA) is a technique for analyzing data from different geographic regions. To examine patterns, ESDA uses univariate and multivariate graphical approaches. Through a case study of diabetes and pre-diabetes prevalence in Florida, we built a novel data visualization framework for ESDA.

Diabetes is a rapidly increasing global disease that is a major global health concern with significant implications for healthcare spending. Information about the relationship between diabetes and geographical sociodemographic characteristics could assist public health programs better targeting those who are at risk. We show the regional prevalence of disease in Florida and its relationship to the geography of risk variables using our multivariate data visualization framework.

Our methodology can be applied to wide range of problems and domains that require complex analysis of disparate data to identify correlations. The method can be used to find patterns and clusters for any problem at any spatial scale.

DOI

https://doi.org/10.57709/28995960

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