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
Recommended Citation
Dhol, Brindal, "A Data Visualization Framework for ESDA: Understanding Pre-Diabetes and Diabetes Prevalence in Florida." Thesis, Georgia State University, 2022.
doi: https://doi.org/10.57709/28995960
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