Date of Award
August 2020
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Mathematical Sciences
Committee Member
Deborah Kunkel
Committee Member
Peter Kiessler
Committee Member
Xiaoqian Sun
Abstract
Amateur radio spots are studied by scientists for many reasons. The Reverse Beacon Network (RBN) records thousands of spots and their characteristics on a daily basis. Located at the public server, http://www.reversebeacon.net/, it is open to be downloaded and explored by all. A "spot" is by definition where a propagation path exists between a transmitter and a receptor location at a certain time and frequency (Miller et al., 2019). While this data can be useful to scientists, we do not have any knowledge to know when or how spots will occur. In this paper, we explore the idea of using the data for prediction. We start with the general question: Given input explanatory variables, what is the probability of a spot from a certain transmitter to a certain receptor? We begin with exploratory data analysis to find patterns or characteristics which may help with our choice of explanatory variables. Then, we research different statistical models and implement one which we deem most appropriate.
Recommended Citation
Chris, Nicole, "Exploratory Data Analysis and Point Process Modeling of Amateur Radio Spots" (2020). All Theses. 3418.
https://tigerprints.clemson.edu/all_theses/3418