Date of Award

Summer 2023

Document Type

Open Access Thesis



First Advisor

Stella Self


Scan statistics are useful methods for detecting spatial clustering. While they were initially developed to detect regions with an excess of binomial or Poisson events, spatial scan statistics have been extended to detect hotspots in other types of data including continuous data. They have many applications in different fields such as epidemiology (e.g. detecting disease outbreaks), sociology (e.g. detecting crime hotspots), and environmental health (e.g. detecting high-pollution areas). Spatial scan statistics identify a ‘most likely cluster’ and then use a likelihood ratio test to determine if this cluster is statistically significant. Spatial scan statistics have been extended to the Bayesian paradigm for different types of data such as zero-inflated count data and multivariate count data. Bayesian spatial scan statistics generally exhibit higher power and reduced computational cost than their frequentist counterparts. In this work, we develop a Bayesian spatial scan statistic for normal data. We conduct a simulation study to evaluate the performance of our method under varying sample sizes, cluster sizes, and observation means. We examine the number of times we reject the null hypothesis using the Bayes factor as well as the overall sensitivity and positive predictive value of our test.

Included in

Biostatistics Commons