Spring 2020
Instructor: Professor Steven E. Rigdon
Introduction: This course covers models for spatial data, that is, data that are geographically coded. Specific attention is given to disease rates which may vary across regions
Purpose: Students should learn to apply the appropriate statistical method to data sets that include geographic information.
Description: Statistical methods for disease data that include geographic information. Methods include spatial scan statistics, kriging, measures of autocorrelation, Moran's I, regression with exposure data and covariates. Disease maps and relative risk estimation. Mapping and geographic information systems. Bayesian methods of estimation for conditional autoregressive models.
Textbooks：
- Chris Brundson & Lex Comber (2019) Introduction to R for Spatial Analysis and Mapping, Second Edition. Sage. ISBN: 978-1526428509.
- Andrew Lawson (2018) Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Third Edition. CRC. ISBN: 978-138-57542-4.