A Practical Introduction to Hierarchical Modelling for Spatially Referenced Data

A Practical Introduction to Hierarchical Modelling for Spatially Referenced Data

Sudipto Banerjee, School of Public Health, University of Minnesota

 

SSC 2011 Annual Meeting

June 12, 2011
Wolfville, NS

 

Abstract

New methods for recording the locations of health data due to recent advances in Geographical Information Systems (GIS) and Global Positioning Systems (GPS) have permitted new types of disease mapping and spatial modelling of health data, as well as new approaches to support disease prevention and control activities in public health. This has generated considerable interest in statistical modelling for location-referenced (point-level or geostatistical) data and areal (aggregated over regions) data. This course offers an introduction to the methods for modelling and carrying out inference with spatial point-level and areal data. Basic elements such as classical approaches in geostatistics, spatial disease mapping, and Bayesian inference for spatial data will be covered in detail. Each topic will include theory, examples and data analysis along with live interactive computing demonstrations. The course will also detail recent advancements in Bayesian hierarchical models for spatial data using Markov chain Monte Carlo (MCMC) methods.

Specific topics that will be covered include: geostatistical modelling, spatial risk assessment, disease mapping, CAR models for areal data, spatial linear regression, generalized linear models, uncertainty analysis, diagnostics and validation for spatial models, and spatial Bayesian inference.

We will offer a hands-on opportunity to explore the use of WinBUGS, the leading Bayesian software package, as well as several spatial packages in R for spatial geocoded and areal data. The computing demonstrations will encompass exploratory spatial data analysis as well as estimation of statistical models with practical data sets in public health and the environmental sciences.