5.1 Introduction

This chapter is about Aereal data. We will be looking at some real world examples provided by the {SpatialEpi} package, and do some math for measuring disease risks before building the model.

Examples of Areal data are:

• the number of cancer cases in counties
• the number of road accidents in provinces
• the proportion of people living in poverty in census tracts.

Measure of disease risk in areas:

• standardized incidence ratio (SIR) which is defined as the ratio of the observed to the expected counts
• Bayesian hierarchical models

Spatial model: Besag-York-Mollié (BYM) model

• autocorrelation in temporal and spatial structure
• spatial random effects

Data used: lung cancer in Pennsylvania counties, USA, from the {SpatialEpi} package