Spatial Analysis of Public Health Data

Much of the data encountered in public health studies has a spatial dimension. The aim is to cover statistical approaches that can be used to make inferences and predictions from spatial data of the kind encountered in epidemiological analyses.

We will introduce different types of spatial data, such as point-level and areal data, and understand that these data types require different analytical approaches. We will understand the need to apply statistical approaches that account for spatial dependence, and cover a range of methods of increasing complexity, such as spatial regression, geostatistical models for point level data and conditional autoregressive models for areal data. The students will need a statistical background and familiarity with linear regression techniques and familiarity with programming in R.