Chapter 1 An overview of spatio-temporal epidemiology and knowledge discovery?

This book provides a comprehensive treatment of methods for spatio-temporal modelling and their use in epidemiological studies. Throughout the book, we present examples of spatio-temporal modelling within a Bayesian framework and describe how data can be used to update knowledge, together with in-depth consideration of the role of uncertainty.

Throughout the book we will use the R statistical software. R is an object-orientated programming language and provides an integrated suite of software facilities for data manipulation, calculation and graphical display. It provides an environment within which almost all classical and modern statistical techniques are available. R can be used in a variety of ways, including directly from the command line or from within other programming environments. For many of the examples in this book we use , an integrated development environment (IDE) for R. It includes a console, syntax-highlighting editor that supports direct code execution, as well as tools for plotting, history, debugging and workspace management. Both R (cran.r-project.org) and RStudio are open source and can be downloaded free of charge. They are both available for Windows, Mac OSX and Linux.

The base R environment provides a rich set of functions with many thousands of functions with many more provided as additional packages, including those which allow the manipulation and visualization of spatio-temporal data, performing epidemiological analysis and advanced spatio-temporal analyses. These are available at The Comprehensive R Archive Network (CRAN) and can be downloaded from cran.r-project.org, a repository for R software. Additional packages need to be downloaded and installed.

From this book, the reader will have gained an understanding of the following topics:

  • The basic concepts of epidemiology and the estimation of risks associated with environmental hazards;

  • The theory of spatial, temporal and spatio-temporal processes needed for environmental health risk analysis;

  • Fundamental questions related to the nature and role of uncertainty in environmental epidemiology, and methods that may help answer those questions;

  • How data can be used to update knowledge and reducing uncertainty;

  • A history of data collection and how databases may be constructed and how data is formally represented through the sample space and associated formal constructs such as the sample space and sampling distributions;

  • Important areas of application within environmental epidemiology, together with strategies for building the models that are needed and coping with challenges that arise;

  • Computational methods for the analysis of complex data measured over both space and time and how they can be implemented using R;

  • Areas of current and future research directions in spatio-temporal modelling;

  • Examples of R code are given throughout the book and the code, together with data for the examples, are available online on the book’s GitHub site;

  • The book contains a variety of exercises, both theoretical and practical, to assist in the development of the skills needed to perform spatio-temporal analyses.