Spatial and Spatio-Temporal Bayesian Models with R

This page contains the material related to the book "Spatial and Spatio-Temporal Bayesian Models with R" published by Wiley (see here) in 2015.

Book description

The Bayesian approach is particularly effective at modelling large datasets including spatial and temporal information due to its flexibility and ease with which it can formally include correlation and hierarchical structures in the data. However, classical simulation methods such as Markov Chain Monte Carlo can become computationally unfeasible; this book presents the Integrated Nested Laplace Approximations (INLA) approach as a computationally effective and extremely powerful alternative.

Spatial and Spatio-temporal Bayesian models with R-INLA introduces the basic paradigms of the Bayesian approach and describes the associated computational issues. Detailing the theory behind the INLA approach and the R-INLA package, it focuses on spatial and spatio-temporal modeling for area and point-referenced data.

The combination of detailed theory and practical data analysis is beneficial for readers at any level. The coding of all the examples in R-INLA and the availability of all the datasets used throughout the book on the INLA website (www.r-inla.org) make an appealing feature for applied researchers wanting to approach or increase their knowledge and practice of the INLA method.

Contacts

• Marta Blangiardo

  • MRC-PHE Centre for Environment and Health, Dept. of Epidemiology and Biostatistics, Imperial College London, UK
  • email: m.blangiardo@imperial.ac.uk

• Michela Cameletti

  • Dept. of Economics, University of Bergamo, Bergamo, Italy
  • email: michela.cameletti@unibg.it

R Code

Click here to download the R code for reproducing the examples contained in the book.

Datasets

• National morbidity, mortality and air pollution study
  • This dataset is described in Section 1.4.1 and used in Section 5.1.2. Click here to get the data.
• Average income in Swedish municipalities
  • This dataset is described in Section 1.4.2 and used in Section 5.2.1 Click here to get the data.
• Stroke in Sheffield
  • This dataset is described in Section 1.4.3 and used in Section 2.3, 5.3, 5.4.6 and 5.5. Click here to get the data.
• Ship accidents
  • This dataset is described in Section 1.4.4 and used in Section 5.3. Click here to get the data.
• CD4 in HIV patients
  • This dataset is described in Section 1.4.5 and used in Section 5.4.4, 5.6.1 and 5.6.2. Click here to get the data.
• Lip cancer in Scotland
  • This dataset is described in Section 1.4.6 and used in Section 5.4.5 and 5.6.1. Click here to get the data.
• Suicides in London
  • This dataset is described in Section 1.4.7 and used in Section 6.1.2 and 6.2. Click here to get the data.
• Brain cancer in Navarra, Spain
  • This dataset is described in Section 1.4.8 and used in Section 6.3.1. Click here to get the data.
• Respiratory hospital admission in Turin province, Italy
  • This dataset is described in Section 1.4.9 and used in Section 6.3.2. Click here to get the data.
• Lung cancer mortality in Ohio
  • This dataset is described in Section 1.4.12 and used in Section 7.1. Click here to get the data.
• Low birth weight births in Georgia
  • This dataset is described in Section 1.4.13 and used in Section 7.1.1 and 7.1.2. Click here to get the data.
• Air pollution in Piemonte
  • This dataset is described in Section 1.4.14 and used in Section 7.2. Click here to get the data.

Errata

Click here to download the errata file.