Short Course Description
The course will provide an introduction to the analysis of spatial and spatio-temporal data with the R statistical software. First of all, the course will focus on the different packages to import, handle and visualise geographical data in R. Secondly, Bayesian model building with the R-INLA package will also be discussed to analyse different types of data. It will start with the analysis of lattice data. Next, geostatistical models will be addressed with a focus on Stochastic Partial Differential Equation (SPDE). Finally, the analysis of point patterns with INLA will be described.
The course will include hands-on practicals on all the topics covered in the course. Practicals will be carried out in a computer lab, but attendants can
also bring their own computers. Practicals will be done with the R software and will be based on the analysis of real datasets with R and R-INLA.
All course materials (slides, R code and datasets) will be available on-line so that attendants can reproduce the examples by themselves.
The course will include hands-on practicals on all the topics covered in the course. Practicals will be carried out in a computer lab, but attendants can
also bring their own computers. Practicals will be done with the R software and will be based on the analysis of real datasets with R and R-INLA.
All course materials (slides, R code and datasets) will be available on-line so that attendants can reproduce the examples by themselves.
Reference & Software
- Roger S. Bivand, Edzer J. Pebesma and Virgilio Gómez-Rubio. (2013) Applied Spatial Data Analysis with R. Springer.
- Blangiardo, M and Cameletti, M. (2015) Spatial and Spatio-temporal Bayesian Models with R–INLA. Wiley.
- Rue H., Martino S., Chopin N. (2009). Approximate Bayesian inference for latent Gaussian models using integrated nested Laplace approximations (with discussion). Journal of the Royal Statistical Society, Series B 71(2), 319–392.
Lecturer Background Information
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Virgilio Gómez-Rubio is Associate Professor in the Department of Mathematics, Universidad de Castilla-La Mancha (UCLM) in Spain. Prior to joining UCLM, he was Research Associate at the Department of Epidemiology and Biostatistics, Imperial College London (U.K.).
Dr. Gómez-Rubio has developed and contributed to a number of packages for the R software on spatial data analysis and Bayesian inference. He is also co-author of Springer’s bestselling book ‘Applied Spatial Data Analysis with R’. He has given courses on spatial data analysis and small area estimation at international conferences and universities worldwide. Currently, his main research interests are in Bayesian inference, spatial statistics and computational statistics. He is leading a project on the analysis of multivariate data for disease mapping to develop novel models, computational tools and software for Bayesian inference of spatio-temporal models. He is also involved in a project with the VABAR research group at Univesitat de València (Spain) on the analysis of highly correlated data, where he is developing models for the analysis of spatio-temporal data. |