Modeling spatial and spatio-temporal continuous processes is an important and challenging problem in spatial statistics. Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA describes in detail the stochastic partial differential equations (SPDE) approach for modeling continuous spatial processes with a Matérn covariance, which has been implemented using the integrated nested Laplace approximation (INLA) in the R-INLA package. Key concepts about modeling spatial processes and the SPDE approach are explained with examples using simulated data and real applications.
This book has been authored by leading experts in spatial statistics, including the main developers of the INLA and SPDE methodologies and the R-INLA package. It also includes a wide range of applications:
* Spatial and spatio-temporal models for continuous outcomes
* Analysis of spatial and spatio-temporal point patterns
* Coregionalization spatial and spatio-temporal models
* Measurement error spatial models
* Modeling preferential sampling
* Spatial and spatio-temporal models with physical barriers
* Survival analysis with spatial effects
* Dynamic space-time regression
* Spatial and spatio-temporal models for extremes
* Hurdle models with spatial effects
* Penalized Complexity priors for spatial models
All the examples in the book are fully reproducible. Further information about this book, as well as the R code and datasets used, is available from the book website at http://www.r-inla.org/spde-book.
The tools described in this book will be useful to researchers in many fields such as biostatistics, spatial statistics, environmental sciences, epidemiology, ecology and others. Graduate and Ph.D. students will also find this book and associated files a valuable resource to learn INLA and the SPDE approach for spatial modeling.
1. The Integrated Nested Laplace Approximation. 2. Continuous spatial processes. 3. Non-Gaussian observations and covariates in the covariance. 4. Manipulating the random field and more than one likelihood. 5. Log-Cox point process and preferential sampling. 6. Space-time models. 7. Space-time models with different meshes.
"Besides the epidemiological perspective, they have also tried to address many of the applied issues in disease mapping practice, which may make this book different from others previously published on this topic. To further facilitate understanding, the authors have made all code and data used in their examples available. A GitHub repository has also been created for hosting the book’s online supplementary material. Thus, one can make use of the functionalities that GitHub deploys for their repositories, such as highlighting points to be clarified by the authors. As a great novelty of the book, the online material may enable readers to have direct access to most of the statistical/computing details that there is not enough room to fully explain within the book." ~Sada Nand Dwivedi, ICSB News