Bayesian Modeling of Spatio-Temporal Data with R

Applied sciences gather large volumes of spatial and spatio-temporal data to make inference and prediction. Unified Bayesian modeling is a key to unlocking the full power of these methods for solving practical problems. This book provides a detailed discussion of Bayesian methods and computations with worked examples, numerical illustrations, and exercises. It also includes R code notes and practical examples of spatio-temporal modeling of point referenced and areal unit data. The emphasis is on validating models by splitting data into test and training sets.

Format: Hardback
Length: 411 pages
Publication date: 02 March 2022
Publisher: Taylor & Francis Ltd

Applied sciences, encompassing both physical and social domains, such as atmospheric, biological, climate, demographic, economic, ecological, environmental, oceanic, and political sciences, routinely collect vast volumes of spatial and spatio-temporal data to facilitate comprehensive inference and prediction. Ideally, these inferential tasks should be approached through modeling, which aids in estimating uncertainties in all conclusions drawn from such data. Unified Bayesian modeling, implemented through user-friendly software packages, holds the key to unlocking the full potential of these methods for solving challenging practical problems.

Key features of the book:
• Accessible and detailed discussion of various aspects of Bayesian methods and computations, with worked examples, numerical illustrations, and exercises.
• A jargon-buster chapter on spatial statistics that enables readers to build a vocabulary without getting overwhelmed by modeling and technicalities.
• Computation and modeling illustrations are provided with the help of the dedicated R package bmstdr, allowing readers to utilize well-known packages and platforms, such as rstan, INLA, spBayes, spTimer, spTDyn, CARBayes, CARBayesST, and more.
• R code notes detailing the algorithms used to produce all the tables and figures are included, with data and code available via an online supplement.
• Two dedicated chapters discuss practical examples of spatio-temporal modeling of point-referenced and areal unit data.
• Throughout, the emphasis has been on validating models by splitting data into test and training sets, following the philosophy of machine learning and data science.

This book is designed to make spatio-temporal modeling and analysis accessible and understandable to a wide audience of students and researchers, including mathematicians, geographers, environmental scientists, and social scientists. By leveraging the power of Bayesian methods and computational tools, readers will gain the skills and knowledge necessary to analyze and interpret spatial and spatio-temporal data, leading to informed decision-making and better understanding of complex systems.

Weight: 830g
Dimension: 160 x 241 x 28 (mm)
ISBN-13: 9780367277987