“This extremely useful book contains extensive R code and hands-on "Lab" sections at the end of each chapter that walk you through data processing and implementation. This is exactly what an applied statistics book needs to be relevant, allowing the reader to immediately start analyzing data and interrogating output. The book focuses on the Bayesian hierarchical perspective and applications in the geophysical sciences. The authors do a great service emphasizing the inferential point of view (e.g., characterizing uncertainty for model parameters and forecasting) providing a distinct contrast with other current paradigms such as Deep Learning. The structure is concise and logical with a nice progression from exploration and visualization, space-time regression, descriptive models (e.g., kriging) and then dynamic space-time models, with an emphasis throughout on dimension reduction and basis function perspectives which is timely and increasing in importance.”—J. Andrew Royle, Senior Scientist, USGS Patuxent Wildlife Research Center"This book is a comprehensive and very readable tutorial on modelling and visualizing spatio-temporal (ST) processes. It emphasises the need to understand an ST process before attempting to model it. Along the way, the reader learns the descriptive phase of exploratory analysis and moves on to the dynamic modelling of ST processes. Only then does the reader move onto the rich libraries of R tools available for the model construction. In the final phase the reader learns how to assess the models he or she has created with the goal of improving them and ultimately choosing the best one. All this is accomplished using a hands-on approach through lab work that involves complex datasets and the very large library of R packages now available. Thus, the reader will learn amongst many other things, how to animate their spatial plots of data and the use of Trelliscope for visualizing large ST datasets. For data wrangling, the reader learns about the dyplr and tidyr R packages. And the reader will master a lot of the skills needed for spatial regression with generalized linear models, Bayesian hierarchical modelling, using the integrated nested Laplace (INLA) approximation, spatial prediction and future forecasting. Of particular note is the connections the book develops with stochastic partial differential equations and uncertainty quantification, that are developed through discussion of dynamic modelling. This book will have a prominent place in my reference library."—James V. Zidek, Professor Emeritus, University of British Columbia"This book provides the ideal modern approach to the analysis of spatial-temporal data and implementation of associated models. The theory is laid out clearly by masters of the field and the accompanying R code, packages, and data laboratories both in the text and available online bring the subject to life. This is not a book to sit on your shelf -- it should be on your desk for ready access and continual use."—Marc Mangel, University of California, Santa Cruz and University of Bergen"Spatio-Temporal Statistics with R is the perfect companion to the earlier title by the authors on Statistics for Spatio-Temporal Data. This newest book augments the reader’s skillset by showing how to implement a variety of methods to create spatio-temporal graphics and perform data analysis. By making a massive set of data and code available, this book encourages the reader to follow along on the computer while working through the chapters. In fact, a unique element of the authors’ approach is that they provide a solid review of existing software and complement that with a new software package so that no techniques fall through the cracks. I also particularly like the series of text boxes throughout the book that detail expert tips for computing and include technical comments for more advanced readers. It is this masterful blend of information that beginners and power users alike will find critical for enhancing their understanding of spatio-temporal statistics in practice. This book will be recommended reading for all of my future graduate students!" —Mevin B. Hooten, Professor, Colorado State University and U.S. Geological Survey"This book is an excellent offering from some of the leading researchers and authors in the field of spatio-temporal statistics. The book will be especially useful for scientists and researchers seeking a hands-on approach to statistical modeling and analysis for spatio-temporal data. The text is organized beautifully and offers a pleasing blend of technical material and computer programs for implementing a variety of spatio-temporal models. What is especially attractive is the detail with which the computer programs have been explained and exemplified. The theoretical and more technical material are supplied as "Technical Tips" in conspicuous boxes that accompany the modeling and computing details. The book will be useful to methodologists and practitioners working on spatio-temporal analysis and will especially appeal to the broader scientific community who will enjoy the very accessible treatment of spatial-temporal modeling and computing in an open-source and highly accessible software environment."—Sudipto Banerjee, Professor and Chair, Department of Biostatistics, University of California Los Angeles"To help statisticians understand and apply appropriate statistical methods, the authors provide this highly interesting book, intended as an introduction to spatio-temporal statistics with R. The book is divided into six chapters, each of them standing alone and in a form such that they can be read separately. The first chapter is an introduction to spatio-temporal analysis. It is useful because it introduces the reader to the definitions of the various concepts used in this branch of statistical analysis and explains the fields of application of spatio-temporal analyses. One of the main qualities of this chapter is to give a clear explanation of the differences between spatial analyses, temporal analyses, and spatio-temporal analyses... the authors explain and develop several methods used to explore spatio-temporal data, such as Hovmöller plots and the Trelliscope method. Moreover, the authors provide readers with a solid basis for one of the major challenges with such data: data exploration... To conclude, this is a very clear introduction to spatio-temporal analysis using R, it is thorough and is suitable for readers of different levels. The possibility to experiment with the topics introduced in each chapter with a dedicated R ‘Lab’ is a good way of learning to apply these models. Moreover, appendices are provided to further extend the development of the topics covered in the different chapters."- Sébastien Bailly, French Institute of Health and Medical Research, Appeared in ISCB News, January 2020
