The world is becoming increasingly complex, with larger quantities of data available to be analyzed. It so happens that much of these "big data" that are available are spatio-temporal in nature, meaning that they can be indexed by their spatial locations and time stamps. Spatio-Temporal Statistics with R provides an accessible introduction to statistical analysis of spatio-temporal data, with hands-on applications of the statistical methods using R Labs found at the end of each chapter. The book: Gives a step-by-step approach to analyzing spatio-temporal data, starting with visualization, then statistical modelling, with an emphasis on hierarchical statistical models and basis function expansions, and finishing with model evaluation - Provides a gradual entry to the methodological aspects of spatio-temporal statistics - Provides broad coverage of using R as well as "R Tips" throughout. - Features detailed examples and applications in end-of-chapter Labs - Features "Technical Notes" throughout to provide additional technical detail where relevant - Supplemented by a website featuring the associated R package, data, reviews, errata, a discussion forum, and more The book fills a void in the literature and available software, providing a bridge for students and researchers alike who wish to learn the basics of spatio-temporal statistics. It is written in an informal style and functions as a down-to-earth introduction to the subject. Any reader familiar with calculus-based probability and statistics, and who is comfortable with basic matrix-algebra representations of statistical models, would find this book easy to follow. The goal is to give as many people as possible the tools and confidence to analyze spatio-temporal data. “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 b