Book Reviews

A Gentle Introduction to Spatio-Temporal Statistics

The dynamic nature of our world is being increasingly captured by sensors as big data. Much of this big data can be indexed by its spatial location and time stamp. Spatio-Temporal Statistics with R, by Christopher K. Wikle, Andrew Zammit-Mangion, and Noel Cressie, provides an accessible introduction to analyzing spatio-temporal data using the programming language R.

The format for each of its stand-alone chapters is an explanation of methodology followed by hands-on application of relatively mature models and methodologies. R was chosen because it is open source, has strong community support, and has many packages that can be used for spatio-temporal modeling.

This book assumes that readers have a background in calculus-based probability and inference and are comfortable with basic matrix algebra representations of statistical models. However, to support those with a less robust background in these subjects, technical notes throughout the book provide short reviews of these methods and ideas, and the appendix contains a brief refresher on matrix algebra. The exercises in this book assume prior knowledge of R and familiarity with Tidyverse, a collection of R packages designed for data science. To further aid with the exercises, helpful R tips have been included.

Spatio-Temporal Statistics with R is a down-to-earth and engaging introduction to the topic, rather than a comprehensive book on the subject. More advanced and technically trained readers may wish to look at a previous book on the same subject by authors Wikle and Cressie, Statistics for Spatio-Temporal Data; however, this book does not include software or coding examples.

Since increasing the number of people who are analyzing spatio-temporal data was a fundamental goal of the authors, a PDF of the entire book is available, with support of the book’s print edition publishers. The hardcover edition of this book was published by Chapman and Hall/CRC as part of The R Series in 2019, ISBN 9781138711136.

ArcUser (Spring 2019, GIS Bookshelf), a publication of Esri


“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, Canada

“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 (USA) and University of Bergen (Norway)

“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, USA

“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, USA