E-raamat: Spatial Analysis

[ Spatial Analysis] is a splendid text on spatial statistics written by two eminent scholars in the field who have beautifully presented a wide range of topics. The text begins with some very interesting examples of spatially oriented data and their features and is followed by some superbly compiled expository chapters. What is especially appealing, in my opinion, is the attention paid by the authors to the theoretical developments and exposition of seemingly abstruse topics. The book is compactly written while retaining mathematical rigor. Specifically, the chapters on different flavours of spatial random fields and that on conditional autoregression models stand out in terms of their clarity of presentation. Inference primarily focuses on likelihood based methods and kriging, while appearing somewhat late in the book (Chapter 7 out of eight chapters), receives a very detailed treatment that includes Bayesian prediction methods as well. In summary, this elegant text will serve students, researchers and scholars invested in spatial statistics very well as a source of reference as well as a text to build courses from. I congratulate the authors' on this wonderful accomplishment.  

Sudipto Banerjee, PhD, Professor and Chair, Dept. of Biostatistics, UCLA Fielding School of Public Health





This book, Spatial Analysis, by John Kent and Kanti Mardia is a must-read by all statisticians whose interests include spatial statistics, and it would make an excellent graduate-level text. The authors find the sweet spot at the juncture of intuition, methodology, and applications, and they show us in many ways how the validity of scientific inferences and its software has its genesis in the construction of valid statistical models.

 

 Noel Cressie, Distinguished Professor and Director, Centre for Environmental Informatics, University of Wollongong, Australia

The modern world of big data is often accompanied by little understanding. Often this data comes in a spatial form, and critically our understanding emerges from spatial analysis. Its not possible to imagine two better guides to this domain than John Kent and Kanti Mardia. In Spatial Analysis Kent and Mardia provide a comprehensive guide to modern thinking that is classically grounded. This book is a must-read for those who are taking understanding seriously as part of handling modern spatial data sets across the domains of machine learning, statistics and data science.

Neil Lawrence, DeepMind Professor of Machine Learning, University of Cambridge

[ Spatial Analysis] is a delightful and authoritative book on the subject of spatial statistical analysis by two of the worlds most eminent researchers in the field of spatial statistics and shape analysis. The book eloquently discusses most of the topics in spatial analysis in a wonderfully organised manner. For example, there are two chapters on random fields, two chapters on estimation methods, one on modelling and another large chapter on kriging. With another chapter on additional topics such as Co-kriging, Bayesian hierarchical modeling, spatio-temporal modeling, and thin plate splines this book covers most of the concepts researchers need to know in this area. The main emphasis of the book is on theoretical aspects but it does not lose sight of applications. The chapter one itself motivates the theory with several example data sets which include fingerprint of the famous statistician Sir R A Fisher. The book does justice to the theory by presenting and explaining it in an accessible format for all graduate students and researchers. The book also provides enjoyable to read personal historical notes and anecdotes regarding the course of development of the theory of spatial analysis.

Sujit Sahu, Professor of Mathematical Sciences, University of Southampton