Statistics for Spatial Data (Wiley Series in Probability and Mathematical Statistics-Applied Probability and Statistics Section)

By Noel A.C. Cressie

Book Overview

Statistics for Spatial Data is concerned with analyzing spatial data through statistical models. The book unifies many diverse areas by using consistent notation, and delineates clearly the three strongest growth areas - geostatistical data, lattice data and point patterns. It corrects mistakes which have not previously been subject to close scrutiny from statisticians, and gives new results at the frontiers of the subject.

• Format: Hardcover
• Language: English
• ISBN: 0471843369
• ISBN13: 9780471843368
• Release Date: January 1991
• Publisher: John Wiley & Sons, Inc.
• Length: 900 Pages
• Weight: 2.98 lbs.
• Dimensions: 9.8" x 6.5" x 1.8"

Customer Reviews

thorough account of spatial statistical methods including resampling approaches

This is a wonderful source of information on methods for analyzing spatial data. It is still reasonably current for this rapidly developing area in statistics. You won't find some of the latest research on the joint modeling of space and time. But I expect that Cressie is working on a book that will cover it. It is an encyclopedia of references. The author covers the gamut of techniques. This explains why the book is so lengthy. I think that if he plans a revision he will be more selective and only cover those methods that have proved to be the most promising. Here you can finally learn what kriging is and even see how the bootstrap can be applied to spatial data. Michael Stein's book is also very good and specializes in kriging. This book may look pricy at $138 but given the size of the book, the content and current textbook prices, this is a good buy.

Excellent book to learn geostatistics

I am a beginner in geostatistics and this book helped me alot to learn and analyze my data for my thesis. Excellent reference book to keep.

The best book for Geostatistician and not only ...

The book covers very wide spectrum of different simulation and prediction methods and techniques. One can really merit from this book but under good mathematical background. The large number of practical examples helps for better understanding. The most of them can be recommended to apply for the development and testing of own statistical and mathematical software.