Causality: Models, Reasoning, and Inference

sampling theory of Pierre Gy (1982). This theory addresses the physical and practical aspects of sampling in the  eld that result in variation and bias. The third, geostatistical appraisal (GA), looks at the spatial relationships among samples, ef cient estimation, and minimization of uncertainty. The combination, geostatistical error management (GEM), provides a strategy for the whole process, from start to  nish. This book only recently came to my attention, but as soon as I learned of it, I ordered it. A quick glance was suf cient motivation for me to start reading it in detail immediately. The style is conversational but has enough theory and examples to be self-contained. In fact, there were several areas of which I had a passing knowledge, but now I have a  rm foundation and can explain the techniques to others. The author relies heavily on other published books, and, with their permission, takes graphs and maps directly from them. So rather than having to sift through various references for those things applicable to the problem at hand, practitioners have almost everything in one place. GEM has 17 chapters organized into  ve parts. The  rst part, “ Introduction to Geostatistical Error Management,” has three chapters. They provide motivation, background, and introductory material. The information is quite valuable in giving an overview of the subject history, issues, controversies, and vocabulary. Part 2, “Statistical Considerations,” contains Chapters 4–6 and focuses on statistics. These chapters provide the basic statistical information used in later chapters, including the binomial, Poisson, and normal distributions and formulas for estimators. The author also makes an important distinction between a sampling unit and sampling support, and this plays a major role in subsequent chapters. Parts 3, 4, and 5 provide great detail on the principal aspects of the book listed previously—DQO’s, STP, and GA. Part 3, “Sampling Theory and Practice,” relies mainly on a book by Francis Pitard (1993), a practitioner of the sampling theory of Pierre Gy, and Chapters 7 and 8 cover the seven sampling errors discovered by Gy. There are a few formulas, but the author keeps these to a minimum, focusing on the practical aspects. Thorough detail is given in Appendixes A, B, and C for the interested reader. The appendix material will be very challenging and time consuming, however, for someone encountering it for the  rst time, partly because of the new vocabulary and partly because of the new perspective. Practitioners, however, need not read it, since the book chapters are suf cient to provide the main ideas. Another source with an overview of this topic is the short primer I wrote (Smith 2001). Part 4, “Geostatistical Appraisal,” contains the meat of the book in Chapters 9–14. More statistical information is covered here, but the main topics are variogram modeling and kriging. The explanations and numerous examples on these two topics are excellent. The reader does not get bogged down in too much theory, which is readily available from Cressie (1991), for example. The chapter titles describe the breadth and depth of this part of the book: 9. “Bivariate Distributions,” 10. “Variograms: Quanti cation of Spatial Continuity,” 11. “The Volume-Variance Relationship,” 12. “Estimation Variance,” 13. “Optimizing Estimation: Kriging,” 14. “Practical Aspects of Kriging. ” Part 5 of the book, “Data Quality Objectives,” consists of Chapters 15–17. Chapter 15 goes through the seven-step DQO process. The title of Chapter 16 is suf cient to describe it—“Integrating DQOs and STP: Development of Sampling Strategies. ” Chapter 17 integrates DQO’s and GA. In other words, these last three chapters provide an excellent overview of the book once details in the preceding 14 chapters are understood. In fact, Part 5, about 100 pages long, can be used as a guide for planning, sampling, estimating, and making decisions on a project. The book has a number of typographical errors in equations and formulas, a common feature in  rst editions. Some arise from the author trying to reconcile different notation from the numerous sources he uses. Others result from resetting at the printers. For someone not familiar with the various theories or for someone wanting to use the details for calculations or for in-depth understanding, this is a big drawback. However, the author has made revisions, which he hopes to have in a second edition. The book could also potentially bene t from including a list of available software programs that carry out the computations discussed, especially in the areas of variograms and kriging. This, however, comes with the risk of dating the book as new software becomes available, existing software becomes unavailable, or companies merge or consolidate. The author provides a Web-based solution to this issue, listing currently available software on his Web site at http://www.gemdqos. com. Geostatistical Error Management integrates DQO’s, STP, and GA, offering a defensible and holistic approach to environmental decision making that also emphasizes the importance of economics. It has been welcomed by the EPA, which now references it in several of its sampling and quality assurance guidance documents. I highly recommend this book for any environmental statistics library.