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A reader-friendly introduction to geostatistics for students and researchers struggling with statistics. Using simple, clear explanations for introductory and advanced material, it demystifies complex concepts and makes formulas and statistical tests easy to apply. Beginning with a critical evaluation of experimental and sampling design, the book moves on to explain essential concepts of probability, statistical significance and type 1 and type 2 error. An accessible graphical explanation of analysis of variance (ANOVA) leads onto advanced ANOVA designs, correlation and regression, and non-parametric tests including chi-square. Finally, it introduces the essentials of multivariate techniques, multi-dimensional scaling and cluster analysis, analysis of sequences and concepts of spatial analysis. Illustrated with wide-ranging examples from topics across the Earth and environmental sciences, Geostatistics Explained can be used for undergraduate courses or for self-study and reference. Worked examples at the end of each chapter reinforce a clear understanding of the statistical tests and their applications.
This reader-friendly introduction to geostatistics demystifies complex concepts and makes formulas and statistical tests easy to apply. With wide-ranging examples from topics across the Earth and environmental sciences, and worked examples at the end of each chapter, this book can be used for undergraduate courses or for self-study and reference.
Introducing geostatistics from a multivariate perspective is the main aim of this book. The idea took root while teaching geostatistics at the Centre de Geostatis tique (Ecole des Mines de Paris) over the past ten years in the two postgraduate programs DEA and CFSG. A first script of lecture notes in French originated from this activity. A specialized course on Multivariate and Exploratory Geostatistics held in September 1993 in Paris (organized in collaboration with the Department of Statistics of Trinity College Dublin) was the occasion to test some of the mate rial on a pluridisciplinary audience. Another important opportunity arose last year when giving a lecture on Spatial Statistics during the summer term at the Department of Statistics of the University of Washington at Seattle, where part of this manuscript was distributed in an early version. Short accounts were also given during COMETT and TEMPUS courses on geostatistics for environment al studies in Fontainebleau, Freiberg, Rome and Prague, which were sponsored by the European Community. I wish to thank the participants of these various courses for their stimulating questions and comments. Among the organizers of these courses, I particularly want to acknowledge the support received from Georges Matheron, Pierre Chau vet, Margaret Armstrong, John Haslett and Paul Sampson. Michel Grzebyk has made valuable comments on Chapters 26 and 27, which partly summarize some of his contributions to the field.
"Ideal for anyone who wishes to gain a practical understanding of spatial statistics and geostatistics. Difficult concepts are well explained and supported by excellent examples in R code, allowing readers to see how each of the methods is implemented in practice" - Professor Tao Cheng, University College London Focusing specifically on spatial statistics and including components for ArcGIS, R, SAS and WinBUGS, this book illustrates the use of basic spatial statistics and geostatistics, as well as the spatial filtering techniques used in all relevant programs and software. It explains and demonstrates techniques in: spatial sampling spatial autocorrelation local statistics spatial interpolation in two-dimensions advanced topics including Bayesian methods, Monte Carlo simulation, error and uncertainty. It is a systematic overview of the fundamental spatial statistical methods used by applied researchers in geography, environmental science, health and epidemiology, population and demography, and planning. A companion website includes digital R code for implementing the analyses in specific chapters and relevant data sets to run the R codes.
Presents a set of linked HTML documents on the application of geostatistical theory, designed to be viewed and navigated with an Internet browser.
Geostatistics is essential for environmental scientists. Weather and climate vary from place to place, soil varies at every scale at which it is examined, and even man-made attributes – such as the distribution of pollution – vary. The techniques used in geostatistics are ideally suited to the needs of environmental scientists, who use them to make the best of sparse data for prediction, and top plan future surveys when resources are limited. Geostatistical technology has advanced much in the last few years and many of these developments are being incorporated into the practitioner’s repertoire. This second edition describes these techniques for environmental scientists. Topics such as stochastic simulation, sampling, data screening, spatial covariances, the variogram and its modeling, and spatial prediction by kriging are described in rich detail. At each stage the underlying theory is fully explained, and the rationale behind the choices given, allowing the reader to appreciate the assumptions and constraints involved.
New Methods of Geostatistical Analysis and Graphical Presentation
A two-week summer short course entitled Current Statistical Methods in Geology supported by the National Science Foundation was held at the University of Illinois at Chicago Circle in Chicago, Illinois from June 19 to June 30, 1972. The aim of the short course was to bridge the gap between the traditional first courses in sta tistics offered at most educational institutions and geostatistics as it is being developed by geologists and statisticians engaged in the application of statistics in geology. The course was intended for geology college teachers who were either then teaching or preparing to teach a course within their department dealing with computer ap plications and the use of statistical methods in geology. This book arose out of the class notes which were prepared by the course director and the invited lecturers. We are grateful to the 28 teachers who attended for their enthu siastic interest and thoughtful responses to the many statistical concepts presented to them as geologists during the two weeks of the course. I am deeply grateful to my graduate assistants, Richard Kolb and Andrea Krivz, for the long hours spent in collating the course mater ials, testing the various computer programs, and instructing the par ticipants in the use of computer BASIC.
Thoroughly revised and updated, this new edition of the text that helped define the field continues to present important methods in the quantitative analysis of geologic data, while showing students how statistics and computing can be applied to commonly encountered problems in the earth sciences. In addition to new and expanded coverage of key topics, the Third Edition features new pedagogy, end-of-chapter review exercises, and an accompanying website that contains all of the data for every example and exercise found in the book.
Geostatistical Functional Data Analysis Explore the intersection between geostatistics and functional data analysis with this insightful new reference Geostatistical Functional Data Analysis presents a unified approach to modelling functional data when spatial and spatio-temporal correlations are present. The Editors link together the wide research areas of geostatistics and functional data analysis to provide the reader with a new area called geostatistical functional data analysis that will bring new insights and new open questions to researchers coming from both scientific fields. This book provides a complete and up-to-date account to deal with functional data that is spatially correlated, but also includes the most innovative developments in different open avenues in this field. Containing contributions from leading experts in the field, this practical guide provides readers with the necessary tools to employ and adapt classic statistical techniques to handle spatial regression. The book also includes: A thorough introduction to the spatial kriging methodology when working with functions A detailed exposition of more classical statistical techniques adapted to the functional case and extended to handle spatial correlations Practical discussions of ANOVA, regression, and clustering methods to explore spatial correlation in a collection of curves sampled in a region In-depth explorations of the similarities and differences between spatio-temporal data analysis and functional data analysis Aimed at mathematicians, statisticians, postgraduate students, and researchers involved in the analysis of functional and spatial data, Geostatistical Functional Data Analysis will also prove to be a powerful addition to the libraries of geoscientists, environmental scientists, and economists seeking insightful new knowledge and questions at the interface of geostatistics and functional data analysis.