Geostatistics for environmental scientists [[electronic resource] /] / Richard Webster and Margaret A. Oliver

• Record Nr.: UNINA9910830444003321
• Autore: Webster R
• Pubbl/distr/stampa: Chichester, : Wiley, c2007
• ISBN: 1-281-13530-5; 9786611135300; 0-470-51727-1; 0-470-51726-3
• Edizione: [2nd ed.]
• Descrizione fisica: 1 online resource (332 p.)
• Collana: Statistics in practice
• Altri autori (Persone): OliverM. A (Margaret A.)
• Disciplina: 550.72; 550/.7/2
• Soggetti: Geology - Statistical methods; Environmental sciences - Statistical methods
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Previous ed.: 2001.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Geostatistics for Environmental Scientists; Contents; Preface; 1 Introduction; 1.1 WHY GEOSTATISTICS?; 1.1.1 Generalizing; 1.1.2 Description; 1.1.3 Interpretation; 1.1.4 Control; 1.2 A LITTLE HISTORY; 1.3 FINDING YOUR WAY; 2 Basic Statistics; 2.1 MEASUREMENT AND SUMMARY; 2.1.1 Notation; 2.1.2 Representing variation; 2.1.3 The centre; 2.1.4 Dispersion; 2.2 THE NORMAL DISTRIBUTION; 2.3 COVARIANCE AND CORRELATION; 2.4 TRANSFORMATIONS; 2.4.1 Logarithmic transformation; 2.4.2 Square root transformation; 2.4.3 Angular transformation; 2.4.4 Logit transformation; 2.5 EXPLORATORY DATA ANALYSIS AND DISPLAY2.5.1 Spatial aspects; 2.6 SAMPLING AND ESTIMATION; 2.6.1 Target population and units; 2.6.2 Simple random sampling; 2.6.3 Confidence limits; 2.6.4 Student's t; 2.6.5 The x2 distribution; 2.6.6 Central limit theorem; 2.6.7 Increasing precision and efficiency; 2.6.8 Soil classification; 3 Prediction and Interpolation; 3.1 SPATIAL INTERPOLATION; 3.1.1 Thiessen polygons (Voronoi polygons, Dirichlet tessellation); 3.1.2 Triangulation; 3.1.3 Natural neighbour interpolation; 3.1.4 Inverse functions of distance; 3.1.5 Trend surfaces; 3.1.6 Splines; 3.2 SPATIAL CLASSIFICATION AND PREDICTING FROM SOIL MAPS3.2.1 Theory; 3.2.2 Summary; 4 Characterizing Spatial Processes: The Covariance and Variogram; 4.1 INTRODUCTION; 4.2 A STOCHASTIC APPROACH TO SPATIAL VARIATION: THE THEORY OF REGIONALIZED VARIABLES; 4.2.1 Random variables; 4.2.2 Random functions; 4.3 SPATIAL COVARIANCE; 4.3.1 Stationarity; 4.3.2 Ergodicity; 4.4 THE COVARIANCE FUNCTION; 4.5 INTRINSIC VARIATION AND THE VARIOGRAM; 4.5.1 Equivalence with covariance; 4.5.2 Quasi-stationarity; 4.6 CHARACTERISTICS OF THE SPATIAL CORRELATION FUNCTIONS; 4.7 WHICH VARIOGRAM?; 4.8 SUPPORT AND KRIGE'S RELATION4.8.1 Regularization; 4.9 ESTIMATING SEMIVARIANCES AND COVARIANCES; 4.9.1 The variogram cloud; 4.9.2 h-Scattergrams; 4.9.3 Average semivariances; 4.9.4 The experimental covariance function; 5 Modelling the Variogram; 5.1 LIMITATIONS ON VARIOGRAM FUNCTIONS; 5.1.1 Mathematical constraints; 5.1.2 Behaviour near the origin; 5.1.3 Behaviour towards infinity; 5.2 AUTHORIZED MODELS; 5.2.1 Unbounded random variation; 5.2.2 Bounded models; 5.3 COMBINING MODELS; 5.4 PERIODICITY; 5.5 ANISOTROPY; 5.6 FITTING MODELS; 5.6.1 What weights?; 5.6.2 How complex?; 6 Reliability of the Experimental Variogram and Nested Sampling6.1 RELIABILITY OF THE EXPERIMENTAL VARIOGRAM; 6.1.1 Statistical distribution; 6.1.2 Sample size and design; 6.1.3 Sample spacing; 6.2 THEORY OF NESTED SAMPLING AND ANALYSIS; 6.2.1 Link with regionalized variable theory; 6.2.2 Case study: Youden and Mehlich's survey; 6.2.3 Unequal sampling; 6.2.4 Case study: Wyre Forest survey; 6.2.5 Summary; 7 Spectral Analysis; 7.1 LINEAR SEQUENCES; 7.2 GILGAI TRANSECT; 7.3 POWER SPECTRA; 7.3.1 Estimating the spectrum; 7.3.2 Smoothing characteristics of windows; 7.3.3 Confidence; 7.4 SPECTRAL ANALYSIS OF THE CARAGABAL TRANSECT
• Sommario/riassunto: 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 reperto