LEADER 05388nam 2200661Ia 450 001 9910830444003321 005 20230721030214.0 010 $a1-281-13530-5 010 $a9786611135300 010 $a0-470-51727-1 010 $a0-470-51726-3 035 $a(CKB)1000000000377261 035 $a(EBL)326412 035 $a(OCoLC)476124168 035 $a(SSID)ssj0000162228 035 $a(PQKBManifestationID)11149505 035 $a(PQKBTitleCode)TC0000162228 035 $a(PQKBWorkID)10201111 035 $a(PQKB)10901281 035 $a(MiAaPQ)EBC326412 035 $a(EXLCZ)991000000000377261 100 $a20070522d2007 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aGeostatistics for environmental scientists$b[electronic resource] /$fRichard Webster and Margaret A. Oliver 205 $a2nd ed. 210 $aChichester $cWiley$dc2007 215 $a1 online resource (332 p.) 225 1 $aStatistics in practice 300 $aPrevious ed.: 2001. 311 $a0-470-02858-0 320 $aIncludes bibliographical references and index. 327 $aGeostatistics 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 327 $a2.5 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 327 $a3.2 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? 327 $a4.8 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? 327 $a6 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 327 $a7.4 SPECTRAL ANALYSIS OF THE CARAGABAL TRANSECT 330 $aGeostatistics is essential for environmental scientists. Weather and climate vary from place to place, soil varies at every scale at which it is examined, and even man-made attributes - such as the distribution of pollution - vary. The techniques used in geostatistics are ideally suited to the needs of environmental scientists, who use them to make the best of sparse data for prediction, and top plan future surveys when resources are limited. Geostatistical technology has advanced much in the last few years and many of these developments are being incorporated into the practitioner's reperto 410 0$aStatistics in practice. 606 $aGeology$xStatistical methods 606 $aEnvironmental sciences$xStatistical methods 615 0$aGeology$xStatistical methods. 615 0$aEnvironmental sciences$xStatistical methods. 676 $a550.72 676 $a550/.7/2 700 $aWebster$b R$01701173 701 $aOliver$b M. A$g(Margaret A.)$01701174 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910830444003321 996 $aGeostatistics for environmental scientists$94084736 997 $aUNINA