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Geostatistics for Environmental Scientists

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ISBN-10: 0471965537

ISBN-13: 9780471965534

Edition: 2001

Authors: Richard Webster, Margaret A. Oliver

List price: $145.00
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Description:

This is a concise yet comprehensive guide to the statistical methods and modelling techniques which are most useful to environmental scientists. Starting at an elementary level, the book goes on to cover more advanced techniques.
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Book details

List price: $145.00
Copyright year: 2001
Publisher: John Wiley & Sons, Incorporated
Publication date: 3/12/2001
Binding: Hardcover
Pages: 286
Size: 6.25" wide x 9.50" long x 0.75" tall
Weight: 1.144
Language: English

Preface
Introduction
Why geostatistics?
Generalizing
Description
Interpretation
Control
A little history
Finding your way
Basic Statistics
Measurement and summary
Notation
Representing variation
The centre
Dispersion
The normal distribution
Covariance and correlation
Transformations
Logarithmic transformation
Square root transformation
Angular transformation
Logit transformation
Exploratory data analysis and display
Spatial aspects
Sampling and estimation
Target population and units
Simple random sampling
Confidence limits
Student's t
The x[superscript 2] distribution
Central limit theorem
Increasing precision and efficiency
Soil classification
Prediction and Interpolation
Spatial interpolation
Thiessen polygons (Voronoi polygons, Dirichlet tessellation)
Triangulation
Natural neighbour interpolation
Inverse functions of distance
Trend surfaces
Splines
Spatial classification and predicting from soil maps
Theory
Summary
Characterizing Spatial Processes: The Covariance and Variogram
Introduction
A stochastic approach to spatial variation: the theory of regionalized variables
Random variables
Random functions
Spatial covariance
Stationarity
Ergodicity
The covariance function
Intrinsic variation and the variogram
Equivalence with covariance
Quasi-stationarity
Characteristics of the spatial correlation functions
Which variogram?
Support and Krige's relation
Regularization
Estimating semivariances and covariances
The variogram cloud
h-Scattergrams
Average semivariances
The experimental covariance function
Modelling the Variogram
Limitations on variogram functions
Mathematical constraints
Behaviour near the origin
Behaviour towards infinity
Authorized models
Unbounded random variation
Bounded models
Combining models
Periodicity
Anisotropy
Fitting models
What weights?
How complex?
Reliability of the Experimental Variogram and Nested Sampling
Reliability of the experimental variogram
Statistical distribution
Sample size and design
Sample spacing
Theory of nested sampling and analysis
Link with regionalized variable theory
Case study: Youden and Mehlich's survey
Unequal sampling
Case study: Wyre Forest survey
Summary
Spectral Analysis
Linear sequences
Gilgai transect
Power spectra
Estimating the spectrum
Smoothing characteristics of windows
Confidence
Spectral analysis of the Caragabal transect
Bandwidths and confidence intervals for Caragabal
Further reading on spectral analysis
Local Estimation or Prediction: Kriging
General characteristics of kriging
Kinds of kriging
Theory of ordinary kriging
Weights
Examples
Kriging at the centre of the lattice
Kriging off-centre in the lattice and at a sampling point
Kriging from irregularly spaced data
Neighbourhood
Ordinary kriging for mapping
Case study
Kriging with known measurement error
Summary
Regional estimation
Simple kriging
Lognormal kriging
Optimal sampling for mapping
Isotropic variation
Anisotropic variation
Cross-validation
Scatter and regression
Kriging in the Presence of Trend and Factorial Kriging
Non-stationarity in the mean
Some background
Application of residual maximum likelihood
Estimation of the variogram by REML
Practicalities
Kriging with external drift
Case study
Factorial kriging analysis
Nested variation
Theory
Kriging analysis
Illustration
Cross-Correlation, Coregionalization and Cokriging
Introduction
Estimating and modelling the cross-correlation
Intrinsic coregionalization
Example: CEDAR Farm
Cokriging
Is cokriging worth the trouble?
Example of benefits of cokriging
Principal components of coregionalization matrices
Pseudo-cross-variogram
Disjunctive Kriging
Introduction
The indicator approach
Indicator coding
Indicator variograms
Indicator kriging
Disjunctive kriging
Assumptions of Gaussian disjunctive kriging
Hermite polynomials
Disjunctive kriging for a Hermite polynomial
Estimation variance
Conditional probability
Change of support
Case study
Other case studies
Summary
Stochastic Simulation
Introduction
Simulation from a random process
Unconditional simulation
Conditional simulation
Technicalities
Lower-upper decomposition
Sequential Gaussian simulation
Simulated annealing
Simulation by turning bands
Algorithms
Uses of simulated fields
Illustration
Aide-memoire for Spatial Analysis
Introduction
Notation
Screening
Histogram and summary
Normality and transformation
Spatial distribution
Spatial analysis: the variogram
Modelling the variogram
Spatial estimation or prediction: kriging
Mapping
GenStat Instructions for Analysis
Summary statistics
Histogram
Cumulative distribution
Posting
The variogram
Experimental variogram
Fitting a model
Kriging
Coregionalization
Auto- and cross-variograms
Fitting a model of coregionalization
Cokriging
Control
References
Index