Geostat office: software for spatial data analysis

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Analysis and Modelling of Spatial Environmental Data presents traditional geostatistics methods for variography and spatial predictions, approaches to conditional stochastic simulation and local probability distribution function estimation, and select aspects of Geographical Information Systems. It includes real case studies using Geostat Office software tools under MS Windows and also provides tools and methods to solve problems in prediction, characterization, optimization, and density estimation. The author describes fundamental methodological aspects of the analysis and modelling of spatially distributed data and the application by way of a specific and user-friendly software, GSO Geostat Office.

Presenting complete coverage of geostatistics and machine learning algorithms, the book explores the relationships and complementary nature of both approaches and illustrates them with environmental and pollution data. The book includes introductory chapters on machine learning, artificial neural networks of different architectures, and support vector machines algorithms. Several chapters cover monitoring network analysis, artificial neural networks, support vector machines, and simulations. The book demonstrates thepromising results of the application of SVM to environmental and pollution data.

Preface	p. vii
Chapter 1 Introduction to Environmental Data Analysis and Modelling	p. 1
1.1 Introduction	p. 1
1.2 Environmental decision support systems and prediction mapping	p. 7
1.3 Presentation of the case studies	p. 8
1.4 Spatial data analysis with Geostat Office	p. 12
Chapter 2 Exploratory Spatial Data Analysis. Analysis of Monitoring Networks. Declustering	p. 17
2.1 Introduction	p. 17
2.2 Exploratory data analysis	p. 18
2.3 Transformation of data	p. 27
2.4 Quantitative description of monitoring networks	p. 30
2.5 Declustering	p. 38
2.6 Geostat Office: monitoring networks and declustering	p. 43
2.7 Conclusions	p. 44
Chapter 3 Spatial Data Analysis: Deterministic Interpolations	p. 47
3.1 Introduction	p. 47
3.2 Validation tools	p. 48
3.3 Models of deterministic interpolations	p. 51
3.4 Deterministic interpolations with Geostat Office	p. 59
3.5 Conclusions	p. 61
Chapter 4 Introduction to Geostatistics. Variography	p. 63
4.1 Geostatistics: Theory of regionalized variables	p. 63
4.2 Geostatistics: Basic hypotheses	p. 64
4.3 Variography	p. 65
4.4 Coregionalization models	p. 71
4.5 Exploratory variography in practice	p. 75
4.6 Variography with Geostat Office	p. 79
4.7 Comments and interpretations	p. 85
4.8 Conclusions	p. 88
Chapter 5 Geostatistical Spatial Predictions	p. 89
5.1 Introduction	p. 89
5.2 Family of kriging models	p. 90
5.3 Kriging predictions with Geostat Office	p. 97
5.4 Spatial co-estimations. Co-kriging models	p. 111
5.5 Co-kriging predictions. A case study	p. 114
5.6 Conclusions	p. 117
Chapter 6 Estimation of Local Probability Density Functions	p. 119
6.1 Introduction	p. 119
6.2 Indicator kriging	p. 120
6.3 Indicator Kriging. A case study	p. 123
6.4 Conclusions and comments on indicator kriging	p. 127
Chapter 7 Conditional Stochastic Simulations	p. 129
7.1 Introduction	p. 129
7.2 Models of spatial simulations	p. 131
7.3 Conditional stochastic simulations. Case studies	p. 138
7.4 Review of other simulation models	p. 148
7.5 Comments and discussions	p. 152
7.6 Check of the simulations	p. 155
7.7 Conclusions	p. 156
Annex 1. Conditioning simulations with conditional kriging	p. 157
Annex 2. Non-conditional simulations of stationary isotropic multigaussian random functions	p. 159
Annex 3. Sequential gaussian simulations with Geostat Office	p. 164
Chapter 8 Artificial Neural Networks and Spatial Data Analysis	p. 169
8.1 Introduction	p. 169
8.2 Basics of ANN	p. 170
8.3 Artificial neural networks learning	p. 173
8.4 Multilayer feedforward neural networks	p. 175
8.5 General regression neural networks (GRNS)	p. 189
8.6 Neural network residual kriging model (NNRK)	p. 199
8.7 Conclusions	p. 205
Chapter 9 Support Vector Machines for Environmental Spatial Data	p. 207
9.1 Introduction	p. 207
9.2 Support vector machines classification	p. 208
9.3 Spatial data mapping with support vector regression	p. 212
9.4 A case study	p. 216
9.5 Evaluation of SVM binary spatial classification with nonparametric conditional stochastic simulations	p. 227
9.6 GeoSVM computer program	p. 235
9.7 Conclusions	p. 237
Chapter 10 Geographical Information Systems and Spatial Data Analysis	p. 239
10.1 Introduction	p. 239
10.2 Contributing disciplines and technologies	p. 240
10.3 GIS technology	p. 242
10.4 GIS functionality	p. 243
10.5 Basic objects of GIS	p. 244
10.6 Representation of the GIS object	p. 244
10.7 GIS layers	p. 247
10.8 Map projections	p. 248
10.9 Geostat Office and GIS	p. 248
10.10 Conclusions	p. 254
Chapter 11 Conclusions	p. 257
Glossaries	p. 259
Statistics, Geostatistics, Fractals	p. 259
Machine Learning	p. 267
References	p. 276

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