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Informationen zum Autor Mikhail Kanevski , Institute of Geomatics and Analysis of Risk, University of Lausanne, Switzerland. Klappentext This book combines geostatistics and global mapping systems to present an up-to-the-minute study of environmental data. Featuring numerous case studies, the reference covers model dependent (geostatistics) and data driven (machine learning algorithms) analysis techniques such as risk mapping, conditional stochastic simulations, descriptions of spatial uncertainty and variability, artificial neural networks (ANN) for spatial data, Bayesian maximum entropy (BME), and more. Zusammenfassung This book combines geostatistics and global mapping systems to present an up-to-the-minute study of environmental data. Inhaltsverzeichnis Preface xi Chapter 1. Advanced Mapping of Environmental Data: Introduction 1 M. KANEVSKI 1.1. Introduction 1 1.2. Environmental data analysis: problems and methodology 3 1.2.1. Spatial data analysis: typical problems 3 1.2.2. Spatial data analysis: methodology 5 1.2.3. Model assessment and model selection 8 1.3. Resources 12 1.3.1. Books, tutorials 12 1.3.2. Software 12 1.4. Conclusion 14 1.5. References 15 Chapter 2. Environmental Monitoring Network Characterization and Clustering 19 D. TUIA and M. KANEVSKI 2.1. Introduction 19 2.2. Spatial clustering and its consequences 20 2.2.1. Global parameters 21 2.2.2. Spatial predictions 22 2.3. Monitoring network quantification 23 2.3.1. Topological quantification 23 2.3.2. Global measures of clustering 23 2.3.2.1. Topological indices 23 2.3.2.2. Statistical indices 24 2.3.3. Dimensional resolution: fractal measures of clustering 26 2.3.3.1. Sandbox method 27 2.3.3.2. Box-counting method 30 2.3.3.3. Lacunarity 33 2.4. Validity domains 34 2.5. Indoor radon in Switzerland: an example of a real monitoring network 36 2.5.1. Validity domains 37 2.5.2. Topological index 37 2.5.3. Statistical indices 38 2.5.3.1. Morisita index 38 2.5.3.2. K-function 39 2.5.4. Fractal dimension 40 2.5.4.1. Sandbox and box-counting fractal dimension 40 2.5.4.2. Lacunarity 42 2.6. Conclusion 43 2.7. References 44 Chapter 3. Geostatistics: Spatial Predictions and Simulations 47 E. SAVELIEVA, V. DEMYANOV and M. MAIGNAN 3.1. Assumptions of geostatistics 47 3.2. Family of kriging models 49 3.2.1. Simple kriging 50 3.2.2. Ordinary kriging 50 3.2.3. Basic features of kriging estimation 51 3.2.4. Universal kriging (kriging with trend) 56 3.2.5. Lognormal kriging 56 3.3. Family of co-kriging models 58 3.3.1. Kriging with linear regression 58 3.3.2. Kriging with external drift 58 3.3.3. Co-kriging 59 3.3.4. Collocated co-kriging 60 3.3.5. Co-kriging application example 61 3.4. Probability mapping with indicator kriging 64 3.4.1. Indicator coding 64 3.4.2. Indicator kriging 66 3.4.3. Indicator kriging applications 69 3.4.3.1. Indicator kriging for 241Am analysis 69 3.4.3.2. Indicator kriging for aquifer layer zonation 71 3.4.3.3. Indicator kriging for localization of crab crowds 74 3.5. Description of spatial uncertainty with conditional stochastic simulations 76 3.5.1. Simulation vs. estimation 76 3.5.2. Stochastic simulation algorithms 77 3.5.3. Sequential Gaussian simulation 81 3.5.4. Sequential indicator simulations 84 3.5.5. Co-simulations of correlated variables 88 3.6. References 92 Chapter 4. Spatial Data Analysis and Mapping Using Machine Learning Algorithms 95 F. RAT...
