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VersandkostenfreiA state-of-the-art presentation of optimum spatio-temporal sampling design - bridging classic ideas with modern statistical modeling concepts and the latest computational methods.
Spatio-temporal Design presents a comprehensive state-of-the-art presentation combining both classical and modern treatments of network design and planning for spatial and spatio-temporal data acquisition. A common problem set is interwoven throughout the chapters, providing various perspectives to illustrate a complete insight to the problem at hand.
Motivated by the high demand for statistical analysis of data that takes spatial and spatio-temporal information into account, this book incorporates ideas from the areas of time series, spatial statistics and stochastic processes, and combines them to discuss optimum spatio-temporal sampling design.
Spatio-temporal Design: Advances in Efficient Data Acquisition:
* Provides an up-to-date account of how to collect space-time data for monitoring, with a focus on statistical aspects and the latest computational methods
* Discusses basic methods and distinguishes between design and model-based approaches to collecting space-time data.
* Features model-based frequentist design for univariate and multivariate geostatistics, and second-phase spatial sampling.
* Integrates common data examples and case studies throughout the book in order to demonstrate the different approaches and their integration.
* Includes real data sets, data generating mechanisms and simulation scenarios.
* Accompanied by a supporting website featuring R code.
Spatio-temporal Design presents an excellent book for graduate level students as well as a valuable reference for researchers and practitioners in the fields of applied mathematics, engineering, and the environmental and health sciences.
Spatio-temporal Design presents a comprehensive state-of-the-art presentation combining both classical and modern treatments of network design and planning for spatial and spatio-temporal data acquisition. A common problem set is interwoven throughout the chapters, providing various perspectives to illustrate a complete insight to the problem at hand.
Motivated by the high demand for statistical analysis of data that takes spatial and spatio-temporal information into account, this book incorporates ideas from the areas of time series, spatial statistics and stochastic processes, and combines them to discuss optimum spatio-temporal sampling design.
Spatio-temporal Design: Advances in Efficient Data Acquisition:
* Provides an up-to-date account of how to collect space-time data for monitoring, with a focus on statistical aspects and the latest computational methods
* Discusses basic methods and distinguishes between design and model-based approaches to collecting space-time data.
* Features model-based frequentist design for univariate and multivariate geostatistics, and second-phase spatial sampling.
* Integrates common data examples and case studies throughout the book in order to demonstrate the different approaches and their integration.
* Includes real data sets, data generating mechanisms and simulation scenarios.
* Accompanied by a supporting website featuring R code.
Spatio-temporal Design presents an excellent book for graduate level students as well as a valuable reference for researchers and practitioners in the fields of applied mathematics, engineering, and the environmental and health sciences.
Inhaltsverzeichnis
Contributors xv
Foreword xix
1 Collecting spatio-temporal data 1
Jorge Mateu and Werner G. Mü ller
1. 1 Introduction 1
1. 2 Paradigms in spatio-temporal design 2
1. 3 Paradigms in spatio-temporal modeling 3
1. 4 Geostatistics and spatio-temporal random functions 4
1. 4. 1 Relevant spatio-temporal concepts 4
1. 4. 2 Properties of the spatio-temporal covariance and variogram functions 6
1. 4. 3 Spatio-temporal kriging 8
1. 4. 4 Spatio-temporal covariance models 10
1. 4. 5 Parametric estimation of spatio-temporal covariograms 11
1. 5 Types of design criteria and numerical optimization 13
1. 6 The problem set: Upper Austria 17
1. 6. 1 Climatic data 17
1. 6. 2 Grassland usage 18
1. 7 The chapters 23
Acknowledgments 28
References 28
2 Model-based frequentist design for univariate and multivariate geostatistics 37
Dale L. Zimmerman and Jie li
2. 1 Introduction 37
2. 2 Design for univariate geostatistics 38
2. 2. 1 Data-model framework 38
2. 2. 2 Design criteria 38
2. 2. 3 Algorithms 42
2. 2. 4 Toy example 42
2. 3 Design for multivariate geostatistics 45
2. 3. 1 Data-model framework 45
2. 3. 2 Design criteria 47
2. 3. 3 Toy example 48
2. 4 Application: Austrian precipitation data network 50
2. 5 Conclusions 52
References 53
3 Model-based criteria heuristics for second-phase spatial sampling 54
Eric M. Delmelle
3. 1 Introduction 54
3. 2 Geometric and geostatistical designs 56
3. 2. 1 Efficiency of spatial sampling designs 56
3. 2. 2 Sampling spatial variables in a geostatistical context 57
3. 2. 3 Sampling designs minimizing the kriging variance 58
3. 3 Augmented designs: Second-phase sampling 59
3. 3. 1 Additional sampling schemes to maximize change in the kriging variance 59
3. 3. 2 A weighted kriging variance approach 60
3. 4 A simulated annealing approach 63
3. 5 Illustration 65
3. 5. 1 Initial sampling designs 66
3. 5. 2 Augmented designs 68
3. 6 Discussion 68
References 69
4 Spatial sampling design by means of spectral approximations to the error process 72
Gunter Spö ck and Jü rgen Pilz
4. 1 Introduction 72
4. 2 A brief review on spatial sampling design 75
4. 3 The spatial mixed linear model 76
4. 4 Classical Bayesian experimental design problem 77
4. 5 The Smith and Zhu design criterion 79
4. 6 Spatial sampling design for trans-Gaussian kriging 81
4. 7 The spatDesign toolbox 82
4. 7. 1 Covariance estimation and variography software 83
4. 7. 2 Spatial interpolation and kriging software 84
4. 7. 3 Spatial sampling design software 85
4. 8 An example session 89
4. 8. 1 Preparatory calculations 89
4. 8. 2 Optimal design for the BSLM 93
4. 8. 3 Design for the trans-Gaussian kriging 94
4. 9 Conclusions 98
References 99
5 Entropy-based network design using hierarchical Bayesian kriging 103
Baisuo Jin, Yuehua Wu and Baiqi Miao
5. 1 Introduction 103
5. 2 Entropy-based network design using hierarchical Bayesian kriging 105
5. 3 The data 107
5. 4 Spatio-temporal modeling 107
5. 5 Obtaining a staircase data structure 111
5. 6 Estimating the hyperparameters H g and the spatial correlations between gauge stations 113
5. 7 Spatial predictive distribution over the 445 areas located in the 18 districts of Upper Austria 117
5. 8 Adding gauge stations over the 445 areas located in the 18 districts of Upper Austria 120
5. 9 Closing down an existing gauge station 122
5. 10 Model evaluation 124
Appendix 5. 1: Hierarchical Bayesian spatio-temporal modeling (or kriging) 124
Appendix 5. 2: Some estimated parameters 128
Acknowledgments 129
References 129
6 Accounting for design in the analysis of spatial data 131
Brian J. Reich and Montserrat Fuentes
6. 1 Introduction 131
6. 2 Modeling approaches 134
6. 2. 1 Informative missingness 134
6. 2. 2 Informative sampling 135
6. 2. 3 A two-stage approach for informative sampling 136
6. 3 Analysis of the Austrian precipitation data 137
6. 4 Discussion 139
References 141
7 Spatial design for knot selection in knot-based dimension reduction models 142
Alan E. Gelfand, Sudipto Banerjee and Andrew O. Finley
7. 1 Introduction 142
7. 2 Handling large spatial datasets 145
7. 3 Dimension reduction approaches 146
7. 3. 1 Basic properties of low rank models 146
7. 3. 2 Predictive process models: A brief review 148
7. 4 Some basic knot design ideas 149
7. 4. 1 A brief review of spatial design 149
7. 4. 2 A strategy for selecting knots 151
7. 5 Illustrations 153
7. 5. 1 A simulation example 153
7. 5. 2 A simulation example using the two-step analysis 159
7. 5. 3 Tree height and diameter analysis 160
7. 5. 4 Austria precipitation analysis 162
7. 6 Discussion and future work 165
References 166
8 Exploratory designs for assessing spatial dependence 170
Agnes Fussl, Werner G. Mü ller and Juan Rodrí guez-Dí az
8. 1 Introduction 170
8. 1. 1 The dataset and its visualization 172
8. 2 Spatial links 174
8. 2. 1 Spatial neighbors 175
8. 2. 2 Spatial weights 176
8. 3 Measures of spatial dependence 178
8. 4 Models for areal data 180
8. 4. 1 H0 : A spaceless regression model 181
8. 4. 2 H0 : Spatial regression models 185
8. 5 Design considerations 190
8. 5. 1 A design criterion 192
8. 5. 2 Example 194
8. 6 Discussion 195
Appendix 8. 1: R code 198
Acknowledgments 202
References 203
9 Sampling design optimization for space-time kriging 207
Gerard B. M. Heuvelink, Daniel A. Griffith, Tomislav Hengl and Stephanie J. Melles
9. 1 Introduction 207
9. 2 Methodology 209
9. 2. 1 Space-time universal kriging 209
9. 2. 2 Sampling design optimization with spatial simulated annealing 211
9. 3 Upper Austria case study 212
9. 3. 1 Descriptive statistics 212
9. 3. 2 Estimation of the space-time model and universal kriging 215
9. 3. 3 Optimal design scenario 1 218
9. 3. 4 Optimal design scenario 2 219
9. 3. 5 Optimal design scenario 3 219
9. 4 Discussion and conclusions 221
Appendix 9. 1: R code 222
Acknowledgment 227
References 228
10 Space-time adaptive sampling and data transformations 231
José M. Angulo, Marí a C. Bueso and Francisco J. Alonso
10. 1 Introduction 231
10. 2 Adaptive sampling network design 233
10. 2. 1 A simulated illustration 235
10. 3 Predictive information based on data transformations 238
10. 4 Application to Upper Austria temperature data 242
10. 5 Summary 246
Acknowledgments 247
References 247
11 Adaptive sampling design for spatio-temporal prediction 249
Thomas R. Fanshawe and Peter J. Diggle
11. 1 Introduction 249
11. 2 Review of spatial and spatio-temporal adaptive designs 251
11. 3 The stationary Gaussian model 253
11. 3. 1 Model specification 253
11. 3. 2 Theoretically optimal designs 254
11. 3. 3 A comparison of design strategies 254
11. 4 The dynamic process convolution model 257
11. 4. 1 Model specification 257
11. 4. 2 A comparison of design strategies 258
11. 5 Upper Austria rainfall data example 262
11. 6 Discussion 264
Appendix 11. 1 266
References 267
12 Semiparametric dynamic design of monitoring networks for non-Gaussian spatio-temporal data 269
Scott H. Holan and Christopher K. Wikle
12. 1 Introduction 269
12. 2 Semiparametric non-Gaussian space-time dynamic design 271
12. 2. 1 Semiparametric spatio-temporal dynamic Gamma model 271
12. 2. 2 Simulation-based dynamic design 274
12. 2. 3 Extended Kalman filter for dynamic gamma models 275
12. 2. 4 Extended Kalman filter design algorithm 277
12. 3 Application: Upper Austria precipitation 278
12. 4 Discussion 282
Acknowledgments 282
References 283
13 Active learning for monitoring network optimization 285
Devis Tuia, Alexei Pozdnoukhov, Loris Foresti and Mikhail Kanevski
13. 1 Introduction 285
13. 2 Statistical learning from data 287
13. 2. 1 Algorithmic approaches to learning 288
13. 2. 2 Over-fitting and model selection 288
13. 3 Support vector machines and kernel methods 289
13. 3. 1 Classification: SVMs 290
13. 3. 2 Density estimation: One-class SVM 292
13. 3. 3 Regression: Kernel ridge regression 293
13. 3. 4 Regression: SVR 294
13. 4 Active learning 294
13. 4. 1 A general framework 295
13. 4. 2 First steps in active learning: Reducing output variance 296
13. 4. 3 Exploration-exploitation strategies: Towards mixed approaches 297
13. 5 Active learning with SVMs 297
13. 5. 1 Margin sampling 297
13. 5. 2 Diversity of batches of samples 299
13. 5. 3 Committees of models 299
13. 6 Case studies 300
13. 6. 1 Austrian climatological data 300
13. 6. 2 Cesium-137 concentration after Chernobyl 304
13. 6. 3 Wind power plants sites evaluation 307
13. 7 Conclusions 312
Acknowledgments 314
References 314
14 Stationary sampling designs based on plume simulations 319
Kristina B. Helle and Edzer Pebesma
14. 1 Introduction 319
14. 2 Plumes: From random fields to simulations 320
14. 3 Cost functions 324
14. 3. 1 Detecting plumes 324
14. 3. 2 Mapping and characterising plumes 325
14. 3. 3 Combined cost functions 325
14. 4 Optimisation 326
14. 4. 1 Greedy search 326
14. 4. 2 Spatial simulated annealing 328
14. 4. 3 Genetic algorithms 329
14. 4. 4 Other methods 331
14. 4. 5 Evaluation and sensitivity 331
14. 4. 6 Use case: Combination and comparison of optimisation algorithms 332
14. 5 Results 334
14. 5. 1 Simulations 334
14. 5. 2 Greedy search 335
14. 5. 3 Sensitivity of greedy search to the plume simulations 336
14. 5. 4 Comparison of optimisation algorithms 337
14. 6 Discussion 340
Acknowledgments 341
References 341
Index 345
Foreword xix
1 Collecting spatio-temporal data 1
Jorge Mateu and Werner G. Mü ller
1. 1 Introduction 1
1. 2 Paradigms in spatio-temporal design 2
1. 3 Paradigms in spatio-temporal modeling 3
1. 4 Geostatistics and spatio-temporal random functions 4
1. 4. 1 Relevant spatio-temporal concepts 4
1. 4. 2 Properties of the spatio-temporal covariance and variogram functions 6
1. 4. 3 Spatio-temporal kriging 8
1. 4. 4 Spatio-temporal covariance models 10
1. 4. 5 Parametric estimation of spatio-temporal covariograms 11
1. 5 Types of design criteria and numerical optimization 13
1. 6 The problem set: Upper Austria 17
1. 6. 1 Climatic data 17
1. 6. 2 Grassland usage 18
1. 7 The chapters 23
Acknowledgments 28
References 28
2 Model-based frequentist design for univariate and multivariate geostatistics 37
Dale L. Zimmerman and Jie li
2. 1 Introduction 37
2. 2 Design for univariate geostatistics 38
2. 2. 1 Data-model framework 38
2. 2. 2 Design criteria 38
2. 2. 3 Algorithms 42
2. 2. 4 Toy example 42
2. 3 Design for multivariate geostatistics 45
2. 3. 1 Data-model framework 45
2. 3. 2 Design criteria 47
2. 3. 3 Toy example 48
2. 4 Application: Austrian precipitation data network 50
2. 5 Conclusions 52
References 53
3 Model-based criteria heuristics for second-phase spatial sampling 54
Eric M. Delmelle
3. 1 Introduction 54
3. 2 Geometric and geostatistical designs 56
3. 2. 1 Efficiency of spatial sampling designs 56
3. 2. 2 Sampling spatial variables in a geostatistical context 57
3. 2. 3 Sampling designs minimizing the kriging variance 58
3. 3 Augmented designs: Second-phase sampling 59
3. 3. 1 Additional sampling schemes to maximize change in the kriging variance 59
3. 3. 2 A weighted kriging variance approach 60
3. 4 A simulated annealing approach 63
3. 5 Illustration 65
3. 5. 1 Initial sampling designs 66
3. 5. 2 Augmented designs 68
3. 6 Discussion 68
References 69
4 Spatial sampling design by means of spectral approximations to the error process 72
Gunter Spö ck and Jü rgen Pilz
4. 1 Introduction 72
4. 2 A brief review on spatial sampling design 75
4. 3 The spatial mixed linear model 76
4. 4 Classical Bayesian experimental design problem 77
4. 5 The Smith and Zhu design criterion 79
4. 6 Spatial sampling design for trans-Gaussian kriging 81
4. 7 The spatDesign toolbox 82
4. 7. 1 Covariance estimation and variography software 83
4. 7. 2 Spatial interpolation and kriging software 84
4. 7. 3 Spatial sampling design software 85
4. 8 An example session 89
4. 8. 1 Preparatory calculations 89
4. 8. 2 Optimal design for the BSLM 93
4. 8. 3 Design for the trans-Gaussian kriging 94
4. 9 Conclusions 98
References 99
5 Entropy-based network design using hierarchical Bayesian kriging 103
Baisuo Jin, Yuehua Wu and Baiqi Miao
5. 1 Introduction 103
5. 2 Entropy-based network design using hierarchical Bayesian kriging 105
5. 3 The data 107
5. 4 Spatio-temporal modeling 107
5. 5 Obtaining a staircase data structure 111
5. 6 Estimating the hyperparameters H g and the spatial correlations between gauge stations 113
5. 7 Spatial predictive distribution over the 445 areas located in the 18 districts of Upper Austria 117
5. 8 Adding gauge stations over the 445 areas located in the 18 districts of Upper Austria 120
5. 9 Closing down an existing gauge station 122
5. 10 Model evaluation 124
Appendix 5. 1: Hierarchical Bayesian spatio-temporal modeling (or kriging) 124
Appendix 5. 2: Some estimated parameters 128
Acknowledgments 129
References 129
6 Accounting for design in the analysis of spatial data 131
Brian J. Reich and Montserrat Fuentes
6. 1 Introduction 131
6. 2 Modeling approaches 134
6. 2. 1 Informative missingness 134
6. 2. 2 Informative sampling 135
6. 2. 3 A two-stage approach for informative sampling 136
6. 3 Analysis of the Austrian precipitation data 137
6. 4 Discussion 139
References 141
7 Spatial design for knot selection in knot-based dimension reduction models 142
Alan E. Gelfand, Sudipto Banerjee and Andrew O. Finley
7. 1 Introduction 142
7. 2 Handling large spatial datasets 145
7. 3 Dimension reduction approaches 146
7. 3. 1 Basic properties of low rank models 146
7. 3. 2 Predictive process models: A brief review 148
7. 4 Some basic knot design ideas 149
7. 4. 1 A brief review of spatial design 149
7. 4. 2 A strategy for selecting knots 151
7. 5 Illustrations 153
7. 5. 1 A simulation example 153
7. 5. 2 A simulation example using the two-step analysis 159
7. 5. 3 Tree height and diameter analysis 160
7. 5. 4 Austria precipitation analysis 162
7. 6 Discussion and future work 165
References 166
8 Exploratory designs for assessing spatial dependence 170
Agnes Fussl, Werner G. Mü ller and Juan Rodrí guez-Dí az
8. 1 Introduction 170
8. 1. 1 The dataset and its visualization 172
8. 2 Spatial links 174
8. 2. 1 Spatial neighbors 175
8. 2. 2 Spatial weights 176
8. 3 Measures of spatial dependence 178
8. 4 Models for areal data 180
8. 4. 1 H0 : A spaceless regression model 181
8. 4. 2 H0 : Spatial regression models 185
8. 5 Design considerations 190
8. 5. 1 A design criterion 192
8. 5. 2 Example 194
8. 6 Discussion 195
Appendix 8. 1: R code 198
Acknowledgments 202
References 203
9 Sampling design optimization for space-time kriging 207
Gerard B. M. Heuvelink, Daniel A. Griffith, Tomislav Hengl and Stephanie J. Melles
9. 1 Introduction 207
9. 2 Methodology 209
9. 2. 1 Space-time universal kriging 209
9. 2. 2 Sampling design optimization with spatial simulated annealing 211
9. 3 Upper Austria case study 212
9. 3. 1 Descriptive statistics 212
9. 3. 2 Estimation of the space-time model and universal kriging 215
9. 3. 3 Optimal design scenario 1 218
9. 3. 4 Optimal design scenario 2 219
9. 3. 5 Optimal design scenario 3 219
9. 4 Discussion and conclusions 221
Appendix 9. 1: R code 222
Acknowledgment 227
References 228
10 Space-time adaptive sampling and data transformations 231
José M. Angulo, Marí a C. Bueso and Francisco J. Alonso
10. 1 Introduction 231
10. 2 Adaptive sampling network design 233
10. 2. 1 A simulated illustration 235
10. 3 Predictive information based on data transformations 238
10. 4 Application to Upper Austria temperature data 242
10. 5 Summary 246
Acknowledgments 247
References 247
11 Adaptive sampling design for spatio-temporal prediction 249
Thomas R. Fanshawe and Peter J. Diggle
11. 1 Introduction 249
11. 2 Review of spatial and spatio-temporal adaptive designs 251
11. 3 The stationary Gaussian model 253
11. 3. 1 Model specification 253
11. 3. 2 Theoretically optimal designs 254
11. 3. 3 A comparison of design strategies 254
11. 4 The dynamic process convolution model 257
11. 4. 1 Model specification 257
11. 4. 2 A comparison of design strategies 258
11. 5 Upper Austria rainfall data example 262
11. 6 Discussion 264
Appendix 11. 1 266
References 267
12 Semiparametric dynamic design of monitoring networks for non-Gaussian spatio-temporal data 269
Scott H. Holan and Christopher K. Wikle
12. 1 Introduction 269
12. 2 Semiparametric non-Gaussian space-time dynamic design 271
12. 2. 1 Semiparametric spatio-temporal dynamic Gamma model 271
12. 2. 2 Simulation-based dynamic design 274
12. 2. 3 Extended Kalman filter for dynamic gamma models 275
12. 2. 4 Extended Kalman filter design algorithm 277
12. 3 Application: Upper Austria precipitation 278
12. 4 Discussion 282
Acknowledgments 282
References 283
13 Active learning for monitoring network optimization 285
Devis Tuia, Alexei Pozdnoukhov, Loris Foresti and Mikhail Kanevski
13. 1 Introduction 285
13. 2 Statistical learning from data 287
13. 2. 1 Algorithmic approaches to learning 288
13. 2. 2 Over-fitting and model selection 288
13. 3 Support vector machines and kernel methods 289
13. 3. 1 Classification: SVMs 290
13. 3. 2 Density estimation: One-class SVM 292
13. 3. 3 Regression: Kernel ridge regression 293
13. 3. 4 Regression: SVR 294
13. 4 Active learning 294
13. 4. 1 A general framework 295
13. 4. 2 First steps in active learning: Reducing output variance 296
13. 4. 3 Exploration-exploitation strategies: Towards mixed approaches 297
13. 5 Active learning with SVMs 297
13. 5. 1 Margin sampling 297
13. 5. 2 Diversity of batches of samples 299
13. 5. 3 Committees of models 299
13. 6 Case studies 300
13. 6. 1 Austrian climatological data 300
13. 6. 2 Cesium-137 concentration after Chernobyl 304
13. 6. 3 Wind power plants sites evaluation 307
13. 7 Conclusions 312
Acknowledgments 314
References 314
14 Stationary sampling designs based on plume simulations 319
Kristina B. Helle and Edzer Pebesma
14. 1 Introduction 319
14. 2 Plumes: From random fields to simulations 320
14. 3 Cost functions 324
14. 3. 1 Detecting plumes 324
14. 3. 2 Mapping and characterising plumes 325
14. 3. 3 Combined cost functions 325
14. 4 Optimisation 326
14. 4. 1 Greedy search 326
14. 4. 2 Spatial simulated annealing 328
14. 4. 3 Genetic algorithms 329
14. 4. 4 Other methods 331
14. 4. 5 Evaluation and sensitivity 331
14. 4. 6 Use case: Combination and comparison of optimisation algorithms 332
14. 5 Results 334
14. 5. 1 Simulations 334
14. 5. 2 Greedy search 335
14. 5. 3 Sensitivity of greedy search to the plume simulations 336
14. 5. 4 Comparison of optimisation algorithms 337
14. 6 Discussion 340
Acknowledgments 341
References 341
Index 345
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Produktdetails
Erscheinungsdatum
17. Dezember 2012
Sprache
englisch
Seitenanzahl
384
Reihe
Statistics in Practice
Herausgegeben von
Jorge Mateu, Werner G Müller
Verlag/Hersteller
Produktart
gebunden
Gewicht
544 g
Größe (L/B/H)
234/157/23 mm
ISBN
9780470974292
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