Spatio-Temporal Design -

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Editeur : John Wiley & Sons

Langue : Anglais

Parution : 01/12/2012

Format : Moyen, de 350g à 1kg

Nombre de pages : 384

Expédition : 544

Dimensions : 23.4 x 15.7 x 2.3

ISBN : 047097429X

Résumé :

A 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.

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Biographie:

Jorge Mateu, Department of Mathematics of the University Jaume I of Castellon, Spain,

Werner G. M?ller, Department of Applied Statistics, Johannes Kepler University Linz, Austria.

Sommaire:

Contributors xv

Foreword xix

1 Collecting spatio-temporal data 1
Jorge Mateu and Werner G. M?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 Sp?ck and J?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...