Detailansicht
Spatio-temporal Design
Advances in Efficient Data Acquisition, Statistics in Practice
ISBN/EAN: 9780470974292
Umbreit-Nr.: 4032731
Sprache:
Englisch
Umfang: 370 S.
Format in cm:
Einband:
gebundenes Buch
Erschienen am 16.11.2012
Auflage: 1/2012
- Zusatztext
- 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. Spatiotemporal Design presents a comprehensive stateoftheart presentation combining both classical and modern treatments of network design and planning for spatial and spatiotemporal 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. Spatiotemporal 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. Spatiotemporal 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.
- Kurztext
- 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. Spatiotemporal Design presents a comprehensive stateoftheart presentation combining both classical and modern treatments of network design and planning for spatial and spatiotemporal 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. Spatiotemporal 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. Spatiotemporal 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.
- Autorenportrait
- InhaltsangabeContributors xv Foreword xix 1 Collecting spatio-temporal data 1 Jorge Mateu and Werner G. Muller 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 Spatiotemporal kriging 8 1.4.4 Spatiotemporal 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 Modelbased 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 Datamodel 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 Datamodel 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 Modelbased criteria heuristics for secondphase 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 Spock and Jurgen 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 Entropybased network design using hierarchical Bayesian kriging 103 Baisuo Jin, Yuehua Wu and Baiqi Miao 5.1 Introduction 103 5.2 Entropybased network design using hierarchical Bayesian kriging 105 5.3 The data 107 5.4 Spatiotemporal modeling 107 5.5 Obtaining a staircase data structure 111 5.6 Estimating the hyperparameters Hg 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 twostage approach for informative sampling 136 6.3 Analysis of the Austrian precipitation data 137 6.4 Discussion 139