Spatiotemporal Design Advances In Efficient Data Acquisition Jorge Mateu by Jorge Mateu, Werner G. Müller 9780470974292, 9781118441862, 047097429X, 1118441869 instant download after payment.

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.

Content:
Chapter 1 Collecting Spatio?Temporal Data (pages 1–36): Jorge Mateu and Werner G. Muller
Chapter 2 Model?Based Frequentist Design for Univariate and Multivariate Geostatistics (pages 37–53): Dale L. Zimmerman and Jie Li
Chapter 3 Model?Based Criteria Heuristics for Second?Phase Spatial Sampling (pages 54–71): Eric M. Delmelle
Chapter 4 Spatial sampling design by means of spectral approximations to the error process (pages 72–102): Gunter Spock and Jurgen Pilz
Chapter 5 Entropy?Based Network Design Using Hierarchical Bayesian Kriging (pages 103–130): Baisuo Jin, Yuehua Wu and Baiqi Miao
Chapter 6 Accounting for Design in the Analysis of Spatial Data (pages 131–141): Brian J. Reich and Montserrat Fuentes
Chapter 7 Spatial Design for Knot Selection in Knot?Based Dimension Reduction Models (pages 142–169): Alan E. Gelfand, Sudipto Banerjee and Andrew O. Finley
Chapter 8 Exploratory Designs for Assessing Spatial Dependence (pages 170–206): Agnes Fussl, Werner G. Muller and Juan Rodriguez?Diaz
Chapter 9 Sampling Design Optimization for Space?Time Kriging (pages 207–230): Gerard B. M. Heuvelink, Daniel A. Griffith, Tomislav Hengl and Stephanie J. Melles
Chapter 10 Space?Time Adaptive Sampling and Data Transformations (pages 231–248): Jose M. Angulo, Maria C. Bueso and Francisco J. Alonso
Chapter 11 Adaptive Sampling Design for Spatio?Temporal Prediction (pages 249–268): Thomas R. Fanshawe and Peter J. Diggle
Chapter 12 Semiparametric Dynamic Design of Monitoring Networks for Non?Gaussian Spatio?Temporal Data (pages 269–284): Scott H. Holan and Christopher K. Wikle
Chapter 13 Active Learning for Monitoring Network Optimization (pages 285–318): Devis Tuia, Alexei Pozdnoukhov, Loris Foresti and Mikhail Kanevski
Chapter 14 Stationary Sampling Designs Based on Plume Simulations (pages 319–344): Kristina B. Helle and Edzer Pebesma