دانلود کتاب Spatio-Temporal Design: Advances in Efficient Data Acquisition

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