Spatio-temporal Design: Advances in Efficient Data Acquisition

Spatio-temporal Design: Advances in Efficient Data Acquisition (Statistics in Practice)

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.

Table of Contents

1 Collecting spatio-temporal data 1
2 Model-based frequentist design for univariate and multivariate geostatistics 37
3 Model-based criteria heuristics for second-phase spatial sampling 54
5 Entropy-based network design using hierarchical Bayesian kriging 103
6 Accounting for design in the analysis of spatial data 131
7 Spatial design for knot selection in knot-based dimension reduction models 142
8 Exploratory designs for assessing spatial dependence 170
9 Sampling design optimization for space-time kriging 207
10 Space-time adaptive sampling and data transformations 231
11 Adaptive sampling design for spatio-temporal prediction 249
12 Semiparametric dynamic design of monitoring networks for non-Gaussian spatio-temporal data 269
13 Active learning for monitoring network optimization 285
14 Stationary sampling designs based on plume simulations 319