Spatial Sampling with R
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Book Description
Scientific research often starts with data collection. However, many researchers pay insufficient attention to this first step in their research. The author, researcher at Wageningen University and Research, often had to conclude that the data collected by fellow researchers were suboptimal, or in some cases even unsuitable for their aim. One reason is that sampling is frequently overlooked in statistics courses. Another reason is the lack of practical textbooks on sampling. Numerous books have been published on the statistical analysis and modelling of data using R, but to date no book has been published in this series on how these data can best be collected. This book fills this gap. Spatial Sampling with R presents an overview of sampling designs for spatial sample survey and monitoring. It shows how to implement the sampling designs and how to estimate (sub)population- and space-time parameters in R.
Key features
- Describes classical, basic sampling designs for spatial survey, as well as recently developed, advanced sampling designs and estimators
- Presents probability sampling designs for estimating parameters for a (sub)population, as well as non-probability sampling designs for mapping
- Gives comprehensive overview of model-assisted estimators
- Covers Bayesian approach to sampling design
- Illustrates sampling designs with surveys of soil organic carbon, above-ground biomass, air temperature, opium poppy
- Explains integration of wall-to-wall data sets (e.g. remote sensing images) and sample data
- Data and R code available on github
- Exercises added making the book suitable as a textbook for students
The target group of this book are researchers and practitioners of sample surveys, as well as students in environmental, ecological, agricultural science or any other science in which knowledge about a population of interest is collected through spatial sampling. This book helps to implement proper sampling designs, tailored to their problems at hand, so that valuable data are collected that can be used to answer the research questions.
Table of Contents
Preface
Chapter 1 Introduction
Chapter 2 Introduction to probability sampling
Chapter 3 Simple random sampling
Chapter 4 Stratified simple random sampling
Chapter 5 Systematic random sampling
Chapter 6 Cluster random sampling
Chapter 7 Two-stage cluster random sampling
Chapter 8 Sampling with probabilities proportional to size
Chapter 9 Balanced and well-spread sampling
Chapter 10 Model-assisted estimation
Chapter 11 Two-phase random sampling
Chapter 12 Computing the required sample size
Chapter 13 Model-based optimisation of probability sampling designs
Chapter 14 Sampling for estimating parameters of (small) domains
Chapter 15 Repeated sample surveys for monitoring population parameters
Chapter 16 Introduction to sampling for mapping
Chapter 17 Regular grid and spatial coverage sampling
Chapter 18 Covariate space coverage sampling
Chapter 19 Conditioned Latin hypercube sampling
Chapter 20 Spatial response surface sampling
Chapter 21 Introduction to kriging
Chapter 22 Model-based optimisation of the grid spacing
Chapter 23 Model-based optimisation of the sampling pattern
Chapter 24 Sampling for estimating the semivariogram
Chapter 25 Sampling for validation of maps
Chapter 26 Design-based, model-based, and model-assisted approach for sampling and inference
Answers to Exercises
Bibliography
Index
Author(s)
Biography
Dick J. Brus worked as a researcher and statistics teacher at the Wageningen University and Research (Netherlands) for 38 years. His main fields of interest are sampling theory and geostatistics. He has gathered rich research experience in developing and applying statistical methods for natural resources inventory and monitoring. In 2015, he was appointed Adjunct Professor at Nanjing Normal University, Nanjing, China. He has published about 100 papers in peer-reviewed, international journals. He is second co-author of the book 'Sampling for Natural Resource Monitoring', published in 2006 by Springer. This book is widely acclaimed in soil, earth, environmental, agricultural and statistical science. Since January 1, 2022 he is retired and lives a joyful life in the countryside, where he grows vegetables in the garden, goes on cycling tours and sings in a choir. Every now and then, during rainy days, he works as a private contractor for the sole proprietorship Spatial Sampling registered with the Chamber of Commerce in the Netherlands.