ABSTRACT

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.

chapter Chapter 1|16 pages

Introduction

part I|298 pages

Probability sampling for estimating population parameters

chapter 18Chapter 2|8 pages

Introduction to probability sampling

chapter Chapter 3|22 pages

Simple random sampling

chapter Chapter 4|30 pages

Stratified simple random sampling

chapter Chapter 5|14 pages

Systematic random sampling

chapter Chapter 6|16 pages

Cluster random sampling

chapter Chapter 7|14 pages

Two-stage cluster random sampling

chapter Chapter 8|14 pages

Sampling with probabilities proportional to size

chapter Chapter 9|24 pages

Balanced and well-spread sampling

chapter Chapter 10|40 pages

Model-assisted estimation

chapter Chapter 11|10 pages

Two-phase random sampling

chapter Chapter 12|20 pages

Computing the required sample size

chapter Chapter 13|32 pages

Model-based optimisation of probability sampling designs

chapter Chapter 14|26 pages

Sampling for estimating parameters of domains

part II|180 pages

Sampling for mapping

chapter 316Chapter 16|8 pages

Introduction to sampling for mapping

chapter Chapter 17|10 pages

Regular grid and spatial coverage sampling

chapter Chapter 18|10 pages

Covariate space coverage sampling

chapter Chapter 19|12 pages

Conditioned Latin hypercube sampling

chapter Chapter 20|18 pages

Spatial response surface sampling

chapter Chapter 21|22 pages

Introduction to kriging

chapter Chapter 22|16 pages

Model-based optimisation of the grid spacing

chapter Chapter 23|16 pages

Model-based optimisation of the sampling pattern

chapter Chapter 24|30 pages

Sampling for estimating the semivariogram

chapter Chapter 25|14 pages

Sampling for validation of maps