Spatial Sampling with R

<p>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. <b>Spatial Sampling with R</b> 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.</p><p><b>Key features</b></p><ul> <li>Describes classical basic sampling designs for spatial survey as well as recently developed advanced sampling designs and estimators</li> <li>Presents probability sampling designs for estimating parameters for a (sub)population as well as non-probability sampling designs for mapping</li> <li>Gives comprehensive overview of model-assisted estimators</li> <li>Covers Bayesian approach to sampling design</li> <li>Illustrates sampling designs with surveys of soil organic carbon above-ground biomass air temperature opium poppy</li> <li>Explains integration of wall-to-wall data sets (e.g. remote sensing images) and sample data</li> <li>Data and R code available on github</li> <li>Exercises added making the book suitable as a textbook for students</li> </ul><p>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.</p>