Dick J. Brus

Spatial Sampling with R (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 548
• Erscheinungstermin: 26. September 2022
• Englisch
• ISBN-13: 9781000600018
• Artikelnr.: 65483323

Autorenporträt

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.

Inhaltsangabe

1 Introduction 2 Introduction to probability sampling 3 Simple random
sampling 4 Stratified simple random sampling 5 Systematic random sampling 6
Cluster random sampling 7 Two-stage cluster random sampling 8 Sampling with
probabilities proportional to size 9 Balanced and well-spread sampling 10
Model-assisted estimation 11 Two-phase random sampling 12 Computing the
required sample size 13 Model-based optimisation of probability sampling
designs 14 Sampling for estimating parameters of (small) domains 15
Repeated sample surveys for monitoring population parameters 16
Introduction to sampling for mapping 17 Regular grid and spatial coverage
sampling 18 Covariate space coverage sampling 19 Conditioned Latin
hypercube sampling 20 Spatial response surface sampling 21 Introduction to
kriging 22 Model-based optimisation of the grid spacing 23 Model-based
optimisation of the sampling pattern 24 Sampling for estimating the
semivariogram 25 Sampling for validation of maps 26 Design-based,
model-based, and model-assisted approach for sampling and inference