Spatial sampling with R
Author(s)
Bibliographic Information
Spatial sampling with R
（The R series）
CRC Press, 2022
- : hardback
Available at 3 libraries
Note
Content Type: text (rdacontent), Media Type: unmediated (rdamedia), Carrier Type: volume (rdacarrier)
Includes bibliographical references (p. 515-528) and index
Description and Table of Contents
Description
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
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
Table of Contents
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
by "Nielsen BookData"