Spatial Data Analysis in Ecology and Agriculture Using R, 2nd Edition provides practical instruction on the use of the R programming language to analyze spatial data arising from research in ecology, agriculture, and environmental science. Readers have praised the book's practical coverage of spatial statistics, real-world examples, and user-friendly approach in presenting and explaining R code, aspects maintained in this update. Using data sets from cultivated and uncultivated ecosystems, the book guides the reader through the analysis of each data set, including setting research objectives, designing the sampling plan, data quality control, exploratory and confirmatory data analysis, and drawing scientific conclusions.
Additional material to accompany the book, on both analyzing satellite data and on multivariate analysis, can be accessed at https://www.plantsciences.ucdavis.edu/plant/additionaltopics.htm.
Author(s): Richard E. Plant
Edition: 2nd
Publisher: CRC Press
Year: 2019
Language: English
Pages: 685
Tags: Statistics, Spatial Data, R
Working with Spatial Data
The R Programming Environment
Statistical Properties of Spatially Autocorrelated Data
Measures of Spatial Autocorrelation
Sampling and Data Collection
Preparing Spatial Data for Analysis
Preliminary Exploration of Spatial Data
Data Exploration using Non-Spatial Methods: The Linear Model
Data Exploration using Non-Spatial Methods: Nonparametric Methods
Variance Estimation, the Effective Sample Size, and the Bootstrap
Measures of Bivariate Association between Two Spatial Variables
The Mixed Model
Regression Models for Spatially Autocorrelated Data
Bayesian Analysis of Spatially Autocorrelated Data
Analysis of Spatiotemporal Data
Analysis of Data from Controlled Experiments
Assembling Conclusions
Appendix A: Review of Mathematical Concepts
Appendix B: The Data Sets
Appendix C: An R Thesaurus
