Spatial Data Analysis in Ecology and Agriculture Using R

Description

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. 　 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