This book provides practical instruction on the use of the R programming language to analyze spatial data arising from research in ecology and agriculture. The book is written in terms of the analysis of four data sets, two from ecology and two from agriculture. These data sets are available online. It guides readers through the analysis of each of these data sets, including setting the research objective, designing the sampling plan, data quality control, exploratory and confirmatory data analysis, and drawing scientific conclusions. All of the R code described in the book is made available online.
- 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
- Multivariate Methods for Spatial Data Exploration
- Spatial Data Exploration via Multiple Regression
- 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