Assuming no prior knowledge of R, Spatial Data Analysis in Ecology and Agriculture Using R provides practical instruction on the use of the R programming language to analyze spatial data arising from research in ecology and agriculture. Written in terms of four data sets easily accessible online, this 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. Based on the author’s spatial data analysis course at the University of California, Davis, the book is intended for classroom use or self-study by graduate students and researchers in ecology, geography, and agricultural science with an interest in the analysis of spatial data.

Table of Contents:
Working with Spatial Data Analysis of Spatial Data Data Sets Analyzed in This Book R Programming Environment R Basics Programming Concepts Handling Data in R Writing Functions in R Graphics in R Other Software Packages Statistical Properties of Spatially Autocorrelated Data Components of a Spatial Random Process Monte Carlo Simulation Review of Hypothesis and Significance Testing Modeling Spatial Autocorrelation Application to Field Data Measures of Spatial Autocorrelation Preliminary Considerations Join-Count Statistics Moran’s I and Geary’s c Measures of Autocorrelation Structure Measuring Autocorrelation of Spatially Continuous Data Sampling and Data Collection Preliminary Considerations Developing the Sampling Patterns Methods for Variogram Estimation Estimating the Sample Size Sampling for Thematic Mapping Design-Based and Model-Based Sampling Preparing Spatial Data for Analysis Quality of Attribute Data Spatial Interpolation Procedures Spatial Rectification and Alignment of Data Preliminary Exploration of Spatial Data Data Set 1 Data Set 2 Data Set 3 Data Set 4 Multivariate Methods for Spatial Data Exploration Principal Components Analysis Classification and Regression Trees (aka Recursive Partitioning) Random Forest Spatial Data Exploration via Multiple Regression Multiple Linear Regression Building a Multiple Regression Model for Field 4.1 Generalized Linear Models Variance Estimation, the Effective Sample Size, and the Bootstrap Bootstrap Estimation of the Standard Error Bootstrapping Time Series Data Bootstrapping Spatial Data Application to the EM38 Data Measures of Bivariate Association between Two Spatial Variables Estimating and Testing the Correlation Coefficient Contingency Tables Mantel and Partial Mantel Statistics Modifiable Areal Unit Problem and Ecological Fallacy Mixed Model Basic Properties of the Mixed Model Application to Data Set 3 Incorporating Spatial Autocorrelation Generalized Least Squares Spatial Logistic Regression Regression Models for Spatially Autocorrelated Data Detecting Spatial Autocorrelation in a Regression Model Models for Spatial Processes Determining the Appropriate Regression Model Fitting the Spatial Lag and Spatial Error Models Conditional Autoregressive Model Application of SAR and CAR Models to Field Data Autologistic Model for Binary Data Bayesian Analysis of Spatially Autocorrelated Data Markov Chain Monte Carlo Methods Introduction to WinBUGS Hierarchical Models Incorporation of Spatial Effects Analysis of Spatiotemporal Data Spatiotemporal Cluster Analysis Factors Underlying Spatiotemporal Yield Clusters Bayesian Spatiotemporal Analysis Other Approaches to Spatiotemporal Modeling Analysis of Data from Controlled Experiments Classical Analysis of Variance Comparison of Methods Pseudoreplicated Data and the Effective Sample Size Assembling Conclusions Data Set 1 Data Set 2 Data Set 3 Data Set 4 Conclusions Appendices Review of Mathematical Concepts The Data Sets An R Thesaurus References Index