Plant, Richard E.

Spatial data analysis in ecology and agriculture using R - 2nd - Florida CRC Press 2019 - xvii, 666p. With index

Table of Contents

1 Working with Spatial Data

2 The R Programming Environment

3 Statistical Properties of Spatially Autocorrelated Data

4 Measures of Spatial Autocorrelation

5 Sampling and Data Collection

6 Preparing Spatial Data for Analysis

7 Preliminary Exploration of Spatial Data

8 Data Exploration using Non-Spatial Methods: The Linear Model

9 Data Exploration using Non-Spatial Methods: Nonparametric Methods

10 Variance Estimation, the Effective Sample Size, and the Bootstrap

11 Measures of Bivariate Association between Two Spatial Variables

12 The Mixed Model

13 Regression Models for Spatially Autocorrelated Data

14 Bayesian Analysis of Spatially Autocorrelated Data

15 Analysis of Spatiotemporal Data

16 Analysis of Data from Controlled Experiments

17 Assembling Conclusions

Appendix A: Review of Mathematical Concepts

Appendix B: The Data Sets

Appendix C: An R Thesaurus

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. This new edition
Provides exercises in each chapter to facilitate the book's use as a course textbook or for self-study
Adds new material on generalized additive models, point pattern analysis, and new methods of Bayesian analysis of spatial data.
Includes a completely revised chapter on the analysis of spatiotemporal data featuring recently introduced software and methods
Updates its coverage of R software including newly introduced packages
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.


Agriculture - Statistical methods
R-computer program language
Spatial analysis - Statistics
Spatial analysis

338.10727 / P5S7

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