Spatial Data Analysis in Ecology and Agriculture Using R
Key features:

  • Unique in its combination of serving as an introduction to spatial statistics and to modeling agricultural and ecological data using R
  • 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

 

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.

1104954344
Spatial Data Analysis in Ecology and Agriculture Using R
Key features:

  • Unique in its combination of serving as an introduction to spatial statistics and to modeling agricultural and ecological data using R
  • 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

 

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.

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Spatial Data Analysis in Ecology and Agriculture Using R

Spatial Data Analysis in Ecology and Agriculture Using R

by Richard E. Plant
Spatial Data Analysis in Ecology and Agriculture Using R

Spatial Data Analysis in Ecology and Agriculture Using R

by Richard E. Plant

Hardcover(3rd ed.)

$300.00 
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    Available for Pre-Order. This item will be released on January 28, 2026

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Overview

Key features:

  • Unique in its combination of serving as an introduction to spatial statistics and to modeling agricultural and ecological data using R
  • 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

 

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.


Product Details

ISBN-13: 9781032935331
Publisher: CRC Press
Publication date: 01/28/2026
Edition description: 3rd ed.
Pages: 520
Product dimensions: 7.00(w) x 10.00(h) x (d)

About the Author

Richard Plant is a Professor Emeritus of Plant Sciences and Biological and Agricultural Engineering at the University of California, Davis. He is the co-author of Knowledge-Based Systems in Agriculture and is a former Editor-in-Chief of Computers and Electronics in Agriculture and Associate Editor of Precision Agriculture. He has published extensively on applications of crop modeling, expert systems, spatial statistics, remote sensing, and geographic information systems to problems in crop production and natural resource management.

Table of Contents

Working with Spatial Data. 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. Using Non-Spatial Methods to Explore Spatial Data. Variance Estimation, the Effective Sample Size, and the Bootstrap. Measures of Bivariate Association between Two Spatial Variables. 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. Appendices. Review of Mathematical Concepts. The Data Sets. An R Thesaurus. References.
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