Spatial Data Analysis in Ecology and Agriculture Using R
eBook - ePub

Spatial Data Analysis in Ecology and Agriculture Using R

  1. 666 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Spatial Data Analysis in Ecology and Agriculture Using R

About this book

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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Yes, you can access Spatial Data Analysis in Ecology and Agriculture Using R by Richard E. Plant in PDF and/or ePUB format, as well as other popular books in Biological Sciences & Biology. We have over one million books available in our catalogue for you to explore.

Information

Publisher
CRC Press
Year
2018
eBook ISBN
9781351189897
Topic
Biological Sciences
Subtopic
Biology
Index
Biological Sciences

Table of contents

  1. Cover
  2. Half Title
  3. Title Page
  4. Copyright Page
  5. Dedication
  6. Table of Contents
  7. Preface to the First Edition
  8. Preface to the Second Edition
  9. Author
  10. 1. Working with Spatial Data
  11. 2. The R Programming Environment
  12. 3. Statistical Properties of Spatially Autocorrelated Data
  13. 4. Measures of Spatial Autocorrelation
  14. 5. Sampling and Data Collection
  15. 6. Preparing Spatial Data for Analysis
  16. 7. Preliminary Exploration of Spatial Data
  17. 8. Data Exploration Using Non-Spatial Methods: The Linear Model
  18. 9. Data Exploration Using Non-Spatial Methods: Nonparametric Methods
  19. 10. Variance Estimation, the Effective Sample Size, and the Bootstrap
  20. 11. Measures of Bivariate Association between Two Spatial Variables
  21. 12. The Mixed Model
  22. 13. Regression Models for Spatially Autocorrelated Data
  23. 14. Bayesian Analysis of Spatially Autocorrelated Data
  24. 15. Analysis of Spatiotemporal Data
  25. 16. Analysis of Data from Controlled Experiments
  26. 17. Assembling Conclusions
  27. Appendix A: Review of Mathematical Concepts
  28. Appendix B: The Data Sets
  29. Appendix C: An R Thesaurus
  30. References
  31. Index