Incluye bibliografía: páginas 599-617 e índice: páginas 619-631

Preface.. Acknowledgments.. Author.. 1. Working with Spatial Data.. 2. 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. Multivariate Methods for Spatial Data Exploration.. 9. Spatial Data Exploration via Multiple Regression.. 10. Variance Estimation, the Effective Sample Size, and the Bootstrap.. 11. Measures of Bivariate Association between Two Spatial Variables.. 12. 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.. References.. Index

Disponible para usuarios de ECOSUR con su clave de acceso

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. Inglés

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