Richard E. 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.

"Given the widespread availability of large, georeferenced datasets, now is the time for analysts to consider specialized statistical techniques to accommodate spatial autocorrelation and reach valid conclusions about relationships between various attributes. Richard Plant’s third edition of “Spatial Analysis in Ecology and Agriculture using R” revisits the research progress in the field of spatial statistics, covering a variety of modeling approaches that will prove to be highly useful to the intelligent non-specialist. Methods are explained in great detail and illustrated with updated R computer code and results from several different application areas. Scientists, researchers, and related professionals who work with georeferenced data will benefit greatly from this highly instructive, readable book."Dan S. Long, Research Agronomist (Emeritus), USDA-ARS Soil and Water Conservation Research, USA

• 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. Acquisition and Analysis of Remotely Sensed Data 7. Preparing Spatial Data for Analysis 8. Preliminary Exploration of Spatial Data 9. Non-Spatial Methods: Linear and Additive Models 10. Variance Estimation, the Effective Sample Size, and the Bootstrap 11. Measures of Bivariate Association between Two Spatial Variables 12. Machine Learning Methods 1: Recursive Partitioning 13. Machine Learning Methods 2: Supervised Classification Methods: 14. The Mixed Model 15. Regression Models for Spatially Autocorrelated Data 16. Assembling Conclusions. Appendix A. Review of Mathematical Concepts

Richard E. 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.

"Given the widespread availability of large, georeferenced datasets, now is the time for analysts to consider specialized statistical techniques to accommodate spatial autocorrelation and reach valid conclusions about relationships between various attributes. Richard Plant’s third edition of “Spatial Analysis in Ecology and Agriculture using R” revisits the research progress in the field of spatial statistics, covering a variety of modeling approaches that will prove to be highly useful to the intelligent non-specialist. Methods are explained in great detail and illustrated with updated R computer code and results from several different application areas. Scientists, researchers, and related professionals who work with georeferenced data will benefit greatly from this highly instructive, readable book."Dan S. Long, Research Agronomist (Emeritus), USDA-ARS Soil and Water Conservation Research, USA

• 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. Acquisition and Analysis of Remotely Sensed Data 7. Preparing Spatial Data for Analysis 8. Preliminary Exploration of Spatial Data 9. Non-Spatial Methods: Linear and Additive Models 10. Variance Estimation, the Effective Sample Size, and the Bootstrap 11. Measures of Bivariate Association between Two Spatial Variables 12. Machine Learning Methods 1: Recursive Partitioning 13. Machine Learning Methods 2: Supervised Classification Methods: 14. The Mixed Model 15. Regression Models for Spatially Autocorrelated Data 16. Assembling Conclusions. Appendix A. Review of Mathematical Concepts

Richard E. 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.

"Given the widespread availability of large, georeferenced datasets, now is the time for analysts to consider specialized statistical techniques to accommodate spatial autocorrelation and reach valid conclusions about relationships between various attributes. Richard Plant’s third edition of “Spatial Analysis in Ecology and Agriculture using R” revisits the research progress in the field of spatial statistics, covering a variety of modeling approaches that will prove to be highly useful to the intelligent non-specialist. Methods are explained in great detail and illustrated with updated R computer code and results from several different application areas. Scientists, researchers, and related professionals who work with georeferenced data will benefit greatly from this highly instructive, readable book."Dan S. Long, Research Agronomist (Emeritus), USDA-ARS Soil and Water Conservation Research, USA

• 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. Acquisition and Analysis of Remotely Sensed Data 7. Preparing Spatial Data for Analysis 8. Preliminary Exploration of Spatial Data 9. Non-Spatial Methods: Linear and Additive Models 10. Variance Estimation, the Effective Sample Size, and the Bootstrap 11. Measures of Bivariate Association between Two Spatial Variables 12. Machine Learning Methods 1: Recursive Partitioning 13. Machine Learning Methods 2: Supervised Classification Methods: 14. The Mixed Model 15. Regression Models for Spatially Autocorrelated Data 16. Assembling Conclusions. Appendix A. Review of Mathematical Concepts