Dale L. Zimmerman is Professor of Statistics at the University of Iowa, and Jay M. Ver Hoef is Senior Scientist and Statistician, Alaska Fisheries Science Center, NOAA Fisheries. Both are Fellows of the American Statistical Association and winners of that association’s Section for Statistics and the Environment Distinguished Achievement Award.

• Preface 1. Introduction 2. An Introduction to Covariance Structures for Spatial Linear Models 3. Exploratory Spatial Data Analysis 4. Provisional Estimation of the Mean Structure by Ordinary Least Squares 5. Generalized Least Squares Estimation of the Mean Structure 6. Parametric Covariance Structures for Geostatistical Models 7. Parametric Covariance Structures for Spatial-Weights Linear Models 8. Likelihood-Based Inference 9. Spatial Prediction 10. Spatial Sampling Design 11. Analysis and Design of Spatial Experiments 12. Extensions Appendix A: Some Matrix Results

"Spatial Linear Models for Environmental Data is a readable, practical, and comprehensive book, covering both the foundation and application of spatial linear models. The authors begin the book with four real data examples, which they revisit regularly as new topics are introduced. Every chapter includes frequent and informative figures and graphics. There is plenty of discussion of the ideas behind the models and analyses. I especially appreciated the chapters on sampling design and design of experiments, since even the best models are useless unless you have informative data."- Lisa Madsen, Professor of Statistics, Oregon State University“[…] I found that the book is a very valuable addition to the literature of spatial statistics. I am likely to recommend it as a reference for my students the next time I teach a spatial statistics class, and I will gratefully draw from it to complement my notes and prepare the tests and homework for the class.”- Bruno Sansó in the Journal of the American Statistical Association, September 2025.

Dale L. Zimmerman is Professor of Statistics at the University of Iowa, and Jay M. Ver Hoef is Senior Scientist and Statistician, Alaska Fisheries Science Center, NOAA Fisheries. Both are Fellows of the American Statistical Association and winners of that association’s Section for Statistics and the Environment Distinguished Achievement Award.

• Preface 1. Introduction 2. An Introduction to Covariance Structures for Spatial Linear Models 3. Exploratory Spatial Data Analysis 4. Provisional Estimation of the Mean Structure by Ordinary Least Squares 5. Generalized Least Squares Estimation of the Mean Structure 6. Parametric Covariance Structures for Geostatistical Models 7. Parametric Covariance Structures for Spatial-Weights Linear Models 8. Likelihood-Based Inference 9. Spatial Prediction 10. Spatial Sampling Design 11. Analysis and Design of Spatial Experiments 12. Extensions Appendix A: Some Matrix Results

"Spatial Linear Models for Environmental Data is a readable, practical, and comprehensive book, covering both the foundation and application of spatial linear models. The authors begin the book with four real data examples, which they revisit regularly as new topics are introduced. Every chapter includes frequent and informative figures and graphics. There is plenty of discussion of the ideas behind the models and analyses. I especially appreciated the chapters on sampling design and design of experiments, since even the best models are useless unless you have informative data."- Lisa Madsen, Professor of Statistics, Oregon State University“[…] I found that the book is a very valuable addition to the literature of spatial statistics. I am likely to recommend it as a reference for my students the next time I teach a spatial statistics class, and I will gratefully draw from it to complement my notes and prepare the tests and homework for the class.”- Bruno Sansó in the Journal of the American Statistical Association, September 2025.

Dale L. Zimmerman is Professor of Statistics at the University of Iowa, and Jay M. Ver Hoef is Senior Scientist and Statistician, Alaska Fisheries Science Center, NOAA Fisheries. Both are Fellows of the American Statistical Association and winners of that association’s Section for Statistics and the Environment Distinguished Achievement Award.

• Preface 1. Introduction 2. An Introduction to Covariance Structures for Spatial Linear Models 3. Exploratory Spatial Data Analysis 4. Provisional Estimation of the Mean Structure by Ordinary Least Squares 5. Generalized Least Squares Estimation of the Mean Structure 6. Parametric Covariance Structures for Geostatistical Models 7. Parametric Covariance Structures for Spatial-Weights Linear Models 8. Likelihood-Based Inference 9. Spatial Prediction 10. Spatial Sampling Design 11. Analysis and Design of Spatial Experiments 12. Extensions Appendix A: Some Matrix Results

"Spatial Linear Models for Environmental Data is a readable, practical, and comprehensive book, covering both the foundation and application of spatial linear models. The authors begin the book with four real data examples, which they revisit regularly as new topics are introduced. Every chapter includes frequent and informative figures and graphics. There is plenty of discussion of the ideas behind the models and analyses. I especially appreciated the chapters on sampling design and design of experiments, since even the best models are useless unless you have informative data."- Lisa Madsen, Professor of Statistics, Oregon State University“[…] I found that the book is a very valuable addition to the literature of spatial statistics. I am likely to recommend it as a reference for my students the next time I teach a spatial statistics class, and I will gratefully draw from it to complement my notes and prepare the tests and homework for the class.”- Bruno Sansó in the Journal of the American Statistical Association, September 2025.