Spatial Data Analysis With R

Danlin Yu - Montclair State University, USA
Advanced Quantitative Techniques in the Social Sciences
Spatial Data Analysis With R
March 2025 | 416 pages | Sage US
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Description

This is an introduction for social science students to the growing field of spatial data analysis using the R platform. The text assumes no prior knowledge of either, beyond the contents of an introductory statistics course. It uses the open-source software R, and relevant spatial data analysis packages, to provide practical guidance of how to conduct spatial data analysis with readers' own data sets. The book first briefly introduces students to R, covers some basic concepts in statistical data analysis, and then focuses on discussing the central ideas of spatial data analysis. All the discussions are supported with R scripts so that students can work on their own and produce results that the book helps interpret. Each chapter ends with review questions to test understanding. The book is suited for upper-level undergraduate social science students and graduate students, and other social scientists who are interested in analyzing their spatial data with R.

A companion website for the book can be found on the Resources tab above. It includes R code and data for students to replicate the examples in the book. The password-protected instructor side of the site includes exercises and answers which can be set for homework.




Contents

Preface

Preface

Acknowledgments

Acknowledgments

About the Author

  • Chapter 1. The Journey Starts With R
  • 1.1 What Is R, and Why Should We Use R?
  • 1.2 Getting and Familiarizing Yourselves With R
  • 1.3 The Two Companions of R
  • 1.4 Basic Operations in R
  • 1.5 The R Packages
  • 1.6 The R Task Views and Spatial Task View
  • Conclusion
  • Review Questions
  • Chapter 2. Very Basic Concepts of Statistical Data Analysis
  • 2.1 The Concepts of Variable, Random Variable and Variable Distribution, and Degrees of Freedom
  • 2.2 The Concept of Hypothesis Testing
  • 2.3 Exploratory Data Analysis
  • 2.4 Have a Taste of Regression Analysis
  • 2.5 Practices in R
  • Review Questions
  • Chapter 3. Spatial Data is Special: Working With the Complexity of Spatial Data
  • 3.1 Spatial/Geographical/Map Data—Recognize Them
  • 3.2 Spatial Data is Special—Spatial Effects
  • 3.3 Spatial Data Analysis
  • 3.4 Spatial Effects’ Impact on Data Analysis
  • 3.5 Exploratory Spatial Data Analysis
  • 3.6 Quantifying Spatial Autocorrelation—Essence of ESDA
  • 3.7 Practice in R
  • Review Questions
  • Chapter 4. The Concept of Neighbor: Spatial Linkage Matrix and Spatial Weight
  • 4.1 Second Contact: Spatial Autocorrelation
  • 4.2 Spatial Neighbors—Are You My Neighbor?
  • 4.3 Spatial Weight and Spatial Lag Revisit
  • 4.4 Practice in R
  • Review Questions
  • Chapter 5. Global Spatial Autocorrelation
  • 5.1 Third Contact: Spatial Autocorrelation: The Global and Local Versions
  • 5.2 Introducing the Moran’s Index (Coefficient)
  • 5.3 Practice in R
  • Review Questions
  • Chapter 6. Local Spatial Autocorrelation
  • 6.1 Global and Local: What Is Their Relationship
  • 6.2 The Local Moran’s Index
  • 6.3 Global and Local Again: The Moran’s Scatterplot
  • 6.4 Practice in R
  • Review Questions
  • Chapter 7. Spatial Autoregressive Models
  • 7.1 Regression With Spatial Data
  • 7.2 Taxonomy of Spatial Autoregressive Models as Alternative
  • 7.3 Practice in R
  • Review Questions
  • Chapter 8. Eigenfunction-Based Spatial Filtering Regression
  • 8.1 Fourth Contact: Spatial Autocorrelation
  • 8.2 Spatial Autocorrelation as Map Pattern
  • 8.3 Augmented Regression With Spatial Filters as Synthetic Covariates
  • 8.4 Practice in R
  • Review Questions
  • Chapter 9. Introduction to Local Models: Geographically Weighted Regression and Eigenfunction-Based Spatial Filtering Approach
  • 9.1 Global and Local Regression
  • 9.2 Geographically Weighted Regression (GWR)
  • 9.3 Eigenfunction-Based Spatial Filtering Approach to Addressing Spatial Nonstationarity
  • 9.4 Comparison Between GWR and ESF SVC Models
  • 9.5 Practice in R
  • Review Questions
  • Chapter 10. Brief Introduction to Spatial Panel Regression and SVC Panel Regression
  • 10.1 Panel Dataset and Panel Regression
  • 10.2 Spatial Panel Models
  • 10.3 Spatially Varying Coefficient Process With Panel Model
  • 10.4 Practice in R
  • Review Questions
  • Chapter 11. Conclusion
  • 11.1 Journey So Far
  • 11.2 Future Learning Directions

Appendix: Answers to Review Questions

Appendix: Answers to Review Questions

References

References

Index

Index

Additional materials

Description

This is an introduction for social science students to the growing field of spatial data analysis using the R platform. The text assumes no prior knowledge of either, beyond the contents of an introductory statistics course. It uses the open-source software R, and relevant spatial data analysis packages, to provide practical guidance of how to conduct spatial data analysis with readers' own data sets. The book first briefly introduces students to R, covers some basic concepts in statistical data analysis, and then focuses on discussing the central ideas of spatial data analysis. All the discussions are supported with R scripts so that students can work on their own and produce results that the book helps interpret. Each chapter ends with review questions to test understanding. The book is suited for upper-level undergraduate social science students and graduate students, and other social scientists who are interested in analyzing their spatial data with R.

A companion website for the book can be found on the Resources tab above. It includes R code and data for students to replicate the examples in the book. The password-protected instructor side of the site includes exercises and answers which can be set for homework.




Contents

Preface

Preface

Acknowledgments

Acknowledgments

About the Author

  • Chapter 1. The Journey Starts With R
  • 1.1 What Is R, and Why Should We Use R?
  • 1.2 Getting and Familiarizing Yourselves With R
  • 1.3 The Two Companions of R
  • 1.4 Basic Operations in R
  • 1.5 The R Packages
  • 1.6 The R Task Views and Spatial Task View
  • Conclusion
  • Review Questions
  • Chapter 2. Very Basic Concepts of Statistical Data Analysis
  • 2.1 The Concepts of Variable, Random Variable and Variable Distribution, and Degrees of Freedom
  • 2.2 The Concept of Hypothesis Testing
  • 2.3 Exploratory Data Analysis
  • 2.4 Have a Taste of Regression Analysis
  • 2.5 Practices in R
  • Review Questions
  • Chapter 3. Spatial Data is Special: Working With the Complexity of Spatial Data
  • 3.1 Spatial/Geographical/Map Data—Recognize Them
  • 3.2 Spatial Data is Special—Spatial Effects
  • 3.3 Spatial Data Analysis
  • 3.4 Spatial Effects’ Impact on Data Analysis
  • 3.5 Exploratory Spatial Data Analysis
  • 3.6 Quantifying Spatial Autocorrelation—Essence of ESDA
  • 3.7 Practice in R
  • Review Questions
  • Chapter 4. The Concept of Neighbor: Spatial Linkage Matrix and Spatial Weight
  • 4.1 Second Contact: Spatial Autocorrelation
  • 4.2 Spatial Neighbors—Are You My Neighbor?
  • 4.3 Spatial Weight and Spatial Lag Revisit
  • 4.4 Practice in R
  • Review Questions
  • Chapter 5. Global Spatial Autocorrelation
  • 5.1 Third Contact: Spatial Autocorrelation: The Global and Local Versions
  • 5.2 Introducing the Moran’s Index (Coefficient)
  • 5.3 Practice in R
  • Review Questions
  • Chapter 6. Local Spatial Autocorrelation
  • 6.1 Global and Local: What Is Their Relationship
  • 6.2 The Local Moran’s Index
  • 6.3 Global and Local Again: The Moran’s Scatterplot
  • 6.4 Practice in R
  • Review Questions
  • Chapter 7. Spatial Autoregressive Models
  • 7.1 Regression With Spatial Data
  • 7.2 Taxonomy of Spatial Autoregressive Models as Alternative
  • 7.3 Practice in R
  • Review Questions
  • Chapter 8. Eigenfunction-Based Spatial Filtering Regression
  • 8.1 Fourth Contact: Spatial Autocorrelation
  • 8.2 Spatial Autocorrelation as Map Pattern
  • 8.3 Augmented Regression With Spatial Filters as Synthetic Covariates
  • 8.4 Practice in R
  • Review Questions
  • Chapter 9. Introduction to Local Models: Geographically Weighted Regression and Eigenfunction-Based Spatial Filtering Approach
  • 9.1 Global and Local Regression
  • 9.2 Geographically Weighted Regression (GWR)
  • 9.3 Eigenfunction-Based Spatial Filtering Approach to Addressing Spatial Nonstationarity
  • 9.4 Comparison Between GWR and ESF SVC Models
  • 9.5 Practice in R
  • Review Questions
  • Chapter 10. Brief Introduction to Spatial Panel Regression and SVC Panel Regression
  • 10.1 Panel Dataset and Panel Regression
  • 10.2 Spatial Panel Models
  • 10.3 Spatially Varying Coefficient Process With Panel Model
  • 10.4 Practice in R
  • Review Questions
  • Chapter 11. Conclusion
  • 11.1 Journey So Far
  • 11.2 Future Learning Directions

Appendix: Answers to Review Questions

Appendix: Answers to Review Questions

References

References

Index

Index

Additional materials

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Spatial Data Analysis With R

Danlin Yu
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March 2025 | 416 pages | Sage US

Format Published Date ISBN Price

This is an introduction for social science students to the growing field of spatial data analysis using the R platform. The text assumes no prior knowledge of either, beyond the contents of an introductory statistics course. It uses the open-source software R, and relevant spatial data analysis packages, to provide practical guidance of how to conduct spatial data analysis with readers' own data sets. The book first briefly introduces students to R, covers some basic concepts in statistical data analysis, and then focuses on discussing the central ideas of spatial data analysis. All the discussions are supported with R scripts so that students can work on their own and produce results that the book helps interpret. Each chapter ends with review questions to test understanding. The book is suited for upper-level undergraduate social science students and graduate students, and other social scientists who are interested in analyzing their spatial data with R.

A companion website for the book can be found on the Resources tab above. It includes R code and data for students to replicate the examples in the book. The password-protected instructor side of the site includes exercises and answers which can be set for homework.





Table Of Contents:

  • Preface
  • Acknowledgments
  • About the Author
  • Chapter 1. The Journey Starts With R
  • 1.1 What Is R, and Why Should We Use R?
  • 1.2 Getting and Familiarizing Yourselves With R
  • 1.3 The Two Companions of R
  • 1.4 Basic Operations in R
  • 1.5 The R Packages
  • 1.6 The R Task Views and Spatial Task View
  • Conclusion
  • Review Questions
  • Chapter 2. Very Basic Concepts of Statistical Data Analysis
  • 2.1 The Concepts of Variable, Random Variable and Variable Distribution, and Degrees of Freedom
  • 2.2 The Concept of Hypothesis Testing
  • 2.3 Exploratory Data Analysis
  • 2.4 Have a Taste of Regression Analysis
  • 2.5 Practices in R
  • Review Questions
  • Chapter 3. Spatial Data is Special: Working With the Complexity of Spatial Data
  • 3.1 Spatial/Geographical/Map Data—Recognize Them
  • 3.2 Spatial Data is Special—Spatial Effects
  • 3.3 Spatial Data Analysis
  • 3.4 Spatial Effects’ Impact on Data Analysis
  • 3.5 Exploratory Spatial Data Analysis
  • 3.6 Quantifying Spatial Autocorrelation—Essence of ESDA
  • 3.7 Practice in R
  • Review Questions
  • Chapter 4. The Concept of Neighbor: Spatial Linkage Matrix and Spatial Weight
  • 4.1 Second Contact: Spatial Autocorrelation
  • 4.2 Spatial Neighbors—Are You My Neighbor?
  • 4.3 Spatial Weight and Spatial Lag Revisit
  • 4.4 Practice in R
  • Review Questions
  • Chapter 5. Global Spatial Autocorrelation
  • 5.1 Third Contact: Spatial Autocorrelation: The Global and Local Versions
  • 5.2 Introducing the Moran’s Index (Coefficient)
  • 5.3 Practice in R
  • Review Questions
  • Chapter 6. Local Spatial Autocorrelation
  • 6.1 Global and Local: What Is Their Relationship
  • 6.2 The Local Moran’s Index
  • 6.3 Global and Local Again: The Moran’s Scatterplot
  • 6.4 Practice in R
  • Review Questions
  • Chapter 7. Spatial Autoregressive Models
  • 7.1 Regression With Spatial Data
  • 7.2 Taxonomy of Spatial Autoregressive Models as Alternative
  • 7.3 Practice in R
  • Review Questions
  • Chapter 8. Eigenfunction-Based Spatial Filtering Regression
  • 8.1 Fourth Contact: Spatial Autocorrelation
  • 8.2 Spatial Autocorrelation as Map Pattern
  • 8.3 Augmented Regression With Spatial Filters as Synthetic Covariates
  • 8.4 Practice in R
  • Review Questions
  • Chapter 9. Introduction to Local Models: Geographically Weighted Regression and Eigenfunction-Based Spatial Filtering Approach
  • 9.1 Global and Local Regression
  • 9.2 Geographically Weighted Regression (GWR)
  • 9.3 Eigenfunction-Based Spatial Filtering Approach to Addressing Spatial Nonstationarity
  • 9.4 Comparison Between GWR and ESF SVC Models
  • 9.5 Practice in R
  • Review Questions
  • Chapter 10. Brief Introduction to Spatial Panel Regression and SVC Panel Regression
  • 10.1 Panel Dataset and Panel Regression
  • 10.2 Spatial Panel Models
  • 10.3 Spatially Varying Coefficient Process With Panel Model
  • 10.4 Practice in R
  • Review Questions
  • Chapter 11. Conclusion
  • 11.1 Journey So Far
  • 11.2 Future Learning Directions
  • Appendix: Answers to Review Questions
  • References
  • Index

Recent Product Reviews:

This text provides an excellent introduction along with a thorough overview of spatial analysis techniques with R. The book provides a solid framework to move students through a wide variety of models and spatial frameworks for analysis while maintaining a level of accessibility superior to other texts on the subject. With the increasing importance and application of spatial analysis in research, this text is appropriate for a variety of disciplines including the natural sciences and social sciences.
Mike Hollingsworth, Black Hills State University
The book's approach to teaching spatial data analysis has the potential to significantly enhance the learning experience in the classroom.
Kesong Hu, University of Arkansas, Little Rock
The book effectively combines theoretical concepts with practical applications, providing students with the essential skills to translate learning into practice.
Man Kit Lei, University of Georgia
This is a textbook I would use with my graduate students and doctoral students. This text will help students feel more comfortable with statistics and numbers.
Bret D. Cormier, Providence College

Recommendations