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Spatial Data Analysis With R
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Spatial Data Analysis With R

Volume: 16
March 2025 | 416 pages | SAGE Publications, Inc
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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 at https://edge.sagepub.com/yu 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.


 
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

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
Key features
  • Each chapter begins with a series of clearly defined learning objectives providing core competencies that students should acquire by the end of the chapter.
  • Includes end-of-chapter review questions to test students’ comprehension of the material.
  • Provides code snippets and annotations that break down complex coding processes into understandable
    steps, demystifying the coding process and making it accessible to those who may be new to programming in R.
  • Is richly illustrated with figures, graphs, and maps created using R. These clear, visual representations of data aid readers in the interpretation and analysis of spatial data.
  • Includes data and code on the student website in order to replicate examples in the book.
  • Provides exercises and answers on the instructor website that can be set for homework.

Sample Materials & Chapters

Sample Chapters 1 & 2

Sage College Publishing

You can purchase or sample this product on our Sage College Publishing site:

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