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 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.
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
