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Spatial Regression Models for the Social Sciences

Spatial Regression Models for the Social Sciences

First Edition
  • Guangqing Chi - The Pennsylvania State University, USA
  • Jun Zhu - University of Wisconsin - Madison, USA

May 2019 | 280 pages | SAGE Publications, Inc
Spatial Regression Models for the Social Sciences provides comprehensive coverage of spatial regression methods for social scientists with limited training in mathematical statistics and introduces the methods in an easy-to-follow manner. Focusing on the methods that are commonly used by social scientists, Guangqing Chi and Jun Zhu explain what each method is and when and how to apply it, by connecting it to social science research topics. They avoid mathematical formulas and symbols, and throughout the book they use the same social science example to demonstrate applications of each method and what the results can tell us.
Chapter 1: Introduction
Learning Objectives  
Spatial Thinking in the Social Sciences  
Spatial Effects  
Data Example of Population Change  
Structure of the Book  
Study Questions  
Chapter 2: Exploratory Spatial Data Analysis
Learning Objectives  
Exploratory Data Analysis  
Neighborhood Structure and Spatial Weight Matrix  
Spatial Autocorrelation, Dependence, and Heterogeneity  
Exploratory Spatial Data Analysis  
Study Questions  
Chapter 3: Models Dealing with Spatial Dependence
Learning Objectives  
Standard Linear Regression and Diagnostics for Spatial Dependence  
Spatial Lag Models  
Spatial Error Models  
Study Questions  
Chapter 4: Advanced Models Dealing with Spatial Dependence
Learning Objectives  
Spatial Error Models with Spatially Lagged Responses  
Spatial Cross-Regressive Models  
Multilevel Linear Regression  
Study Questions  
Chapter 5: Models Dealing with Spatial Heterogeneity
Learning Objectives  
Aspatial Regression Methods  
Spatial Regime Models  
Geographically Weighted Regression  
Study Questions  
Chapter 6: Models Dealing with Both Spatial Dependence and Spatial Heterogeneity Learning Objectives
Learning Objectives  
Spatial Regime Lag Models  
Spatial Regime Error Models  
Spatial Regime Error and Lag Models  
Model Fitting  
Data Example of Population Change  
Chapter 7: Advanced Spatial Regression Models
Learning Objectives  
Spatio-Temporal Regression Models  
Spatial Regression Forecasting Models  
Geographically Weighted Regression for Forecasting  
Study Questions  
Chapter 8: Practical Considerations for Spatial Data Analysis
Learning Objectives  
Data Example of Poverty in R  
General Procedure for Spatial Social Data Analysis  
Appendix A: Spatial Data Sources
Appendix B: Results Using Forty Spatial Weight Matrices
Key features


  • Comprehensive coverage of spatial regression models, from simple concepts and methods to advanced models, makes this book useful for a diverse audience including instructors, researchers, and students in a wide range of disciplines.
  • The book’s pedagogy includes study objectives, sidebars highlighting important points, figures/illustrations, and study questions for easy mastery of the material.
  • The authors include data examples using the increasingly popular R.
  • All figures and illustrations have color versions available on the book’s online companion site.


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ISBN: 9781544302072