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Spatial Regression Models for the Social Sciences
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Spatial Regression Models for the Social Sciences

First Edition
  • Guangqing Chi - The Pennsylvania State University, USA
  • Jun Zhu - University of Wisconsin - Madison, USA
Volume: 14
April 2019 | 272 pages | SAGE Publications, Inc
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Spatial Regression Models for the Social Sciences shows researchers and students how to work with spatial data without the need for advanced mathematical statistics. 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. Throughout the book they use the same social science example to demonstrate applications of each method and what the results can tell us. 

 
Series Editor’s Introduction
 
Preface
 
Acknowledgments
 
About the Authors
 
Chapter 1: Introduction
Learning Objectives

 
1.1 Spatial Thinking in the Social Sciences

 
1.2 Introduction to Spatial Effects

 
1.3 Introduction to the Data Example

 
1.4 Structure of the Book

 
Study Questions

 
 
Chapter 2: Exploratory Spatial Data Analysis
Learning Objectives

 
2.1 Exploratory Data Analysis

 
2.2 Neighborhood Structure and Spatial Weight Matrix

 
2.3 Spatial Autocorrelation, Dependence, and Heterogeneity

 
2.4 Exploratory Spatial Data Analysis

 
Study Questions

 
 
Chapter 3: Models Dealing With Spatial Dependence
Learning Objectives

 
3.1 Standard Linear Regression and Diagnostics for Spatial Dependence

 
3.2 Spatial Lag Models

 
3.3 Spatial Error Models

 
Study Questions

 
 
Chapter 4: Advanced Models Dealing With Spatial Dependence
Learning Objectives

 
4.1 Spatial Error Models With Spatially Lagged Responses

 
4.2 Spatial Cross-Regressive Models

 
4.3 Multilevel Linear Regression

 
Study Questions

 
 
Chapter 5: Models Dealing With Spatial Heterogeneity
Learning Objectives

 
5.1 Aspatial Regression Methods

 
5.2 Spatial Regime Models

 
5.3 Geographically Weighted Regression

 
Study Questions

 
 
Chapter 6: Models Dealing With Both Spatial Dependence and Spatial Heterogeneity
Learning Objectives

 
6.1 Spatial Regime Lag Models

 
6.2 Spatial Regime Error Models

 
6.3 Spatial Regime Error and Lag Models

 
6.4 Model Fitting

 
6.5 Data Example

 
Study Questions

 
 
Chapter 7: Advanced Spatial Regression Models
Learning Objectives

 
7.1 Spatio-temporal Regression Models

 
7.2 Spatial Regression Forecasting Models

 
7.3 Geographically Weighted Regression for Forecasting

 
Study Questions

 
 
Chapter 8: Practical Considerations for Spatial Data Analysis
Learning Objectives

 
8.1 Data Example of U.S. Poverty in R

 
8.2 General Procedure for Spatial Social Data Analysis

 
Study Questions

 
 
Appendix A: Spatial Data Sources
 
Appendix B: Results Using Forty Spatial Weight Matrices available on the website at study.sagepub.com/researchmethods/quantitative-statistical-research/chi
 
Glossary
 
References
 
Index
Key features

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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