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

First Edition
Volume: 14
March 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

Supplements

Student Study Site

An open-access Study Site includes:

  • A downloadable version of the Appendix, “Moran’s I statistics of explanatory variables by forty spatial weight matrices”
  • Full-color versions of the figures in the book

“This is an important book bringing together a family of related statistical measures and explaining them in a coherent way. Written by leading researchers in the field, it uses a consistent spatial example and applies and explains various measures within a unifying frame to aid in understanding by readers. As real-time spatial data becomes increasingly prevalent, the need for analysts to accurately and meaningfully interpret this data is rapidly growing."

David Levinson
University of Sydney

“The field of spatial regression has grown rapidly over the last decade. This book goes a long way toward filling a gap by providing students and practitioners with a useful text that is written at a level that should make it broadly accessible.”

Peter Rogerson
University at Buffalo

“This is an exceptionally well-written text on spatial data analysis tailored for social science research. It deals with spatial thinking and regression analysis with remarkable depth and expertise in a comprehensive and easy-to-follow manner. It is a primer that should be on every social scientist's shelf.”

Zudi Lu
University of Southampton, United Kingdom

“This introductory book offers a full overview of the different ways in which a standard linear regression model can be extended to contain spatial effects.”

J. Paul Elhorst
University of Groningen, the Netherlands

“Spatial data science is an evolving field. This is a valuable book that introduces to students, researchers, and faculty the foundation of spatial statistics and offers tremendous insights on how to statistically analyze geo-spatial data. Anyone working geo-data must read this book if they want accurate and unbiased research findings.”

J.S. Onesimo Sandoval
Saint Louis University

the book’s main strength is its efficiency, organization, and methodical approach to explaining many concepts in spatial regression. It does not necessarily progress in concept difficulty nor in concept importance, but mixes both to form a coherent volume that is a strong reference for both looking up terms as a “refresher” and as a guide to diversifying one’s own spatial regression techniques for a comparative analysis

Clio Andris
Georgia Institute of Technology, USA
EPB: Urban Analytics and City Science

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