Spatial Regression Models for the Social Sciences

First Edition
Guangqing Chi - The Pennsylvania State University, USA
Jun Zhu - University of Wisconsin - Madison, USA
Advanced Quantitative Techniques in the Social Sciences
Spatial Regression Models for the Social Sciences
March 2019 | 272 pages | Sage US
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Description

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. 

Contents

Series Editor’s Introduction

Series Editor’s Introduction

Preface

Preface

Acknowledgments

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 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

Appendix B: Results Using Forty Spatial Weight Matrices available on the website at study.sagepub.com/researchmethods/quantitative-statistical-research/chi

Glossary

Glossary

References

References

Index

Index

Description

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. 

Contents

Series Editor’s Introduction

Series Editor’s Introduction

Preface

Preface

Acknowledgments

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 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

Appendix B: Results Using Forty Spatial Weight Matrices available on the website at study.sagepub.com/researchmethods/quantitative-statistical-research/chi

Glossary

Glossary

References

References

Index

Index

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Spatial Regression Models for the Social Sciences

Guangqing Chi , Jun Zhu
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March 2019 | 272 pages | Sage US

Format Published Date ISBN Price

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. 


Table Of Contents:

  • 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

Recent Product Reviews:

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
“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

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