Spatial regression models for the social sciences

Table of Content

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

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.

https://us.sagepub.com/en-us/nam/spatial-regression-models-for-the-social-sciences/book258546