TY  - BOOK
AU  - Chi, Guangqing 
AU  - Zhu, Jun
TI  - Spatial regression models for the social sciences
SN  - 9781544302072
U1  - 519.53 
PY  - 2020///
CY  - New Delhi
PB  - Sage Publications 
KW  - Social sciences - Statistical methods
KW  - Spatial analysis - Statistics
KW  - Spatial Regression
KW  - Spatial data
N1  - 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
N2  - 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
ER  -