Dr. Guangqing Chi is Associate Professor of Rural Sociology and Demography with courtesy appointments in Department of Sociology and Criminology and Department of Public Health Sciences at The Pennsylvania State University. He also serves as Director of the Computational and Spatial Analysis Core of the Social Science Research Institute and Population Research Institute. Dr. Chi is an environmental demographer. His research examines the interactions between population change and the built and natural environments. He pursues his research program within interwoven research projects on climate change, land use, and community resilience, with an emphasis on environmental migration and critical infrastructure/transportation and population change within the smart cities framework. Most recently, Dr. Chi has applied his expertise in big data to study issues of generalizability and reproducibility of Twitter data for population and social science research. He also studies environmental migration, including projects on coupled migrant-pasture systems in Central Asia, permafrost erosion and coastal communities, and ecological migration in China. Dr. Chi¿s research has been supported through grants from national and state agencies, including the National Science Foundation, National Institutes of Health, National Aeronautics and Space Administration, and U.S. Department of Transportation. He has published about 50 articles in peer-reviewed journals. His research on gasoline prices and traffic safety has been highlighted more than 2,000 times by various news media outlets, such as National Public Radio and Huffington Post.

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.[...]
Glossary
References
Index