Spatial Regression Models for the Social Sciences: (Advanced Quantitative Techniques in the Social Sciences)

Providing comprehensive coverage of spatial regression methods, this text explains what each method is and when and how to apply it, connecting concepts to social science research topics. Avoiding mathematical formulas and symbols as much as possible, the book introduces the methods in an easy-to-follow manner, providing comprehensive coverage and using the same social science example throughout to demonstrate the 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

About the Author :
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. Dr. Jun Zhu is Professor of Statistics at the University of Wisconsin–Madison. She is a faculty member in the Department of Statistics and the Department of Entomology, as well as a faculty affiliate with the Center for Demography and Ecology and the Department of Biostatistics and Medical Informatics. The main components of her research activities are statistical methodological research and scientific collaborative research. Her statistical methodological research concerns developing statistical methodology for analyzing spatially referenced data (spatial statistics) and spatial data repeatedly sampled over time (spatio-temporal statistics) that arise often in the biological, physical, and social sciences. Her collaborative research concerns applying modern statistical methods, especially spatial and spatio-temporal statistics, to studies of agricultural, biological, ecological, environmental, and social systems conducted by research scientists. Dr. Zhu’s methodological and collaborative research projects have been supported by the Environmental Protection Agency, National Institutes of Health, National Science Foundation, U.S. Department of Agriculture, U.S. Department of Defense, and U.S. Geographical Society.  She is a Fellow of the American Statistical Association and a recipient of the Distinguished Achievement Medal in its Section of Statistics and the Environment.

Review :
"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." "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." "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." "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." "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." 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