Spatial Regression Models for the Social Sciences

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.

• Series Editor’s IntroductionPrefaceAcknowledgmentsAbout the AuthorsChapter 1: IntroductionLearning Objectives1.1 Spatial Thinking in the Social Sciences1.2 Introduction to Spatial Effects1.3 Introduction to the Data Example1.4 Structure of the BookStudy QuestionsChapter 2: Exploratory Spatial Data AnalysisLearning Objectives2.1 Exploratory Data Analysis2.2 Neighborhood Structure and Spatial Weight Matrix2.3 Spatial Autocorrelation, Dependence, and Heterogeneity2.4 Exploratory Spatial Data AnalysisStudy QuestionsChapter 3: Models Dealing With Spatial DependenceLearning Objectives3.1 Standard Linear Regression and Diagnostics for Spatial Dependence3.2 Spatial Lag Models3.3 Spatial Error ModelsStudy QuestionsChapter 4: Advanced Models Dealing With Spatial DependenceLearning Objectives4.1 Spatial Error Models With Spatially Lagged Responses4.2 Spatial Cross-Regressive Models4.3 Multilevel Linear RegressionStudy QuestionsChapter 5: Models Dealing With Spatial HeterogeneityLearning Objectives5.1 Aspatial Regression Methods5.2 Spatial Regime Models5.3 Geographically Weighted RegressionStudy QuestionsChapter 6: Models Dealing With Both Spatial Dependence and Spatial HeterogeneityLearning Objectives6.1 Spatial Regime Lag Models6.2 Spatial Regime Error Models6.3 Spatial Regime Error and Lag Models6.4 Model Fitting6.5 Data ExampleStudy QuestionsChapter 7: Advanced Spatial Regression ModelsLearning Objectives7.1 Spatio-temporal Regression Models7.2 Spatial Regression Forecasting Models7.3 Geographically Weighted Regression for ForecastingStudy QuestionsChapter 8: Practical Considerations for Spatial Data AnalysisLearning Objectives8.1 Data Example of U.S. Poverty in R8.2 General Procedure for Spatial Social Data AnalysisStudy QuestionsAppendix A: Spatial Data SourcesAppendix B: Results Using Forty Spatial Weight Matrices available on the website at study.sagepub.com/researchmethods/quantitative-statistical-research/chiGlossaryReferencesIndex

"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."