Spatial Regression Models for the Social Sciences

by Guangqing Chi, Jun Zhu

Overview

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.

Product Details

ISBN-13:	9781544302065
Publisher:	SAGE Publications
Publication date:	03/06/2019
Series:	Advanced Quantitative Techniques in the Social Sciences
Sold by:	Barnes & Noble
Format:	eBook
Pages:	272
File size:	9 MB

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.

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

Editorial Reviews

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.

Peter Rogerson

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.”

J. Paul Elhorst

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.”

J.S. Onesimo Sandoval

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.

Zudi Lu

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."

David Levinson

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

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