Spatial Regression Models for the Social Sciences
Guangqing Chi
The Pennsylvania State University, USA
Jun Zhu
University of Wisconsin - Madison, USA
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Library of Congress Cataloging-in-Publication Data
Names: Chi, Guangqing, author. | Zhu, Jun (Professor of statistics), author.
Title: Spatial regression models for the social sciences / Guangqing Chi, The Pennsylvania State University, USA, Jun Zhu, University of Wisconsin–Madison, USA.
Description: Los Angeles : SAGE, [2020] | Includes bibliographical references and index.
Identifiers: LCCN 2018050719 | ISBN 9781544302072 (hardcover : alk. paper)
Subjects: LCSH: Spatial analysis (Statistics) | Social sciences—Statistical methods.
Classification: LCC HA30.6 .C45 2020 | DDC 519.5/3—dc23 LC record available at https://lccn.loc.gov/2018050719
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Series Editor’s Introduction
I am very pleased to introduce Guangqing Chi and Jun Zhu’s volume Spatial Regression Models for the Social Sciences. Over the past few decades there has been a rapid increase in the use of spatial data to discern spatial effects in social science research due to the upsurge in the availability of geographically referenced data and the availability of user-friendly spatial data analysis software packages. However, many of the empirical studies remain at a stage that is descriptive and exploratory in nature, because many studies are geo-mapping or spatial overlay using geocoding technology. Regression-typed analyses exploring causal or correlational spatial effects are relatively sparse. Some studies employed multilevel regression models with random effects but failed to address the departure from the independent errors assumption at high levels. For contiguous geographic units, spatial lag models, spatial error models, spatial regime models, geographically weighted regression, spatio-temporal regression models, and geographically weighted regression for forecasting are more salient than the multilevel regression model. In essence, these models address the spatial dependence and heterogeneity issues more sophisticatedly by fully exploring the neighborhood structure embedded in an appropriately developed spatial weight matrix.
This volume describes all these latest advances in spatial regression models in a relatively accessible fashion for a broad range of readers conducting social scientific research. It describes the statistical principles, main features, strengths and limitations, and illustrating examples in a clear, succinct, and comprehensive fashion. The book is well organized to discuss lattice (or areal) data analysis using spatial regression methods.
This volume aims to help social scientists learn practical and useful statistical methods for spatial regression with relative ease. The authors have made efforts to limit the use of mathematic notations, derivations, and proofs but present the core materials in a comprehensive and easy-to-follow manner. Each chapter begins with learning objectives and ends with study questions
