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Spatial Regression Models

Spatial Regression Models

Second Edition

July 2018 | 128 pages | SAGE Publications, Inc
Spatial Regression Models illustrates the use of spatial analysis in the social sciences within a regression framework and is accessible to readers with no prior background in spatial analysis. The text covers different modeling-related topics for continuous dependent variables, including mapping data on spatial units, creating data from maps, analyzing exploratory spatial data, working with regression models that have spatially dependent regressors, and estimating regression models with spatially correlated error structures. 

Using social science examples based on real data, the authors illustrate the concepts discussed, and show how to obtain and interpret relevant results. The examples are presented along with the relevant code to replicate all the analysis using the R package for statistical computing. Users can download both the data and computer code to work through all the examples found in the text. New to the Second Edition is a chapter on mapping as data exploration and its role in the research process, updates to all chapters based on substantive and methodological work, as well as software updates, and information on estimation of time-series, cross-sectional spatial models.

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Chapter 1: Why Space in the Social Sciences?
Chapter 2: Maps as Displays of Information
Chapter 3: Interdependency Among Observations
Chapter 4: Spatially Lagged Dependent Variables
Chapter 5: Spatial Error Model
Chapter 6: Extensions


“Ward and Gleditsch provide a valuable and highly accessible introduction to spatial analysis, including data and code for in-text examples and other course materials in an online repository. This is an excellent supplement for any introduction to spatial analysis!” 

Matthew Ingram
University at Albany, SUNY

“This ‘Little Green Book’ by Ward and Gleditsch introduces the fundamental concepts of spatial regression models. It is good for both introductory and intermediate level of students who like to implement spatial regression models into their research.” 

Changjoo Kim
University of Cincinnati

“This text provides a solid introduction to spatial thinking and spatial regression modeling for social scientists that transcends disciplinary boundaries, and will provide a valuable resource for students and professionals alike who are new to this material.” 

Corey Sparks
The University of Texas at San Antonio

“Spatial statistics is becoming increasingly important to all fields of social science. This book does a good job of providing a brief and essential introduction to core ideas in spatial statistics.” 

Juan Sandoval
Saint Louis University

Sample Materials & Chapters

Chapter 3: Interdependency Among Observations

For instructors

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