Spatial Regression Models
2nd Edition
Published on 26. July 2018
Book
Paperback/Softback
128 pages
978-1-5443-2883-6 (ISBN)
Description
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.
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.
Reviews / Votes
"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 "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 "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 "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 SandovalMore details
Series
Edition
2nd Revised edition
Language
English
Place of publication
Thousand Oaks
United States
Target group
College/higher education
Edition type
Revised edition
Dimensions
Height: 216 mm
Width: 140 mm
Weight
163 gr
ISBN-13
978-1-5443-2883-6 (9781544328836)
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Schweitzer Classification
Other editions
Previous edition
Michael D. Ward | Kristian Skrede Gleditsch
Spatial Regression Models
Book
04/2008
1st Edition
SAGE Publications Inc
€39.80
Article exhausted; check for reprint
Persons
Michael D. Ward is Professor of Political Science at Duke University. He is an affiliate of the Duke Network Analysis Center. His primary interests are in international relations (spanning democratization, globalization, international commerce, military spending, as well as international conflict and cooperation), political geography, as well as mathematical and statistical methods. Kristian Skrede Gleditsch is Professor in the Department of Government, University of Essex and a Research Associate at the Centre for the Study of Civil War, PRIO. His research interests include conflict and cooperation, democratization, and spatial dimensions of social and political processes. He is the author of All International Politics is Local: The Diffusion of Conflict, Integration, and Democratization (University of Michigan Press, 2002) and Spatial Regression Models (Sage, 2008, with Michael D. Ward) as well as articles in journals including American Journal of Political Science, American Political Science Review, Annals of the Association of American Geographers, Biological Reviews, International Interactions, International Organization, International Studies Quarterly, Journal of Conflict Resolution, Journal of Peace Research, Political Analysis, Political Psychology, and World Politics.
Content
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
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