Spatial Regression Models

ISBN-10: 1412954150

ISBN-13: 9781412954150

Edition: 2008

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Description: Spatial Regression Models illustrates the use of spatial analysis in the social sciences. The text includes sections that cover different modeling-related topics: mapping and making projections; doing exploratory spatial data analysis; working with models which have lagged endogenous right-handed side variables; using spatial error correction models; employing conditionally autoregressive models; and dealing with over-time panels exhibiting spatial structures. Each of the modeling-based discussions includes separate delineations of how to proceed when dealing with main variables that are quantitative as well as qualitative. In each section, the authors employ prominent and diverse examples, introducing readers to key literature in the field. The examples are presented along with relevant data and programs written in the R, which illustrate exactly how to undertake the analyses described. The book ends with a chapter that covers techniques for presenting spatial information. Key Features Geared toward social science readers, unlike other volumes on this topic. Illustrates concepts using well-known international, comparative, and national examples of spatial regression analysis. Presents each example alongside relevant data and code, which is also available on a Web site maintained by the authors. Intended Audience This book is appropriate for graduate students taking any applied social research methods class.

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Book details

Copyright year: 2008
Publisher: SAGE Publications, Incorporated
Publication date: 2/29/2008
Binding: Paperback
Pages: 112
Size: 5.50" wide x 8.50" long x 0.50" tall
Weight: 0.286
Language: English

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.

Michael D. Ward isnbsp;Professor of Political Sciencenbsp;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.

Preface
Introduction
Interaction and Social Science
Democracy Around the World
Introducing Spatial Dependence
Maps as Visual Displays of Data
Measuring Spatial Association and Correlation
Measuring Proximity
Estimating Spatial Models
Summary
Spatially Lagged Dependent Variables
Regression with Spatially Lagged Dependent Variables
Estimating the Spatially Lagged y Model
Maximum Likelihood Estimates of the Spatially Lagged Y Model of Democracy
Equilibrium Effects in the Spatially Lagged y Model
Spatial Dependence in Turnout in Italy
Using Different Weights Matrices in a Spatially Lagged Dependent Variable Model
The Spatially Lagged Dependent Variable Versus OLS with Dummy Variables
Summary
Spatial Error Model
The Spatial Error Model
Maximum Likelihood Estimation of the Spatial Errors Model
Example: Democracy and Development
Spatially Lagged y Versus Spatial Errors
Assessing Spatial Error in Dyadic Trade Flows
Summary
Extensions
Specifying Connectivities
Inference and Model Evaluation
Summary
Appendix: Software Options
References
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