Fr. 60.90

Spatial Regression Models

English · Paperback / Softback

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Description

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Demonstrating the use of spatial analysis in the social sciences within a regression framework, the second edition of this text employs examples based on real data to illustrate the concepts discussed. The authors also provide relevant code to enable the users to work through the examples in the text.

List of contents










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


About the author

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.

Summary

Demonstrating the use of spatial analysis in the social sciences within a regression framework, the second edition of this text employs examples based on real data to illustrate the concepts discussed. The authors also provide relevant code to enable the users to work through the examples in the text.

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