Spatial Regression Models 2/e

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.

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 "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 Sandoval