Spatial Regression Models

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NOTE: Updated R code for use with this volume, along with other resources, is available at: http://privatewww.essex.ac.uk/~ksg/srm_book.html


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.

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Introduction
1
12 Democracy Around the World
4
13 Introducing Spatial Dependence
8
14 Maps as Visual Displays of Data
11
15 Measuring Spatial Association and Correlation
14
16 Measuring Proximity
19
17 Estimating Spatial Models
28
18 Summary
33
Notes
64
Spatial Error Model
65
32 Maximum Likelihood Estimation of the Spatial Errors Model
67
Democracy and Development
68
34 Spatially Lagged y Versus Spatial Errors
69
35 Assessing Spatial Error in Dyadic Trade Flows
70
36 Summary
75
Notes
76

Notes
34
Spatially Lagged Dependent Variables
35
22 Estimating the Spatially Lagged y Model
40
23 Maximum Likelihood Estimates of the Spatially Lagged y Model of Democracy
43
24 Equilibrium Effects in the Spatially Lagged y Model
44
25 Spatial Dependence in Turnout in Italy
50
26 Using Different Weights Matrices in a Spatially Lagged Dependent Variable Model
56
27 The Spatially Lagged Dependent Variable Versus OLS With Dummy Variables
61
Extensions
77
42 Inference and Model Evaluation
82
43 Summary
86
Software Options
87
References
91
Index
97
About the Authors
99
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Հեղինակի մասին (2008)

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.

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.

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