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"The Spatial Analysis of War" published on by Oxford University Press.

Although spatial analysis and Geographic Information Systems (GIS) have become prevalent in the social and policy sciences, GIS and its mapmaking capability remains an underutilized tool among the decision support tools available to policy makers. Using a case study of Medicaid expenditure changes in Ohio, the authors demonstrate how spatial analysis and display can incorporate useful weights for policy makers. Through the use of dependence indices based upon the distributions of the affected recipients and service providers to weight the expenditures, the link between the effects of policy changes and the spatial distributions of these populations becomes clearer. The article argues that policy makers can be given a more appropriate picture of the potential local implications of statewide policy changes through the use of weights. Because of the power of maps to so starkly display these distributions, the article concludes with a caution that such tools should be used ethically with considered judgment.

AbstractHow does space matter in our analyses? How can we evaluate diffusion of phenomena or interdependence among units? How biased can our analysis be if we do not consider spatial relationships? All the above questions are critical theoretical and empirical issues for political scientists belonging to several subfields from Electoral Studies to Comparative Politics, and also for International Relations. In this special issue on methods, our paper introduces political scientists to conceptualizing interdependence between units and how to empirically model these interdependencies using spatial regression. First, the paper presents the building blocks of any feature of spatial data (points, polygons, and raster) and the task of georeferencing. Second, the paper discusses what a spatial matrix (W) is, its varieties and the assumptions we make when choosing one. Third, the paper introduces how to investigate spatial clustering through visualizations (e.g. maps) as well as statistical tests (e.g. Moran's index). Fourth and finally, the paper explains how to model spatial relationships that are of substantive interest to some of our research questions. We conclude by inviting researchers to carefully consider space in their analysis and to reflect on the need, or the lack thereof, to use spatial models.

How does space matter in our analyses? How can we evaluate diffusion of phenomena or interdependence among units? How biased can our analysis be if we do not consider spatial relationships? All the above questions are critical theoretical and empirical issues for political scientists belonging to several subfields from Electoral Studies to Comparative Politics, and also for International Relations. In this special issue on methods, our paper introduces political scientists to conceptualizing interdependence between units and how to empirically model these interdependencies using spatial regression. First, the paper presents the building blocks of any feature of spatial data (points, polygons, and raster) and the task of georeferencing. Second, the paper discusses what a spatial matrix (W) is, its varieties and the assumptions we make when choosing one. Third, the paper introduces how to investigate spatial clustering through visualizations (e.g. maps) as well as statistical tests (e.g. Moran's index). Fourth and finally, the paper explains how to model spatial relationships that are of substantive interest to some of our research questions. We conclude by inviting researchers to carefully consider space in their analysis and to reflect on the need, or the lack thereof, to use spatial models.

In this study, the differences in the spatial pattern of happiness will be revealed and the distribution of the relationship between happiness and economic variables between countries will be discussed. When the distribution pattern is examined, it can be observed that happy and unhappy countries are concentrated in certain areas. Therefore, the concept of happiness has been evaluated from a geographical point of view. From the analysis of 147 countries in this study, it was found that economic freedom and GDP have a positive effect on happiness whereas inflation and unemployment have a negative effect. A striking result was that in addition to the relevant economic variables, location is also effective in the interpretation of happiness. One of the significant results of the study was that geography is a factor to consider in investigating the relationship between economic variables and happiness.

EU legislative analysis has been enriched by insightful controversies over the interpretation of the policy process. This debate has concentrated on the interpretation of the process by focusing on the identification of the agenda setter and the relevance of voting weights, but little attention has been paid to the accurate specification of the second component of spatial analysis, the preferences of the actors involved. Although a misspecification can seriously distort the predictions of spatial theory, empirical applications often tend to reduce the number of dimensions, exclude actors saliencies and assume continuous policy issues. Using computer simulation we show that spatial models are more robust to a misinterpretation of the policy process than to a misspecification of actors' preferences, and that their institutional elements are less decisive for the models' outcome predictions. Our empirical analysis confirms these results and provides detailed insights into the impact of the institutional and the preference component of spatial theory. We conclude that scholars should pay more attention to the accurate specification of the preference component of the models to improve our understanding of legislative decision making in the EU. [Reprinted by permission of Sage Publications Ltd., copyright 2007.]