This paper introduces a probabilistic method for modeling (geo-)spatial tesselations. We propose a new architecture of conditional random fields in two dimensions to estiamte the determinants of spatial groupings.
Very large spatio-temporal lattice data are becoming increasingly common across a variety of disciplines. However, estimating interdependence across space, time, and outcomes in large lattice data sets remains challenging, as existing approaches are …
Political scientists frequently study spatially interdependent processes, such as policy diffusion, democratization, or the spread of violent conflict. Existing studies of such mechanisms typically rely on the spatio-temporal autoregressive (STAR) …
State capacity is often described as one of the most important explanations of civil conflict. Yet current conceptualizations of state capacity typically focus exclusively on the state, ignoring the relational nature of armed conflict. We propose …
Research on ethnic politics and political violence has benefited substantially from the growing availability of cross-national, geo-coded data on ethnic settlement patterns. However, because existing datasets represent ethnic homelands using …