Contagion, Confounding, and Causality: Confronting the Three C’s of Observational Political Networks Research
Contagion across various types of connections is a central process in the study of many political phenomena (e.g., democratization, civil conflict, voter turnout). Over the last decade the methodological literature addressing the challenges in causally identifying contagion in networks has exploded. In one of the foundational works in this literature, Shalizi and Thomas (2011) propose a permutation test for contagion in longitudinal network data that is not confounded by selection (e.g., homophily). We illustrate the properties of this test via simulation. We assess its statistical power under various conditions of the data; including the nature of the contagion, the structure of the network through which contagion occurs, and the number of time periods included in the data. We then apply this test to an example domain that is commonly considered in the context of observational research on contagion—the international spread of democracy. We find evidence of the international contagion of democracy. We conclude with a discussion of the practical applicability of the Shalizi & Thomas test to the study of contagion in political networks.
Modeling wildfire ignition origins in southern California using linear network point processes
This paper focuses on spatial and temporal modeling of point processes on linear networks. Point processes on linear networks can simply be defined as point events occurring on or near line segment network structures embedded in a certain space. A separable modeling framework is introduced that posits separate formation and dissolution models of point processes on linear networks over time. While the model was inspired by spider web building activity in brick mortar lines, the focus is on modeling wildfire ignition origins near road networks over a span of 14 years. As most wildfires in California have human-related origins, modeling the origin locations with respect to the road network provides insight into how human, vehicular and structural densities affect ignition occurrence. Model results show that roads that traverse different types of regions such as residential, interface and wildland regions have higher ignition intensities compared to roads that only exist in each of the mentioned region types.