  Event data are increasingly common in applied political science research.
While these data are inherently locational, political scientists predominately
analyze aggregate summaries of these events as areal data. In so doing, they
lose much of the information inherent to these point pattern data, and much of
the flexibility that comes analyzing events using point process models.
Recognizing these advantages, applied statisticians have increasingly utilized
point process models in the analysis of political events. Yet, this work often
neglects inherent limitations of political event data (e.g, geolocation
accuracy), which can complicate the direct application of point process models.
In this paper, we attempt to bridge this divide: introducing the benefits of
point process modeling for political science research, and highlighting the
unique challenges political science data pose for these approaches. To ground
our discussion, we focus the Global Terrorism Database, using a univariate and
bivariate log-Gaussian Cox process model (LGCP) to analyze terror attacks in
Nigeria during 2014.
