Hawkes processes (HP) are a large class of stochastic point process models scientists have used to analyze contagion phenomena ranging from earthquakes, infectious diseases and biological neurons to financial trading activity, memes on social media and gun violence. We introduce applications of HP to the latter before reviewing general strategies for fitting HP to data, paying attention to the influence of model structure on computational scalability considerations. We then apply a recently developed high-performance computing powered Bayesian inference strategy for the spatiotemporal HP analysis of 412,376 acts of gun violence in the U.S. between 2014 and 2024. We finish with a discussion of model fit and directions for future research.
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