Bayesian mitigation of spatial coarsening for a Hawkes model applied to gunfire, wildfire and viral contagion

TL;DR

This paper introduces a Bayesian approach to account for spatial coarsening in Hawkes process models, improving inference accuracy for public health data like gun violence, wildfires, and viral spread.

Contribution

It presents a novel Bayesian framework that jointly infers event locations and model parameters while explicitly modeling spatial uncertainty, with scalable computational implementation.

Findings

Bayesian modeling improves location inference accuracy.

The method handles various spatial coarsening regimes.

Scalable implementation enables analysis of thousands of events.

Abstract

Self-exciting spatiotemporal Hawkes processes have found increasing use in the study of large-scale public health threats ranging from gun violence and earthquakes to wildfires and viral contagion. Whereas many such applications feature locational uncertainty, i.e., the exact spatial positions of individual events are unknown, most Hawkes model analyses to date have ignored spatial coarsening present in the data. Three particular 21st century public health crises -- urban gun violence, rural wildfires and global viral spread -- present qualitatively and quantitatively varying uncertainty regimes that exhibit (a) different collective magnitudes of spatial coarsening, (b) uniform and mixed magnitude coarsening, (c) differently shaped uncertainty regions and -- less orthodox -- (d) locational data distributed within the `wrong' effective space. We explicitly model such uncertainties in a Bayesian manner and jointly infer unknown locations together with all parameters of a reasonably flexible Hawkes model, obtaining results that are practically and statistically distinct from those obtained while ignoring spatial coarsening. This work also features two different secondary contributions: first, to facilitate Bayesian inference of locations and background rate parameters, we make a subtle yet crucial change to an established kernel-based rate model; and second, to facilitate the same Bayesian inference at scale, we develop a massively parallel implementation of the model's log-likelihood gradient with respect to locations and thus avoid its quadratic computational cost in the context of Hamiltonian Monte Carlo. Our examples involve thousands of observations and allow us to demonstrate practicality at moderate scales.