Bayesian spatiotemporal modelling of political violence and conflict events using discrete-time Hawkes processes

TL;DR

This paper introduces a Bayesian spatiotemporal Hawkes process model to more accurately monitor and understand political violence risk over time and space, surpassing traditional methods in stability and uncertainty quantification.

Contribution

It develops a novel Bayesian spatiotemporal Hawkes process model for conflict risk assessment, providing detailed uncertainty estimates and insights into regional and conflict-type differences.

Findings

Model effectively estimates conflict risk levels.

Provides more stable and robust monitoring compared to historical averages.

Offers insights into behavioral differences across countries and conflict types.

Abstract

The monitoring of conflict risk in the humanitarian sector is largely based on simple historic averages. The overarching goal of this work is to assess the potential for using a more statistically rigorous approach to monitor the risk of political violence and conflict events in practice, and thereby improve our understanding of their temporal and spatial patterns, to inform preventative measures. In particular, a Bayesian, spatiotemporal variant of the Hawkes process is fitted to data gathered by the Armed Conflict Location and Event Data (ACLED) project to obtain sub-national estimates of conflict risk in South Asia over time and space. Our model can effectively estimate the risk level of these events within a statistically sound framework, with a more precise understanding of uncertainty than was previously possible. The model also provides insights into differences in behaviours between countries and conflict types. We also show how our model can be used to monitor short and long term trends, and that it is more stable and robust to outliers compared to current practices that rely on historical averages.