The Currents of Conflict: Decomposing Conflict Trends with Gaussian Processes

TL;DR

This paper introduces a Gaussian process-based method for analyzing and forecasting the complex temporal and spatial patterns of violent conflict using highly detailed event data, enabling better understanding and prediction of conflict dynamics.

Contribution

It presents a novel, parsimonious approach combining disaggregated conflict data with Gaussian processes to estimate and extrapolate conflict trends in space and time.

Findings

Effective estimation of conflict trends using Gaussian processes

Ability to forecast future conflict patterns

Insights into conflict diffusion and traps

Abstract

I present a novel approach to estimating the temporal and spatial patterns of violent conflict. I show how we can use highly temporally and spatially disaggregated data on conflict events in tandem with Gaussian processes to estimate temporospatial conflict trends. These trends can be studied to gain insight into conflict traps, diffusion and tempo-spatial conflict exposure in general; they can also be used to control for such phenomenons given other estimation tasks; lastly, the approach allow us to extrapolate the estimated tempo-spatial conflict patterns into future temporal units, thus facilitating powerful, stat-of-the-art, conflict forecasts. Importantly, these results are achieved via a relatively parsimonious framework using only one data source: past conflict patterns.