Predicting political violence using a state-space model

We provide a proof-of-concept for a novel state-space modelling approach for predicting monthly deaths due to political violence. Attention is focused on developing the method and demonstrating the utility of this approach, which provides exciting opportunities to engage with domain experts in developing new and improved state-space models for predicting violence. The prediction is made on a grid of cells with spatial resolution of 0.5 × 0.5 degrees, and each cell is modeled to have two mathematically well-defined unobserved/latent/hidden states that evolves over time and encode the “onset risk” and “potential severity”, respectively. This offers a certain level of interpretability of the model. By using the model for computing the probability distribution for a death count at a future time conditioned on all data observed up until the current time, a predictive distribution is obtained. The predictive distribution typically places a certain mass at the death count 0 (no violent outbreak) and the remaining mass indicating a likely interval of the fatality count, should a violent outbreak appear. To evaluate the model performance we—lacking a better alternative—report the mean of the predictive distribution, but the access to the predictive distribution is in itself an interesting contribution to the application. This work merely serves as a proof-of-concept for the state-space modeling approach for this type of data and several possible directions for further work that could improve the predictive performance are suggested.