  We propose a novel framework for integrating Hawkes processes with
multi-armed bandit algorithms to solve spatio-temporal event forecasting and
detection problems when data may be undersampled or spatially biased. In
particular, we introduce an upper confidence bound algorithm using Bayesian
spatial Hawkes process estimation for balancing the tradeoff between exploiting
geographic regions where data has been collected and exploring geographic
regions where data is unobserved. We first validate our model using simulated
data and then apply it to the problem of disaster search and rescue using calls
for service data from hurricane Harvey in 2017. Our model outperforms the state
of the art baseline spatial MAB algorithms in terms of cumulative reward and
several other ranking evaluation metrics.
