  Crime events are known to reveal spatio-temporal patterns, which can be used
for predictive modeling and subsequent decision support. While the focus has
hitherto been placed on areas with high population density, we address the
challenging undertaking of predicting crime hotspots in regions with low
population densities and highly unequally-distributed crime.This results in a
severe sparsity (i.e., class imbalance) of the outcome variable, which impedes
predictive modeling. To alleviate this, we develop machine learning models for
spatio-temporal prediction that are specifically adjusted for an imbalanced
distribution of the class labels and test them in an actual setting with
state-of-the-art predictors (i.e., socio-economic, geographical, temporal,
meteorological, and crime variables in fine resolution). The proposed
imbalance-aware hyper-ensemble increases the hit ratio considerably from 18.1%
to 24.6% when aiming for the top 5% of hotspots, and from 53.1% to 60.4% when
aiming for the top 20% of hotspots.
