  Spatial data is a rich source of information for actuarial applications:
knowledge of a risk's location could improve an insurance company's ratemaking,
reserving or risk management processes. Insurance companies with high exposures
in a territory typically have a competitive advantage since they may use
historical losses in a region to model spatial risk non-parametrically. Relying
on geographic losses is problematic for areas where past loss data is
unavailable. This paper presents a method based on data (instead of smoothing
historical insurance claim losses) to construct a geographic ratemaking model.
In particular, we construct spatial features within a complex representation
model, then use the features as inputs to a simpler predictive model (like a
generalized linear model). Our approach generates predictions with smaller bias
and smaller variance than other spatial interpolation models such as bivariate
splines in most situations. This method also enables us to generate rates in
territories with no historical experience.
