  This study investigates the spatial distribution of emergency alarm call
events to identify spatial covariates associated with the events and discern
hotspot regions for the events. The study is motivated by the problem of
developing optimal dispatching strategies for prehospital resources such as
ambulances. To achieve our goals, we model the spatially varying call
occurrence risk as an intensity function of an inhomogeneous spatial Poisson
process that we assume is a log-linear function of some underlying spatial
covariates. The spatial covariates used in this study are related to road
network coverage, population density, and the socio-economic status of the
population in Skellefte{\aa}, Sweden. A new heuristic algorithm has been
developed to select an optimal estimate of the kernel bandwidth in order to
obtain the non-parametric intensity estimate of the events and to generate
other covariates. Since we consider a large number of spatial covariates as
well as their products, and since some of them may be strongly correlated,
lasso-like elastic-net regularisation has been used in the log-likelihood
intensity modeling to perform variable selection and reduce variance inflation
from overfitting and bias from underfitting. As a result of the variable
selection, the fitted model structure contains individual covariates of both
road network and demographic types. We discovered that hotspot regions of calls
have been observed along dense parts of the road network. Evaluation of the
model also suggests that the estimated model is stable and can be used to
generate a reliable intensity estimate over the region, which can be used as an
input in the problem of designing prehospital resource dispatching strategies.
