  We develop a stochastic modeling approach based on spatial point processes of
log-Gaussian Cox type for a collection of around 5000 landslide events provoked
by a precipitation trigger in Sicily, Italy. Through the embedding into a
hierarchical Bayesian estimation framework, we can use the Integrated Nested
Laplace Approximation methodology to make inference and obtain the posterior
estimates. Several mapping units are useful to partition a given study area in
landslide prediction studies. These units hierarchically subdivide the
geographic space from the highest grid-based resolution to the stronger
morphodynamic-oriented slope units. Here we integrate both mapping units into a
single hierarchical model, by treating the landslide triggering locations as a
random point pattern. This approach diverges fundamentally from the unanimously
used presence-absence structure for areal units since we focus on modeling the
expected landslide count jointly within the two mapping units. Predicting this
landslide intensity provides more detailed and complete information as compared
to the classically used susceptibility mapping approach based on relative
probabilities. To illustrate the model's versatility, we compute absolute
probability maps of landslide occurrences and check its predictive power over
space. While the landslide community typically produces spatial predictive
models for landslides only in the sense that covariates are spatially
distributed, no actual spatial dependence has been explicitly integrated so far
for landslide susceptibility. Our novel approach features a spatial latent
effect defined at the slope unit level, allowing us to assess the spatial
influence that remains unexplained by the covariates in the model.
