  When a latent shoeprint is discovered at a crime scene, forensic analysts
inspect it for distinctive patterns of wear such as scratches and holes (known
as accidentals) on the source shoe's sole. If its accidentals correspond to
those of a suspect's shoe, the print can be used as forensic evidence to place
the suspect at the crime scene. The strength of this evidence depends on the
random match probability---the chance that a shoe chosen at random would match
the crime scene print's accidentals. Evaluating random match probabilities
requires an accurate model for the spatial distribution of accidentals on shoe
soles. A recent report by the President's Council of Advisors in Science and
Technology criticized existing models in the literature, calling for new
empirically validated techniques. We respond to this request with a new spatial
point process model for accidental locations, developed within a hierarchical
Bayesian framework. We treat the tread pattern of each shoe as a covariate,
allowing us to pool information across large heterogeneous databases of shoes.
Existing models ignore this information; our results show that including it
leads to significantly better model fit. We demonstrate this by fitting our
model to one such database.
