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Footwear comparison is used to link between a suspect's shoe and a footprintfound at a crime scene. Investigators compare the two items using randomlyacquired characteristics (RACs), such as scratches or holes. However, to date,the distribution of RAC characteristics has not been investigated thoroughly,and the evidential value of RACs is yet to be explored. An important questionconcerns the distribution of the location of RACs on shoe soles, which canserve as a benchmark for comparison. The location of RACs is modeled here as apoint process over the shoe sole and a data set of 386 independent shoes isused to estimate its rate function. The analysis is somewhat complicated as theshoes are differentiated by shape, level of wear and tear and contact surface.This paper presents methods that take into account these challenges, either byusing natural cubic splines on high resolution data, or by using apiecewise-constant model on larger regions defined by experts' knowledge. It isshown that RACs are likely to appear at certain locations, corresponding to thefoot's morphology. The results can guide investigators in determining theevidential value of footprint comparison.

Spatial modeling of randomly acquired characteristics on outsoles with application to forensic shoeprint analysis