Spatial frequency of randomly acquired characteristics on outsoles

The highest levels of source association in forensic footwear comparison rely on the agreement between randomly acquired characteristics (RACs) identified on questioned and exemplar test impressions. These features are presumed to be randomly acquired and independent. However, independent acquisition does not necessarily mean these features will be uniformly distributed across an outsole. The aim of this research was to determine if the distribution of RACs in a research dataset could be described by an inhomogeneous Poisson point process based on tread contact and wear. To achieve this goal, RAC spatial frequency from an empirical dataset of shoes was compared against simulated and modeled data assuming a Poisson point process. Deviations in count between the empirical and simulated/modeled predictions were examined using a Poisson rate test and Moran's I. Results indicate that RAC frequency over 67%-79% of an outsole can be reasonably well explained as a Poisson point process or by a Poisson generalized linear regression model (non-spatial GLM) with tread contact as a predictor. Moreover, if the predictor is extended to include both tread contact and wear, RAC counts over 84% of the spatial locations on an outsole are well-explained (although autocorrelation persists). Overall, results indicate that RACs are not uniformly distributed in this dataset, most likely because the factors that dictate RAC development (friction, gait, etc.) are not uniformly distributed. Although this observation in no way negates the use of RACs in forming source associations, the value of a correspondence can differ depending on its spatial location.