This final technical report states that randomly acquired characteristics (RACs) in known footwear can form the basis of a source identification if enough detail is present beyond class and subclass of footwear.
In this paper, the authors report on their investigation of the distribution of randomly acquired characteristics (RACs) in an empirical dataset and compared it to an inhomogeneous Poisson point process modified by tread contact and wear. The authors found that when considering only material-loss RACs such as nicks, tears, and scratches, an outsole’s tread pattern dictates the possible distribution of those RACs; they also found that conditions leading to RAC development include factors such as weight, gait, pronation/supination of the person wearing the footwear. Simulations and models were used to test predictions of the effects that tread contact and wear had on shoe outsoles; results from heatmaps based on tread-modified Poisson process were compared against the empirical heatmap by examining the ratio of cell-specific Poisson rates. Based on their findings, the authors suggest that RAC frequency over 67% to 79% of an outsole can be reasonably well explained as a Poisson point process or by a Poisson generalized linear regression model with tread contact as a predictor, and if the predictor includes both tread contact and wear, RAC count over 84% of the spatial locations on an outsole are well-explained. The authors also suggest that the irregularity of the distribution of RACs in this dataset do not negate the use of RACs in forming source associations.
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