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Statistical Evaluation of Randomly Acquired Characteristics on Outsoles with Implications Regarding Chance Co-Occurrence and Spatial Randomness

NCJ Number
306217
Author(s)
Nicole Richetelli
Date Published
2020
Length
187 pages
Annotation

This research sought to quantify the chance association of randomly acquired characteristics on outsole RACs on unrelated shoes and the spatial distribution of these features on outsoles, with the long-term goal of aiding weight of evidence assessments in forensic footwear examinations.

Abstract

Footwear evidence holds tremendous forensic value, owing to its ability to formulate linkages between victims, suspects, and scenes. Naturally, the strength of these linkages is a function of the perceived clarity, quality and rarity of class, subclass and randomly acquired characteristics (RACs), which are the fundamental outsole features used to formulate source associations. To reach a conclusion when performing a footwear comparison, forensic examiners must assign value to the observed similarities and differences that exist between questioned crime scene and test impressions. Embedded within this process is an evaluation of the random association between unrelated shoes as a function of both class and acquired wear characteristics. To date, weight of evidence within this space has been largely informed by the training and subjective casework experience accumulated by an examiner over the life of his or her career. In pursuit of supporting the foundational validity of this comparison process, this research sought to quantify the chance association of RACs on unrelated shoes and the spatial distribution of these features on outsoles, with the long-term goal of aiding weight of evidence assessments in forensic footwear examinations.

Using a large-scale database of 1,300 unrelated outsoles, the position and shape of 72,306 RACs was investigated. Features with consistent position and shape-classification were pairwise compared and sorted using a numerical estimate of similarity. Based on this assessment, more than 91,000 of the most quantitatively similar features were visually evaluated to model the relationship between numerical similarity and visual indistinguishability. Using this model, more than 1 million additional feature comparisons were evaluated to predict the potential for visual confusion. Subsequently, empirical, and modeled probabilities of indistinguishability were combined with the chance for positional overlap to yield location- and shape-specific estimates of chance association. The results indicated that RACs exhibit high discriminating potential. (Published abstract provided)