Location distribution of randomly acquired characteristics on a shoe sole
Open Access
- 19 July 2022
- journal article
- research article
- Published by Wiley in Journal of Forensic Sciences
- Vol. 67 (5) , 1801-1809
Abstract
Footwear comparison is used to link between a suspect's shoe and a shoeprint found at a crime scene. Forensic examiners compare the two items, and the conclusion reached is based on class characteristics and randomly acquired characteristics (RACs), such as scratches or holes. An important question concerns the distribution of the location of RACs on shoe soles, which can serve as a benchmark for comparison. This study examines the probability of observing RACs in different areas of a shoe sole using a database of approximately 13,000 RACs observed on 386 outsoles. The analysis is somewhat complicated as the shoes are differentiated by shape and contact surface, and the RACs' locations are subject to measurement errors. A method that takes into account these challenges is presented. All impressions are normalized to a standardized axis to allow for inter‐comparison of RACs on outsoles of different sizes and contact areas, and RACs are localized to one of 14 subareas of the shoe sole. Expected frequencies in each region are assumed to be Poisson distributed with rate parameters that depend on the subarea and the contact surface. Three different estimation approaches are studied: a naive crude approach, a shoe‐specific random effects model, and an estimate that is based on conditional maximum likelihood. It is shown that the rate is not uniform across the shoe sole and that RACs are approximately twice as likely to appear at certain locations, corresponding to the foot's morphology. The results can guide investigators in determining a shoeprint's evidential value.Keywords
This publication has 14 references indexed in Scilit:
- An algorithm to compare two‐dimensional footwear outsole images using maximum cliques and speeded‐up robust featureStatistical Analysis and Data Mining: An ASA Data Science Journal, 2020
- Dataset of Digitized RACs and Their Rarity Score Analysis for Strengthening Shoeprint EvidenceJournal of Forensic Sciences, 2019
- Empirically observed and predicted estimates of chance association: Estimating the chance association of randomly acquired characteristics in footwear comparisonsForensic Science International, 2019
- Dependence among randomly acquired characteristics on shoeprints and their featuresForensic Science International, 2018
- Quantitative assessment of similarity between randomly acquired characteristics on high quality exemplars and crime scene impressions via analysis of feature size and shapeForensic Science International, 2017
- Quantifying randomly acquired characteristics on outsoles in terms of shape and positionForensic Science International, 2016
- Generalized Linear Models2010
- hglm: A Package for Fitting Hierarchical Generalized Linear ModelsThe R Journal, 2010
- Statistics for Spatial Data1993
- A Survey of Men's FootwearJournal of the Forensic Science Society, 1977