Institute of Criminology, Hebrew University of Jerusalem, Jerusalem, Israel.
Department of Statistics, Hebrew University of Jerusalem, Jerusalem, Israel.
J Forensic Sci. 2022 Sep;67(5):1801-1809. doi: 10.1111/1556-4029.15091. Epub 2022 Jul 19.
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.
鞋类比较用于将嫌疑人的鞋子与犯罪现场发现的鞋印联系起来。法庭科学家将这两个物品进行比较，结论基于类别特征和随机获取特征（RAC），例如划痕或孔。一个重要的问题涉及到鞋底上 RAC 位置的分布，这可以作为比较的基准。本研究使用大约 13000 个 RAC 在 386 个外底上观察到的数据库来检查在鞋底不同区域观察到 RAC 的概率。由于鞋子的形状和接触面不同，并且 RAC 的位置存在测量误差，因此分析有些复杂。提出了一种考虑到这些挑战的方法。所有印象都归一化为标准化轴，以允许比较不同尺寸和接触面积的外底上的 RAC，并将 RAC 定位到鞋底的 14 个亚区之一。假设每个区域的预期频率服从泊松分布，其率参数取决于亚区和接触面。研究了三种不同的估计方法：一种是简单的原始方法、一种是特定鞋子的随机效应模型、以及一种基于条件最大似然的估计方法。结果表明，鞋底的速率不是均匀的，并且 RAC 出现在某些位置的可能性大约是两倍，这与脚部的形态有关。研究结果可以为调查人员确定鞋印的证据价值提供指导。
