When a latent shoeprint is discovered at a crime scene, forensic analysts inspect it for distinctive patterns of wear such as scratches and holes (known as accidentals) on the source shoe’s sole. If its accidentals correspond to those of a suspect’s shoe, the print can be used as forensic evidence to place the suspect at the crime scene. The strength of this evidence depends on the random match probability—the chance that a shoe chosen at random would match the crime scene print’s accidentals. Evaluating random match probabilities requires an accurate model for the spatial distribution of accidentals on shoe soles. A recent report by the President’s Council of Advisors in Science and Technology criticized existing models in the literature, calling for new empirically validated techniques. We respond to this request with a new spatial point process model (code and synthetic data is available as Supplementary Material) for accidental locations, developed within a hierarchical Bayesian framework. We treat the tread pattern of each shoe as a covariate, allowing us to pool information across large heterogeneous databases of shoes. Existing models ignore this information; our results show that including it leads to significantly better model fit. We demonstrate this by fitting our model to one such database.

中文翻译：

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用于评估法证鞋证据的贝叶斯层次模型

当在犯罪现场发现潜在的鞋印时，法医分析人员会检查该鞋印上是否有明显的磨损图案，例如原始鞋底上的划痕和破洞（称为意外磨损）。如果其偶然性与犯罪嫌疑人的鞋子相符，则该印刷品可用作法医证据，将犯罪嫌疑人置于犯罪现场。该证据的强度取决于随机匹配的可能性，即随机选择的鞋子与犯罪现场印刷品的偶然性相匹配的可能性。评估随机匹配的概率需要一个准确的模型，用于鞋底上偶然物的空间分布。总统科学技术顾问委员会的最新报告批评了文献中的现有模型，呼吁采用新的经过实验验证的技术。我们通过在分层贝叶斯框架内开发的针对意外位置的新空间点过程模型（代码和合成数据可作为补充材料）来响应此请求。我们将每只鞋的胎面花纹视为协变量，从而使我们能够在大型异构鞋数据库中汇总信息。现有的模型会忽略此信息。我们的结果表明，包括它可以显着改善模型拟合。我们通过将模型拟合到一个这样的数据库来证明这一点。我们的结果表明，包括它可以显着改善模型拟合。我们通过将模型拟合到一个这样的数据库来证明这一点。我们的结果表明，包括它可以显着改善模型拟合。我们通过将模型拟合到一个这样的数据库来证明这一点。