Open Access
September 2020 A Bayesian hierarchical model for evaluating forensic footwear evidence
Neil A. Spencer, Jared S. Murray
Ann. Appl. Stat. 14(3): 1449-1470 (September 2020). DOI: 10.1214/20-AOAS1334


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.


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Neil A. Spencer. Jared S. Murray. "A Bayesian hierarchical model for evaluating forensic footwear evidence." Ann. Appl. Stat. 14 (3) 1449 - 1470, September 2020.


Received: 1 June 2019; Revised: 1 November 2019; Published: September 2020
First available in Project Euclid: 18 September 2020

MathSciNet: MR4152141
Digital Object Identifier: 10.1214/20-AOAS1334

Keywords: forensic footwear analysis , forensic statistics , hierarchical Bayes , Point processes , Random match probability , Random measures , spatial statistics

Rights: Copyright © 2020 Institute of Mathematical Statistics

Vol.14 • No. 3 • September 2020
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