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Updated: May 26, 2026
09:47
Spotting Cheetahs: Identifying Individuals by Their Footprints
Published on: May 1, 2016
A Bayesian network approach to evaluating footwear evidence.
Danyela Kellett1,2, David Lagnado3, Ruth Morgan1,4,5
1Department of Security and Crime Science, University College London, 35 Tavistock Square, London, WC1H 9EZ, UK.
Forensic Science International. Synergy
|May 25, 2026
Summary
Forensic footwear analysis interpretation faces challenges. Bayesian Networks offer a transparent solution for evaluating evidence, improving communication within the UK criminal justice system.
Area of Science:
- Forensic Science
- Criminal Justice
Background:
- The interpretation, evaluation, and communication of forensic footwear findings significantly impact their perceived value and application.
- Challenges in forensic evaluation stem from current methods of footwear analysis and interpretation in England and Wales.
- The broader forensic science discipline faces similar endemic issues in clarity and robustness.
Purpose of the Study:
- To provide an overview of current forensic footwear analysis and interpretation methods in England and Wales.
- To highlight operational challenges encountered in forensic evaluation.
- To propose a Bayesian Network model to address interpretation and communication issues in forensic footwear examination and broader forensic science.
Main Methods:
- Overview of current forensic footwear analysis and interpretation practices.
- Development and population of Bayesian Network models using operational database data and expert qualitative input.
- Application of the R v T judgment as a case study for demonstrating transparency in evidence communication.
- Sensitivity analysis of data from various sources to explore potential likelihood ratio ranges.
Main Results:
- Bayesian Network models were developed and populated with relevant data.
- The R v T judgment illustrated the potential for Bayesian Networks to enhance transparency in communicating forensic evidence.
- Sensitivity analysis demonstrated the range of possible likelihood ratio values based on data sources.
Conclusions:
- Bayesian Networks present a viable tool to address challenges in the interpretation, evaluation, and communication of forensic footwear evidence.
- The proposed models can enhance transparency and robustness in forensic science practice, particularly within the UK criminal justice system.
- Further adoption of such tools could improve the overall clarity and value perception of forensic science findings.
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