Using the likelihood ratio in bloodstain pattern analysis

A robust interpolation-based method for forensic soil provenancing: A Bayesian likelihood ratio approach

Soil is a complex and spatially variable material that has a demonstrated potential as a useful evidence class in forensic casework and intelligence operations. Here, the capability to spatially constrain police search areas and prioritise resources by triaging areas as low and high interest is advantageous. Conducted between 2017 and 2021, a forensically relevant topsoil survey (0-5 cm depth; 1 sample per 1 km2) was carried out over Canberra, Australia, aiming to document the distribution of chemical elements in an urban/suburban environment, and of acting as a testbed for investigating various aspects of forensic soil provenancing. Geochemical data from X-Ray Fluorescence (XRF; for total major oxides) and Inductively Coupled Plasma-Mass Spectrometry (ICP-MS; for trace elements) following a total digestion (HF + HNO3) of the fused XRF beads were obtained from the survey's 685 topsoil samples (plus 138 additional quality control samples and six "Blind" simulated evidentiary samples). Using those "Blind" samples, we document a likelihood ratio approach where for each grid cell the analytical similarity between the grid cell and evidentiary sample is attributed from a measure of overlap between the two Cauchy distributions, including appropriate uncertainties. Unlike existing methods that base inclusion/exclusion on an arbitrary threshold (e.g., ± three standard deviations), our approach is free from strict binary or Boolean thresholds, providing an unconstrained gradual transition dictated by the analytical similarity. Using this provenancing model, we present and evaluate a new method for upscaling from a fine (25 m x 25 m) interpolated grid to a more appropriate coarser (500 m x 500 m) grid. In addition, an objective method using Random Match Probabilities for ranking individual variables to be used for provenancing prior to receiving evidentiary material was demonstrated. Our results show this collective procedure generates more consistent and robust provenance maps when applied to two different interpolation algorithms (e.g., inverse distance weighting, and natural neighbour), with different grid placements (e.g., grid shifts to the north or east) and by different theoretical users (e.g., different computer systems, or forensic geoscientists).

Distinguishing between stamping in blood from walking through blood using blood pattern analysis

Bloodstains are typically encountered in violent incidents involving the use of a weapon or physical actions, such as punching, kicking, or stamping. Bloodstain pattern analysis can provide inceptive evidence or intelligence about what happened in an alleged incident, the sequence of events, along with indicating possible suspects if blood is analysed through DNA profiling. This research project focused on the differences in patterns created on footwear during a violent action, such as stamping on a person, and a non-violent action, such as walking through a pool of blood. In this project, several experiments were designed to simulate the stamping and walking actions on a surface wet with blood: carpet, lino flooring, and belly pork meat. Two volunteers with varying body weights were recruited to perform the two actions, using a pair of trainers and a pair of Wellington boots. Defibrinated horse blood was used to simulate real human blood. It was found that the patterns created from stamping and walking through blood differed by the type of pattern and the number and size of stains. The footwear used in the stamping action was characterised by a larger contact stain on the sole than those used in the walking actions: ∼209 mm in length by ∼92 mm in width versus ∼65 mm in length by ∼60 mm in width. The stamping action produced a large number of impact spatters (∼435) on the sides of the footwear versus no impact spatters in the walking actions. The presence of impact spatters was found to be the most prominent feature that differentiated between the two actions. The findings were statistically significant (p < 0.05) and could assist in evaluating whether a defendant was actively involved in a stamping action, or the evidence found was due to innocent reasons.

Bloodstain pattern analysis & Bayes: A case report

The findings from a bloodstain pattern analysis (BPA) may assist in formulating or falsifying scenarios that are considered in the investigative stages of a criminal investigation. When a case proceeds to trial the bloodstain pattern expert may be asked about the relevance of their findings given scenarios that are proposed by the prosecution and defense counsel. Such opinions provided by an expert are highly relevant to police investigation or legal proceedings, but the reasoning behind the opinion or implicit assumptions made by the expert may not be transparent. A proper framework for the evaluation of forensic findings has been developed since the late twentieth century, based on the hierarchy of propositions, Bayesian reasoning and a model for case assessment and interpretation. This framework, when implemented in casework, mitigates some of the risks of cognitive biases, and makes the reasoning and scientific basis for the opinion transparent. This framework is broadly used across forensic science disciplines. In this paper we describe its application to the field of BPA using a case example from the Netherlands Forensic Institute (NFI).

Quantifying forensic investigations involving bloodstain pattern analysis within the UK

Research informed by practice is more likely to have a greater impact on society. However, forensic cases are confidential and thus, real-life data regarding the details of violent crime is usually withheld from the public and academia. Through a partnership between the authors institution and a UK police service, casefiles from 78 criminal investigations from 2012 to 2020 involving Bloodstain Pattern Analysis (BPA) were examined and quantified. The most common methods of assault and weapons used were identified as well as the frequency of different bloodstain pattern classifications. The results of this study will help inform researchers and supply forensic training providers with data derived from forensic practice. Despite a significant body of literature exploring impact patterns and software for calculating the Area of Origin (AO), impact pattern was classified at only 22% of scenes, with sharp-force trauma being the most prevalent form of assault. This paper recommends a review of the BPA terminology, to include additional commonly encountered patterns that are not defined by the current standard.