A data set of bloodstain patterns for teaching and research in bloodstain pattern analysis: Gunshot backspatters

Bloodstain classification methods: A critical review and a look to the future

Classifying bloodstains is an essential part of Bloodstain Pattern Analysis. Various experts have developed methods. Each method considers the same basic bloodstain pattern types. These use either terminology based on the observable characteristics or the mechanistic cause of the bloodstain patterns as part of the classification process. This review paper considers ten classification methods from fourteen sources, which are used to classify bloodstain patterns. There are fundamental differences in how the patterns are classified, how differentiated the classification is, and whether the classification process uses clear, unambiguous criteria, and is susceptible to contextual bias. Experts have also reported issues with classifying bloodstains that have indistinguishable features. These differences expose key limitations with current classification methods: mechanistic terminology is too heavily relied on, and the classification process is susceptible to contextual bias. The development of an unambiguous classification method, based on directly observable characteristics within bloodstain patterns is recommended for future work.

Using the likelihood ratio in bloodstain pattern analysis

There is an apparent paradox that the likelihood ratio (LR) approach is an appropriate measure of the weight of evidence when forensic findings have to be evaluated in court, while it is typically not used by bloodstain pattern analysis (BPA) experts. This commentary evaluates how the scope and methods of BPA relate to several types of evaluative propositions and methods to which LRs are applicable. As a result of this evaluation, we show how specificities in scope (BPA being about activities rather than source identification), gaps in the underlying science base, and the reliance on a wide range of methods render the use of LRs in BPA more complex than in some other forensic disciplines. Three directions are identified for BPA research and training, which would facilitate and widen the use of LRs: research in the underlying physics; the development of a culture of data sharing; and the development of training material on the required statistical background. An example of how recent fluid dynamics research in BPA can lead to the use of LR is provided. We conclude that an LR framework is fully applicable to BPA, provided methodic efforts and significant developments occur along the three outlined directions.

Automatic Classification of Bloodstain Patterns Caused by Gunshot and Blunt Impact at Various Distances

The forensics discipline of bloodstain pattern analysis plays an important role in crime scene analysis and reconstruction. One reconstruction question is whether the blood has been spattered via gunshot or blunt impact such as beating or stabbing. This paper proposes an automated framework to classify bloodstain spatter patterns generated under controlled conditions into either gunshot or blunt impact classes. Classification is performed using machine learning. The study is performed with 94 blood spatter patterns which are available as public data sets, designs a set of features with possible relevance to classification, and uses the random forests method to rank the most useful features and perform classification. The study shows that classification accuracy decreases with the increasing distance between the target surface collecting the stains and the blood source. Based on the data set used in this study, the model achieves 99% accuracy in classifying spatter patterns at distances of 30 cm, 93% accuracy at distances of 60 cm, and 86% accuracy at distances of 120 cm. Results with 10 additional backspatter patterns also show that the presence of muzzle gases can reduce classification accuracy.

Charts based on millions of fluid dynamics simulations provide a simple tool to estimate how far from its source a specific blood stain can be found

The bloodstain pattern analyst sometimes has to judge if a given stain could originate from a specific location. A wide range of values of the maximum distance that a blood drop can travel have been reported from experiments, ranging from less than one meter to more than 10 m. Here we formulate the problem in a fluid dynamics and data mining framework. The fluid dynamics is solved with Newton's classical equation of motion coupled with well-established models for the gravity and drag forces that bend the trajectories of drops. The parameters screened are the drop size, initial velocity and launch angle, as well as the height of a blood source and the ceiling height. Combining a wide range of values of those five parameters commended the performance of more than 5 million fluid dynamic simulations. Results of these simulations have been searched and mined for parameters directly measurable on a crime scene, such as the stain size and stain ellipticity. The results are presented in simple, easy to use charts, which do not require any knowledge of fluid dynamics from the analyst.