NCJ Number
241744
Date Published
April 2013
Length
102 pages
Annotation
This study developed a set of statistically significant classifications of blood spatter patterns resulting from interactions between the victim, the suspect, and the weapon.
Abstract
This study on efforts to develop a set of statistically significant classifications of blood spatter patterns resulted in two key findings: quantitative metrics involving the spatially-dependent size distribution of droplets within a spatter pattern could serve as an objective means of differentiating gunshot and blunt instrument spatter patterns; and the double blind investigation revealed that human assessments yielded low error rates for gunshot spatter patterns, but very high error rates for blunt instrument spatter patterns, 0.2 percent and 37 percent, respectively. Data for the study were obtained by videotaping simulated bloodshedding events using various blunt instruments, and recording the spatter drop size distribution and morphology of different caliber bullets from various distances to the target surface. Photographs of blood spatter patterns from both blunt instruments and guns were produced and then assessed by trained analysts. The goal of the assessment was twofold: provide a set of quantitative error rates and test objective criteria for the classification of medium and high velocity bloodstain patterns. The findings from the analyst's observations suggest that caution should be used when attempting to identify a blood spatter pattern resulting from either a gunshot or blunt instrument impact in the absence of secondary indicia. The findings also suggest the need for further development and refinement of quantitative image analysis procedures based on blood droplet spatial distributions. Implications for policy and practice are discussed. Figures, tables, references, and addenda
Date Published: April 1, 2013
Downloads
Similar Publications
- Sex Estimation Using Metrics of the Innominate: A Test of the DSP2 Method
- ILIAD: A Suite of Automated Snakemake Workflows for Processing Genomic Data for Downstream Applications
- Discrimination Between Human and Animal Blood Using Raman Spectroscopy and a Self-Reference Algorithm for Forensic Purposes: Method Expansion and Validation