The effect of type of firearm and shooting distance on pattern distribution, particle dispersion and amount of gunshot residue

Abstract

Abstract Background Evidence can be defined as the object’s availability and/or information that indicates whether a belief or proposition is true or valid. Gunshot residue (GSR) is an important evidence that can serve many roles in ballistic investigation such as shooting distance, type of firearm and ammunition used in shooting related to GSR. However, due to minimal amount of GSR that can be found in crime scene, suitable methods and technique are required in order to obtain the information from the evidence. This action is also known as evidence recovery. When a firearm is fired, soot or particles are discharged from any opening of the firearm and deposited at the vicinity of point of shooting. Results This study emphasized on the examination of the soot/particles produced and pattern distribution of GSR deposited on white cotton cloth target at varying shooting distances (from 3 to 50 cm) using a video spectral comparator. Pattern distribution and GSR particle density are the main factors in determining the shooting distances in clothing. Principle component analysis (PCA) and hierarchical clustering analysis (HCA) were used to classify firearms; the differences in the GSR pattern distribution are highly recognizable. This study showed that the relationship between the GSR particle dispersion and shooting distance was proportionally linear. The results obtained from the shooting test showed that the diameter of GSR distribution and the amount of residues being deposited from shots fired decreased at distances greater than 21 cm. Conclusion This study will help the investigators in determining the shooting distances and evaluating the firearms used. There is a promising method for examination of GSR pattern on the target material which is also important for firing distance estimation.

Keywords

• Gunshot residue
• Pattern distribution
• Particle dispersion
• Close range shooting distance
• Principle component analysis
• Hierarchical cluster analysis