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Just Science Podcast: Episode 34: 2018 IPTES: Just Shoeprint Statistics

NCJ Number
251530
Date Published
February 2018
Author(s)
Andrew Kimble
Agencies
NIJ-Sponsored
Publication Type
Report (Technical Assistance), Report (Grant Sponsored), Interview, Instructional Material (Programmed)
Grant Number(s)
2016-MU-BX-K110
Annotation

This podcast consists of an interview with Andrew Kimble, a student at Albany State University (Georgia), who explains his research in how incorporating statistical models in forensic shoeprint analysis can assist in assessing the reliability of a match.

Abstract

Kimble is a forensic science major at Albany State University, which has an accredited forensic science curriculum. His plan after graduation is to apply for employment with the FBI in the field of crime-scene analysis. His work in incorporating statistical models in forensic shoeprints analysis, however, was done at Iowa State University, with a focus on statistical analysis applied to shoeprint analysis and matching. At the time of Kimble's research, there were no statistical analysis models for shoeprint analysis. Kimble's work produced such a statistical model, which showed 90 percent accuracy in the matching of shoes to their wearers. Shoes were scanned for eight statistical points. The key identifying marks were those caused by wear, which distinguished between shoes identified as being the same model from a particular manufacturer. This interview also includes some advice by Kimble for young people interested in pursuing higher education that will lead to a career in forensic science.

Date Created: February 28, 2018