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Developing a Spatial‐Temporal Method for the Geographic Investigation of Shoeprint Evidence
Author(s) -
Lin Ge,
Elmes Gregory,
Walnoha Mike,
Chen Xiannian
Publication year - 2009
Publication title -
journal of forensic sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.715
H-Index - 96
eISSN - 1556-4029
pISSN - 0022-1198
DOI - 10.1111/j.1556-4029.2008.00913.x
Subject(s) - metropolitan area , matching (statistics) , crime scene , fingerprint (computing) , geography , computer science , geographic information system , data mining , cartography , artificial intelligence , medicine , pathology , archaeology
This article examines the potential of a spatial‐temporal method for analysis of forensic shoeprint data. The large volume of shoeprint evidence recovered at crime scenes results in varied success in matching a print to a known shoe type and subsequently linking sets of matched prints to suspected offenders. Unlike DNA and fingerprint data, a major challenge is to reduce the uncertainty in linking sets of matched shoeprints to a suspected serial offender. Shoeprint data for 2004 were imported from the Greater London Metropolitan Area Bigfoot database into a geographic information system, and a spatial‐temporal algorithm developed for this project. The results show that by using distance and time constraints interactively, the number of candidate shoeprints that can implicate one or few suspects can be substantially reduced. It concludes that the use of space‐time and other ancillary information within a geographic information system can be quite helpful for forensic investigation.