Developing a spatial-temporal method for the geographic investigation of shoeprint evidence

Area of Science:

• Forensic Science
• Geographic Information Systems (GIS)
• Spatial Analysis

Background:

• Forensic shoeprint analysis faces challenges in linking evidence to serial offenders due to data volume and uncertainty.
• Current methods often struggle to efficiently connect multiple shoeprints to a single suspect.
• Unlike DNA or fingerprint analysis, shoeprint data lacks robust methods for serial offender identification.

Purpose of the Study:

• To evaluate a novel spatial-temporal method for analyzing forensic shoeprint data.
• To assess the effectiveness of GIS in reducing uncertainty in shoeprint evidence linkage.
• To improve the efficiency of identifying potential serial offenders through shoeprint analysis.

Main Methods:

• Development and application of a spatial-temporal algorithm using data from the Greater London Metropolitan Area Bigfoot database (2004).
• Integration of shoeprint data into a geographic information system (GIS).
• Interactive application of distance and time constraints for data analysis.

Main Results:

• The spatial-temporal algorithm substantially reduced the number of candidate shoeprints requiring investigation.
• Interactive use of distance and time constraints proved effective in narrowing down suspect pools.
• The method demonstrated potential for implicating one or few suspects from a large dataset.

Conclusions:

• Spatial-temporal analysis within a GIS framework is a valuable tool for forensic investigations.
• The proposed method can significantly decrease uncertainty in linking shoeprints to suspected serial offenders.
• Ancillary information, when integrated with space-time data in GIS, enhances forensic investigation capabilities.