Given that forensic evidence from manufactured products, such as shoe prints and tool marks, can have multiple matches, this study demonstrates that the use of space-time information within a geographic information system (GIS) can be helpful. Regarding the spatial factor, the journey-to-crime literature suggests that criminals tend to commit crime in geographic locales that are familiar to them. In spatial journey-to-crime profiling, suspect location is a reference point to a crime location in radius. The creation of a cluster of matched shoeprints is a function of space and time. By setting 1 mile as the active area of a suspect, and two matched shoeprints within 1 or 2 hours, analysts can reasonably speculate that the two shoeprints were from the same suspect. As time increases, another suspect with the same shoeprint may come to the scene. For this reason, the same two matched shoeprints a few months apart may or may not suggest the same suspect. In order to infer from one shoeprint evidence location to another, researchers developed a self-exclusion algorithm by using a space buffer and a time buffer to eliminate points. Under the algorithm both distance and time threshold are specified. For convenience, distance is specified as 2 miles, and times as 10 days. A starting point is randomly selected from all the matched shoeprints for a particular shoe print code. If the selected point is within the specified distance from another shoeprint site, the point is retained; otherwise, it is dropped from consideration. The retained point is checked to determine whether it is within the specified time duration to the closed point. 6 figures, 1 table, and 19 references