The underlying question that needs to be addressed in crime prevention studies is the spatial movement of a crime pattern over time. This paper describes a method to determine if there has been spatial movement in a crime distribution from one time period to another, and demonstrates this technique using an example of a displacement study from a burglary reduction campaign in Canberra, Australia. The paper begins with a discussion that considers the importance of spatial displacement in recent studies. This is followed by a discussion of the development of a new technique that is appropriate for measuring the change in point patterns over time through the development of random point nearest neighbor distance calculations combined with a Monte Carlo simulation process. This test does not rely on the creation or adoption of formal boundaries within the study region, avoiding the problems of patterning and the Modifiable Areal Unit Problem (MAUP), which are common with a number of spatial displacement and pattern movement studies. This is followed by a section that describes the use of random point nearest neighbor calculations, and then discusses some of the operational factors to be considered with this type of analysis, geocoding limitations, and the necessity to correct for edge effects. The paper ends with a section that describes the application of the Monte Carlo process and presents an example of the application of the technique to a dataset drawn from a city-wide police burglary crackdown in the Australian capital of Canberra. References, tables, figures