Point pattern analysis utilizing controlled randomization for police tactical planning

Law enforcement agencies often rely on crime pattern identification techniques to support their tactical planning. K-function analysis has been one of the most popular crime pattern identification approaches. It has been integrated with point randomization procedures to identify the level of clustering in crimes. One limitation of this integration is that it can only differentiate between a complete random pattern and a clustered point pattern. It is well known that crimes only occur in populated area and the distribution of human population is spatially heterogeneous. A complete random pattern of crimes rarely occurs. The current K-function offers little insights on the clustering levels of crimes given our prior knowledge on the distribution of processes that may have influenced the occurrence of crimes. This study integrates two controlled point randomization procedures with K-function analysis to analyze crime patterns. These two approaches are compared against the complete random pattern and results indicate that the controlled point randomization procedures can reveal detailed information on the underlying processes for point patterns. It can also take into account the underlying processes for the crimes.