By: Benny Salo, Jukka Siren, Jukka Corander, Angelo Zappala, Dario Bosco, Andreas Mokros and Pekka Santtila
This article discusses the importance of crime linking, which is basically drawing the conclusion that the same offender is responsible for more than one crime, to law enforcement and describes a study in which Bayes' theorem was used in order to do this with a high degree of accuracy. Crime linking has the potential to benefit law enforcement greatly because if multiple crimes can be linked together in a reliable way, then the information from the investigation of the separate crimes can be combined which allows for further conclusions, a greater amount of investigation methods, and the
efforts to solve each separate crime can be joined
together. Because crime linking can be an invaluable asset when presenting in court as well as guiding analysis, it is necessary that the methods used to link the crimes are effective and that their theoretical
assumptions can hold true. Since Bayes' theorem is considered a "revolutionary" method in other disciplines, the authors wanted to see if it would be viable for crime linking as well when it is applied to the behavior of offenders which is why this study was conducted.
This article states that a strength of using Bayesian reasoning in this discipline is that "Bayesian reasoning provides a coherent framework for handling
uncertainty, within which the individual behaviours can be weighed against each other
to reach a conditional probability of any particular crimes being linked" (Salol et.al., 2012). Bayesian reasoning has been used before in other studies with empirical Bayesian approaches which did not yield significant results, but this study used a fully Bayesian approach which hadn't been done before.
In this study, 116 homicides belonging to 19 separate series of
homicides were analyzed in regards to number of victims, time period in which the homicides occurred, age of the offender, etc. Basically all of the details of the crimes were analyzed using a Bayesian model that assigned different probabilities on
observing specific behaviors based on which series that the crime belonged to. The researchers used a leave-one-out cross-validation scenario (LOOCV) in order to test the model. When the results of the scenario were analyzed, it was found that the Bayesian model correctly classified 83.6% of the cases. All in all, the researchers seemed to be very impressed with the accuracy of the Bayesian model and feel that it is a promising method to use for crime linkage as it can distinguish even minor behavior variations and is useful in the beginning of the series which is very important.
I thought this article did a very good job of explaining how useful and accurate the Bayesian model can be when it comes to linking crimes, but it did not go into very much detail about how exactly the researchers went about doing this. It did not actually show the model or explain how it worked which I think would have been beneficial. Other than that, however, the article did a pretty good job of critiquing itself as it mentioned all the limitations of the study in the discussion section. Overall, I found this article to be extremely interesting because it delves more into law enforcement intelligence and crime analysis perspectives which I appreciated.