Identification of Reasons for Culpable Homicides and Attempted Murders: A Case Study for the Kelaniya Police Division

Abstract

Culpable Homicides and attempted murders are ultimate crimes that could create ripple effects on a society which could go far beyond the original loss of human life. Owing to the unpredictable nature of such crimes that require complex investigations the objective of this study was to come up with an appropriate model to identify the reason for a culpable homicide or an attempted murder using a statistical approach. This study use data collected from 12 Police stations in Kelaniya Police Division relating to the incidents happened between 2010 and 2020. The Pearson Chi-square test was used in identifying the influential explanatory variables. Out of the 18 variables, 8 predictors including Weapon used, Relationship, Location, Civil Status of the perpetrator were statistically associated with the identified reasons at 5 % level of significance. Multinomial logistic regression followed by four data mining models including classification tree, support vector machine (SVM), k-nearest neighbour (KNN), and probabilistic neural network (PNN) were employed initially with a training and testing set which was randomly selected in the ratio 90:10. The 4 data mining models were then fitted separately by using the bagging technique. The accuracies were compared using the confusion matrixes and rates of misclassifications of the critical classes. Out of the fitted models, the highest accuracy of 93.75 % was shown by the PNN model with a spread of 0.6. The identified model can be used as a decision support tool by crime investigators and relevant authorities for wise decision making.