Abstract:
Crime analysis using data mining techniques can uncover valuable information to assist law enforcement investigations. This study analyses a crime dataset from the Kingdo...Show MoreMetadata
Abstract:
Crime analysis using data mining techniques can uncover valuable information to assist law enforcement investigations. This study analyses a crime dataset from the Kingdom of Bahrain to demonstrate an integrated framework using exploratory analysis and machine learning for predictive modelling. The study dataset, comprising 720 cases, was first explored to reveal patterns, including prevalent theft and assault crimes concentrated in four areas, which have been assigned a character (A to T) due to data sensitivity. Supervised learning algorithms such as Additive Regression, Random Forest, Naive Bayes, and Decision Tree are then applied to predict unknown offender characteristics such as age, sex, and occupation. The algorithms achieve strong predictive performance. Additive Regression achieved a high correlation coefficient of 0.889 for offender age prediction, and Random Forest achieved 83% accuracy in predicting gender through 10-fold cross-validation. However, challenges arise with unbalanced nationality data. A survey of 22 police officers was conducted, discussing the study results and the feasibility of the predictive modelling approach to enhance investigative efficiency. The responses received were mostly positive, reflecting a strong endorsement of the potential of the proposed method to enhance law enforcement practices. This research highlights a promising methodology that combines exploratory and machine learning techniques to extract actionable insights from crime data. With appropriate caution, it can improve investigative efficiency by making data-driven predictions of the attributes of the offender. Further work should expand datasets and explore advanced algorithms while addressing ethical concerns regarding predictive modelling in criminal justice.
Published in: IEEE Access ( Early Access )