Data Mining Analytics Approach-Based System for Crime Prediction along Kaduna–Abuja Highway, Nigeria

Abstract

Criminal activities in the Kaduna-Abuja Highway have been an enormous security risk to the commuters, residents, and economic growth in Northern Nigeria. Conventional law enforcement approaches are still reactive and lack data, which restricts the anticipatory and preemptive actions. This paper describes Data Mining Analytics Approach-Based System of forecasting the occurrence of crime along the Kaduna-Abuja highway, using developed models of machine learning. The analysis used three supervised learning algorithms which include Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Random Forest (RF) to represent crime patterns and predict potential types of crime, time and location. These algorithms were selected because they are widely used for classification problems, handle and have shown strong performance in similar prediction studies. Artificial data of 1,500 entries that cited five years of criminal incidences was created and prepared. Class imbalance was dealt with with the help of SMOTE (Synthetic Minority Oversampling Technique), hyperparameters optimization with the help of the GridSearchCV. The experimental outcomes showed that the KNN classifier performed best having an accuracy of 71% then SVM and Random Forest with 66.5% respectively. Analysis of the importance of features showed that the most significant predictors were month, day of the week, number of victims, year, and time of day. Study evidences that predictive analytics based on data can complement the proactive policing practices and help the security agencies to predict the high-risk times and locations along the transport corridors in Nigeria that are critical.