An enhanced ensemble model for crime occurrence prediction using machine learning

Abstract

With the rapid increase of crime, law enforcement departments are struggling to stop crimes and continuously demand automated advanced systems for crime control to provide better protection to the human being in a community. Crime predication plays a vital role in crime control. Crime analysis & prediction can reveal the complexities and hidden patterns in the crime datasets, and it can be used for early decision making. The early researchers attempted to predict the crime using a machine learning model with main factors including time, date and location but overlooked other essential factors. This paper aims to present an enhanced crime prediction algorithm based on ensemble classification technique while identifying several factors that affect the learning model's performance. The correlation of the factors versus the prediction label is analyzed using the Spearman and Pearson techniques to determine the important, influential factors. The prediction model was developed based on Ensemble techniques using the Random forest model with the Voting Classifier. Multiple decision trees had implemented the crime prediction model of this research as the base model and the Logistic Regression and K-Nearest Neighbor algorithm as the sub-models. The final classifier was developed based on using the Graphical User Interface and the REST API methods to predict the possibilities of the crime occurrences at a given specific time in each location. The proposed method can identify the likelihood of a crime in a particular location at a specific time. This helps to implement better strategic and tactical ways to minimize crimes with less risk, as the accuracy of the crime prediction algorithm was 89%.