Abstract
One of the major roles of government is to curb crime. Despite the measures the government has taken to counteract criminal activity, the security situation in many urban centers has gotten worse. The goal of this study was to create and assess a machine learning model with the core function of forecasting crime categories and utilizing contextual features found in the datasets to visualize the locations in which they occur. This was achieved by combining time, space, and contextual information with machine learning to improve crime prediction and mapping. The datasets were collected from various sources were subjected to a number of machine learning algorithms to evaluate how well they performed. The random forest algorithm emerged as the best algorithm with a classification accuracy of 97% or 0.973301 using the confusion matrix. The longitude and latitude features were used to tag the specific locations of crime occurrences on a map.
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