Artificial Intelligence Techniques and External Factors used in Crime Forecasting in Violence and Property: A Review

Abstract

Crime forecasting is beneficial in providing useful information to authorities in planning effective crime prevention measures. The two types of analysis used in crime forecasting are univariate and multivariate. Comparatively, multivariate analysis provides better forecasting accuracy because of its ability to discover crime patterns not previously seen. Crime is strongly influenced by several external factors, including economic, social and demographic. Hence, an analysis is needed to identify and select relevant factors that influence crime and can later be used to improve forecasting accuracy. Neighborhood Component Analysis (NCA) is a reliable form of analysis for identifying significant relationships between factors and crime data. Several model types have been introduced in crime forecasting, including statistical and artificial intelligence models. Recently, the artificial intelligence model has come into favour because of its ability to handle nonlinearity patterns in crime data well. Within the artificial intelligence model, Gradient Tree Boosting (GTB) shows good performance as it produces a robust and reliable forecast result. GTB uses least square function as a loss function for error fitting during training. Findings show that, in addition to using least square function, implementing other standard mathematical functions that fit to the crime data increases forecasting accuracy. In other cases, both NCA and GTB are sensitive to parameters input. Dragonfly Algorithm (DA) is a promising, nature inspired metaheuristic algorithm that is capable of solving such problems.