Forecasting the influx of crime cases using seasonal autoregressive integrated moving average model

Abstract

Crime constitutes a profound challenge to the societal fabric of a nation and often finds its roots in factors such as avarice, destitution, and economic adversity. This study endeavors to proactively address the issue of crime through the employment of a crime forecasting model, aimed at uncovering latent correlations and underlying patterns. Specifically, it employs the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to project the future incidence of criminal cases. The research objectives encompass forecasting crime case numbers through time series analysis, appraising the statistical significance of monthly crime occurrences, and assessing the crime dataset utilizing the MATLAB Econometric Modeler. Leveraging historical crime data spanning from January 2018 to December 2021, sourced from nineteen municipalities in Negros Occidental, Philippines, forms the basis for crime case forecasting. An autoregressive test is applied to ascertain the acceptable confidence interval and goodness of fit for crime occurrences. Furthermore, MATLAB Econometric Modeler employs the Ljung-Box test to differentiate between stationary and non-stationary time series and residual crime cases. Notably, the study reveals a significant cyclic pattern in crime cases occurring every 20 months, underscoring the imperative for targeted crime prevention interventions. This study underscores the necessity for consistent and robust law enforcement measures by local government units across the nineteen municipalities in Negros Occidental, focusing on the five identified categories of criminal cases. It is recommended that these measures be implemented diligently to mitigate crime occurrences in the subsequent twenty-first month. Moreover, the study holds potential for extension to regions grappling with elevated crime rates due to inadequate control strategies in place.