Analysis of Crime Rates in Rizal Province using Crime Forecasting Models

Crime is one of the major problems of countries all over the world, and the Philippines is no exception. Crime prediction and prevention strategies are vital for police forces to face inevitable increases in the crime rate as a side effect of the growth of the urban population. This paper focuses on determining the significant predictors using Pearson Correlation and prediction of crime and crime rates in Rizal Province. Different time-series forecasting models were compared and evaluated to determine the best forecasting model in the crime datasets. Datasets include the disposition of troops, station raw materials/resources and historical data of crime statistics from 2013 to 2017 which were extracted from fourteen (14) municipal police stations of Rizal Province. MAPE was used to determine the accuracy of each model. In testing the normality of the gathered data, the Shapiro test was used, if normal, t-test will be employed otherwise the Wilcoxon test will be used in testing the statistical significance of the prediction. The prediction results can be useful for the police stations to identify problematic regions to patrol and it can be valuable information in decision making specifically in the disposition of patrol manpower and allocation of station resources for a given urban area to suppress crime.