Abstract

Crimes have been disturbing threats to all the Sri Lankans all over the country. Finding the main variables associated with crimes are very vital for policymakers. Our main goal in this study is to forecast of homicides, rapes and counterfeiting currency from 2013 to 2020 using auto-regressive conditional Poisson (ACP) and auto-regressive integrated moving average (ARIMA) models. All the predictions are made assuming that the prevailing conditions in the country affecting crime rates remain unchanged during the period. Moreover, multiple linear regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis were used to identify the key variables associated with crimes. Profiling of districts as safe or unsafe was performed based on the overall total crime rate of Sri Lanka which is to compare with individual district’s crime rates. Data were collected from the Department of Police and Department of Census and Statistics, Sri Lanka. It is observed that there are 14 safe and 11 unsafe districts in Sri Lanka. Moreover, it is found that the total migrant population and percentage of urban population is positively correlated with total crime. Besides, total migrant population, unemployment rate, mean household income and percentage of the urban population are significant variables for total crimes, and total migrant population, Gini index, mean household income and percentage of the urban population are significant variables for homicides. Random K-nearest neighbour (RKNN) algorithm classified districts as safe and unsafe with 84% of prediction accuracy.

Keywords

autoregressive conditional poisson model, autoregressive integrated moving average, crime analysis, gini index, random k-nearest neighbor algorithm