Abstract
Crime is a bone of contention that can create a societal disturbance. Crime forecasting using time series is an efficient statistical tool for predicting rates of crime in many countries around the world. Crime data can be useful to determine the efficacy of crime prevention steps and the safety of cities and societies. However, it is a difficult task to predict the crime accurately because the number of crimes is increasing day by day. The objective of this study is to apply time series to predict the crime rate to facilitate practical crime prevention solutions. Machine learning can play an important role to better understand and analyze the future trend of violations. Different time-series forecasting models have been used to predict the crime. These forecasting models are trained to predict future violent crimes. The proposed approach outperforms other forecasting techniques for daily and monthly forecast.
Highlights
Urbanization is becoming a global trend [1]
Long short-term memory (LSTM) for regression with time steps is applied on the violent crime in which the previous time step is taken in the series as the input to forecast the output at the time step. is process is applied by setting the columns to be time-step dimension and changing the values of dimension back to 1
In order to perform regression tasks and their validation, the crime data are divided into training and testing data. is study is conducted by using a univariate data structure where UCR_General is the variable used against Dispach_Date_Time
Summary
Urbanization is becoming a global trend [1]. As the city grows, different management challenges increase on a daily basis. Time series models use the statistical properties of the historical data to predict future patterns and trends [7]. Is work compares different time-series analysis models and machine learning models, i.e., ARIMA, simple exponential smoothing (SES), Holt–Winters exponential smoothing (HW), and recurrent neural network (RNN), to predict the crime trends. Is section discusses different time-series forecasting models to predict future crimes. Simple exponential smoothing is the simplest method that is suitable for stationary series It is a time-series forecasting approach for a single parameter without a trend and seasonality. By the given data for 50 weeks of property crime, they forecasted one week ahead from the given observations using the ARIMA model [17] They only compared straightforward techniques and measured the amount of crime over the whole city and not over districts or grid cells. They only compared straightforward techniques and measured the amount of crime over the whole city and not over districts or grid cells. eir approach used grid cells. e data lacked historical information
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