Abstract
Crime and violation are the threat to justice and meant to be controlled. Accurate crime prediction and future forecasting trends can assist to enhance metropolitan safety computationally. The limited ability of humans to process complex information from big data hinders the early and accurate prediction and forecasting of crime. The accurate estimation of the crime rate, types and hot spots from past patterns creates many computational challenges and opportunities. Despite considerable research efforts, yet there is a need to have a better predictive algorithm, which direct police patrols toward criminal activities. Previous studies are lacking to achieve crime forecasting and prediction accuracy based on learning models. Therefore, this study applied different machine learning algorithms, namely, the logistic regression, support vector machine (SVM), Naive Bayes, k-nearest neighbors (KNN), decision tree, multilayer perceptron (MLP), random forest, and eXtreme Gradient Boosting (XGBoost), and time series analysis by long-short term memory (LSTM) and autoregressive integrated moving average (ARIMA) model to better fit the crime data. The performance of LSTM for time series analysis was reasonably adequate in order of magnitude of root mean square error (RMSE) and mean absolute error (MAE), on both data sets. Exploratory data analysis predicts more than 35 crime types and suggests a yearly decline in Chicago crime rate, and a slight increase in Los Angeles crime rate; with fewer crimes occurred in February as compared to other months. The overall crime rate in Chicago will continue to increase moderately in the future, with a probable decline in future years. The Los Angeles crime rate and crimes sharply declined, as suggested by the ARIMA model. Moreover, crime forecasting results were further identified in the main regions for both cities. Overall, these results provide early identification of crime, hot spots with higher crime rate, and future trends with improved predictive accuracy than with other methods and are useful for directing police practice and strategies.
Highlights
Criminality is a negative phenomenon, which occurs worldwide in both developed and underdeveloped countries
This study aims to analyze crime prediction in the Chicago and Los Angeles datasets [18], (1) improving the predictive accuracy compared to results in the recent literature by implementing the Logistic Regression, support vector machine (SVM), Naïve Bayes, k-nearest neighbors (KNN), Decision Tree, multilayer perceptron (MLP), Random Forest, XGBoost algorithms, (2) time-series analysis by long-short term memory (LSTM), (3) creating a visual summary through exploratory data analysis, and (4) crime forecasting for the crime rate and high intensity crime areas for subsequent years by using an autoregressive integrated moving average (ARIMA) model
The results and discussion part is divided into four sections based on methodology as shown in Fig. 1; predictive accuracy, time series analysis through LSTM, exploratory data analysis, and forecasting with an ARIMA model
Summary
Criminality is a negative phenomenon, which occurs worldwide in both developed and underdeveloped countries. Public safety is a considerable factor for secure environments when people travel or move to new places [3]. Crimes take place due to various circumstances including specific motives, human nature and behavior, critical situations and poverty [5]. Multiple factors such as unemployment, gender inequality, high population density, child labor, and illiteracy, can cause an increase in violent crimes [6]. The growing and populated cities have a strong correlation with higher crime rates associated with multiple types of environments such as commercial buildings and municipal housing areas [7]. Analyzing the crime reports and statistics are essential to improve the safety and security of humanity while maintaining sustainable development
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