Machine learning in crime prediction

Abstract

Predicting crimes before they occur can save lives and losses of property. With the help of machine learning, many researchers have studied predicting crimes extensively. In this paper, we evaluate state-of-the-art crime prediction techniques that are available in the last decade, discuss possible challenges, and provide a discussion about the future work that could be conducted in the field of crime prediction. Although many works aim to predict crimes, the datasets they used and methods that are applied are numerous. Using a Systematic Literature Review (SLR) methodology, we aim to collect and synthesize the required knowledge regarding machine learning-based crime prediction and help both law enforcement authorities and scientists to mitigate and prevent future crime occurrences. We focus primarily on 68 selected machine learning papers that predict crime. We formulate eight research questions and observe that the majority of the papers used a supervised machine learning approach, assuming that there is prior labeled data, and however in some cases, there is no labeled data in real-world scenarios. We have also discussed the main challenges found while conducting some of the studies by the researchers. We consider that this research paves the way for further research to help governments and countries fight crime and decrease this for better safety and security.

Swarm Intelligence Response Methods Based on Urban Crime Event Prediction

“…Precision is the ratio of true positives (TP) to the sum of true positives (TP) and false positives (FP), recall is the ratio of true positives (TP) to the sum of true positives (TP) and false negatives (FN), and the F1-score is the harmonic mean of precision and recall. Accuracy refers to the percentage of correct predictions [5,13]. As is shown in Table 7, the oversampled random forest model utilized in this study exhibits a superior performance.…”

“…According to the prediction process shown in Figure 7, crime events were forecasted. The prediction models are evaluated using metrics such as the accuracy, precision, recall, and F1-score, and were compared with algorithms including logistic regression, decision trees, Bayesian methods, random forests, and KNN [5][6][7]. Precision is the ratio of true positives (TP) to the sum of true positives (TP) and false positives (FP), recall is the ratio of true positives (TP) to the sum of true positives (TP) and false negatives (FN), and the F1-score is the harmonic mean of precision and recall.…”

“…With the advancement of artificial intelligence and big data analytics technology, many scholars both domestically and internationally are using spatiotemporal data to predict occurrences of criminal events, providing effective means to reduce crime incidents. Using machine learning, researchers like Jenga [5] have assessed the latest technologies in crime prediction and proposed future research directions in the field of crime prediction. Zhang et al [6], for instance, extracted patterns and features from historical crime data and improved the accuracy of LSTM models by incorporating built environment covariates.…”

Crime Rate Prediction using Machine Learning

“…Besides solving the number of cases being lodged day by day, it is essential to prevent crime that may happen in the future. [11] For this it is essential to analysis crime and carry out a detailed inspection of ongoing crime. In this paper, analysis of vast amount of data is done and meaningful results using machine learning algorithms has been derived.…”

Exploring Crime Rate Trends and Forecasting Future Patterns in Toronto City using Police MCI Data and Deep Learning

“…The study concluded that supervised learning approaches were used more frequently in crime prediction studies, and Logistic Regression is the most powerful method for predicting crime. Another paper (Jenga et al, 2023) evaluated crime prediction techniques using machine learning and discussed the challenges and future work in this field. The researchers used a Systematic Literature Review (SLR) methodology to collect and synthesize the required knowledge, and formulated eight research questions, highlighting that most of the papers used a supervised machine learning approach.…”