Abstract
This research delves into the application of machine learning algorithms for forecasting crime hotspots by leveraging historical data of public property crime in a major coastal city in southeast China. The study conducts a comparative analysis, emphasizing the predictive efficacy of various machine learning models. Results indicate that the LSTM model surpasses other methods including KNN, random forest, support vector machine, naive Bayes, and convolutional neural networks when utilizing solely historical crime data. Moreover, integrating built environment data such as points of interest (POIs) and urban road network density as covariates into the LSTM model enhances predictive accuracy. These findings bear significance for shaping policing strategies and implementing measures for crime prevention and control.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: International Journal of Advanced Research in Science, Communication and Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.