Optimized LSTM-based Machine Learning Framework for Real-Time Crime Hotspot Prediction

The detection of crime hotspot can improve public safety, assist the law enforcement authorities in resource allocation, and deter crimes. The crime patterns, however, are often complex and ever-changing; this makes traditional approaches ineffective for analyzing large volumes of time-based data. The problem addressed here involves the application of (DL) for analyzing crime data and identifying hotspots. The system applies an LSTM algorithm that is well-suited for handling sequential data to analyze past crime data and find patterns and trends over time. The LSTM model is trained on various datasets so that the system learns to precisely pinpoint areas prone to crime. For seamless integration with real-time data and future crime mapping applications, the trained model is uploaded to a centralized data server. This approach surpasses the shortcomings of traditional methods in improving the accuracy and scalability of crime hotspot detection. It is a flexible approach to analyzing changing crime patterns and provides valuable insights for crime prevention efforts, paving the way for advancements in predictive policing and urban security planning.