A Spatiotemporal and NLP-Based Framework for Crime Prediction and Prevention

Abstract

The way police prevent crime keeps on changing. Earlier it was about reacting to things after a crime has occurred and it relied on the past. This led to slower responses and poor resource utilization. But now they use evidence to solve and stop crimes before they happen. This study uses techniques like Machine Learning, Geographic Information Systems, and Predictive Modeling that will help us to find high-crime areas and patterns that occur frequently by using resources effectively. Spatiotemporal Analysis and Natural Language Processing (NLP) are the main two ways to examine it. This will help with finding out trends and frequency of crime occurrence. This research accords a foundation that combines Machine Learning with Spatiotemporal and NLP-driven analysis to strengthen crime forecasting. Moreover, NLP techniques can also draw out useful pieces of information from unstructured text data like police reports, witness statements, and posts on social media. This research accords a foundation that combines Machine Learning with Spatiotemporal and NLP-driven analysis to strengthen crime forecasting. Overall, the study concludes that Ethical Incorporation of data science can significantly reinforce public safety while maintaining ethical values and social norms.