Crime Rate Prediction And Analysis System

Authors: Asst. Prof. Ami Rasiklal Tank, Shivam Bhart, Sanyam Shah, Shreyanshu Das, Subendu Dubey

Abstract: The increase in data and improvements in machine learning (ML) offer a unique opportunity for public safety functions. This paper presents a review of a Crime Rate Prediction and Analysis System that utilizes government crime data to visualize and predict trends, classify geographical areas, and provide tools for public-facing use. The system is unique because it uses a well-defined, highly functional and well-designed web application, alongside a robust ML backend. The web application contains an interactive, and profile-based, color-coded map, that ranks the severity of crime in districts based on their total IPC crimes, allows for dynamic filtering of crime type, and the ability to search by district. The system also possesses an "AI Suggest" button, a critical, innovative, and unique feature which moves beyond analytical reporting and provides personalized, context-specific recommendations for public safety, thus improving public awareness. This review discusses the system's architecture, uses as both an operational mechanism for law enforcement decision making, and for citizen engagement, ethical concerns around predictive policing, and suggests next steps for deploying this kind of technology. One of the key and innovative features of this system, is the "AI Suggest" module, which goes beyond traditional analytical reports, and produces personalized, context-specific and, tailored safety recommendations for the public, thus bridging the gap between publicly available data, and actionable public knowledge. This review explores the dual-value proposition of the system as a decision-support system for law enforcement agencies (LEAs) to maximize resource allocation, patrol routes, and operational planning, as well as providing transparency for the citizens with personalized risk assessment and safety recommendations. In addition, this paper discusses considerations inherent in such systems, including a rigorous examination of the significant ethical implications that accompany such systems, such as algorithmic bias amplification, data integrity, and societal impacts, as well as ways to ensure ethical mitigation of these effects. Ultimately, this review contends that the system is a meaningful step forward in predictive policing technology, as it has the potential to create a more collaborative, informed, and proactive approach to urban public safety if it is implemented with strict ethical responsibility and oversight.