Leveraging AI for real time crime prediction, disaster response optimization and threat detection to improve public safety and emergency management in the US
1 Masters of Science in Computer Science, Maharishi International University, Iowa, USA.
2 MBA in SAP ERP and Data Analytics, Maharishi International University, Iowa, USA.
3 Masters of Science in Computer Science, Makerere University, Kampala, Uganda.
4 PhD. Computer Science and Engineering, University of Fairfax, Salem, Virginia, USA.
Review Article
World Journal of Advanced Research and Reviews, 2024, 23(03), 1907–1918
Publication history:
Received on 03 August 2024; revised on 14 September 2024; accepted on 16 September 2024
Abstract:
The United States faces an escalating array of public safety challenges, from violent crime and mass shootings to increasingly severe climate-related disasters. Between 1980 and 2024, the U.S. experienced 387 weather and climate disasters, each with damages surpassing $1 billion, amounting to over $2.74 trillion in costs. Similarly, mass shootings and gun violence reached an alarming frequency, with more than 630 mass shooting incidents recorded by the Gun Violence Archive (GVA) in 2023 alone. The National Interagency Fire Center (NIFC) also reported 56,580 wildfires that burned over 2.7 million acres nationwide in the same year. These figures highlight the pressing need for robust, real-time public safety and emergency management systems to better predict, respond to, and mitigate such incidents.
This paper explores the transformative potential of Artificial Intelligence (AI) in enhancing public safety and emergency management in the U.S. By leveraging AI-driven predictive analytics, machine learning (ML) models, and advanced threat detection algorithms, this study proposes an integrated approach to optimizing crime prevention, disaster response, and public safety operations. The research builds upon recent advancements in predictive analytics, deep learning, and cybersecurity measures. Through the analysis of real-time data from crime statistics, social media, IoT sensors, and environmental conditions, this study aims to demonstrate how AI can reduce response times, anticipate public safety threats, and allocate resources more effectively.
The study will present case studies on the practical application of AI in real-time crime prediction, disaster management, and threat detection, showcasing how law enforcement, emergency management, and cybersecurity teams can use AI to shift from reactive responses to proactive, intelligence-driven operations. Moreover, it will address ethical concerns around AI deployment in public safety, such as privacy, algorithmic bias, and data governance, proposing frameworks for ensuring responsible use of these technologies. The ultimate goal is to offer a strategic roadmap for integrating AI into U.S. public safety infrastructure, enhancing preparedness, and protecting communities from the growing risks of violent crime, natural disasters, and emerging threats.
Keywords:
Artificial Intelligence; Real-Time Prediction; Public Safety; Emergency Management; Crime Prediction; Disaster Response; Threat Detection; Predictive Analysis
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0