Advancing public safety and housing solutions: A comprehensive framework for machine learning and predictive analytics in urban policy optimization
1 Cubed Partners LLC Oregon, USA.
2 Independent Researcher, UK.
3 Independent Researcher, Irving TX, USA.
4 Montclair State University, Montclair, New Jersey, USA.
Research Article
International Journal of Frontiers in Science and Technology Research, 2025, 08(01), 024-043.
Article DOI: 10.53294/ijfstr.2025.8.1.0024
Publication history:
Received on 12 December 2024; revised on 18 January 2025; accepted on 21 January 2025
Abstract:
Urban areas worldwide face mounting challenges, including public safety concerns, housing shortages, and efficient resource allocation. Addressing these complexities requires innovative solutions that integrate technology and policy frameworks. This paper proposes an integrated framework leveraging machine learning (ML) and predictive analytics to optimize urban policy-making and provide actionable insights for sustainable urban development. The framework combines real-time data streams with advanced predictive modeling to enhance decision-making in urban governance. Key features include the ability to assess and respond to emerging trends in public safety by analyzing crime patterns, resource deployment, and community risk factors. Similarly, the framework addresses housing shortages by evaluating demand-supply dynamics, forecasting population growth, and identifying areas requiring immediate intervention. A critical review of existing models underscores the limitations of traditional urban policy approaches in managing socioeconomic disparities and environmental influences. Building upon these insights, the proposed framework integrates diverse data sources, such as demographic information, environmental metrics, and public sentiment analysis, to create a holistic view of urban systems. This multidimensional approach ensures that policy recommendations are context-sensitive, equitable, and inclusive. Moreover, the framework emphasizes stakeholder engagement, fostering collaboration among policymakers, urban planners, community leaders, and data scientists. It prioritizes scalability and adaptability, allowing its application across diverse urban contexts and geographic locations. This adaptability ensures that the framework remains relevant as urban environments evolve and new challenges arise. By addressing the interplay between data-driven insights and policy optimization, this research aims to bridge the gap between theoretical models and practical implementation. The framework holds the potential to redefine urban governance by enabling proactive decision-making, equitable resource allocation, and sustainable development. Future research should explore real-world applications of this framework to validate its efficacy and refine its components further.
Keywords:
Machine Learning; Predictive Analytics; Urban Policy; Public Safety; Housing Solutions; Resource Allocation; Socioeconomic Disparities; Data-Driven Decision-Making; Sustainability; Stakeholder Engagement
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Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0