A Review of Data Models and Frameworks in Urban Environments in the Context of AI - H. Patricia McKenna (Urban Science, 2025)
This review paper provides a comprehensive review and analysis of the research and practice literature relating to data models and frameworks pertaining to urban and other AI-rich environments, extending to the planetary environment. Elements of focus include the very definition, along with the nature and stability, of the concept of AI itself; consideration of the notion of “open” in an AI context; data sharing, exchange, access, control, and use; and associated challenges and opportunities. Current gaps and problems in the literature on these data models are identified, giving rise to opportunities for research and practice going forward. One of the key gaps associated with AI models and frameworks lies in meeting the needs of the public, with the current top-down approach to AI design, development, and use emerging as a key problem. Such gaps set the stage for a number of recommendations, including human–AI collaboration; extending understanding of human–AI interactions; risk mitigation associated with artificial superintelligence and agentic approaches; and rethinking current AI models and the very definition of AI. This review paper is significant in that it integrates a SWOT (strengths, weaknesses, opportunities, threats) analysis to synthesize challenges, opportunities, gaps, and problems, offering a roadmap for human–AI interactions and collaborations in urban development.