Advances in the acquisition, processing, and sharing of information has led to corresponding advances in the operation, management and planning of cities. In this process, a variety of platforms have been used including space borne and aerial observation systems, ground based and mobile sensor networks, community generated data and administrative data resources. Meanwhile, advances in artificial intelligence and deep learning has resulted in the proliferation of data driven models. These models have demonstrated the potential of urban informatics for connecting data to phenomenon. The urban studies community is now seeking a coherence across these information platforms and models. Addressing the question of urban data maturity, this presentation provides examples of some of the advances noted above, illustrating their utility in real scenarios, while highlighting the opportunities and challenges in the platforms integration.
Masoud Ghandehari is Associate Professor of Urban Systems Engineering at New York University. He was trained in engineering at Columbia University, McGill University, and Northwestern University. His research, teaching and outreach are focused on the application of advanced instrumentation and data analysis for urban taxonomy and platform integration. Through the application of sensing, and modeling, he is working on developing methodologies that generate multi-scale data across the urban systems. This work is aimed at the development of novel approaches for understanding the condition, interdependencies, and health of cities and urban inhabitants.