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Smart city article details

Title Enhancing Urban Resilience: Smart City Data Analyses, Forecasts, And Digital Twin Techniques At The Neighborhood Level
ID_Doc 24071
Authors Gkontzis A.F.; Kotsiantis S.; Feretzakis G.; Verykios V.S.
Year 2024
Published Future Internet, 16, 2
DOI http://dx.doi.org/10.3390/fi16020047
Abstract Smart cities, leveraging advanced data analytics, predictive models, and digital twin techniques, offer a transformative model for sustainable urban development. Predictive analytics is critical to proactive planning, enabling cities to adapt to evolving challenges. Concurrently, digital twin techniques provide a virtual replica of the urban environment, fostering real-time monitoring, simulation, and analysis of urban systems. This study underscores the significance of real-time monitoring, simulation, and analysis of urban systems to support test scenarios that identify bottlenecks and enhance smart city efficiency. This paper delves into the crucial roles of citizen report analytics, prediction, and digital twin technologies at the neighborhood level. The study integrates extract, transform, load (ETL) processes, artificial intelligence (AI) techniques, and a digital twin methodology to process and interpret urban data streams derived from citizen interactions with the city’s coordinate-based problem mapping platform. Using an interactive GeoDataFrame within the digital twin methodology, dynamic entities facilitate simulations based on various scenarios, allowing users to visualize, analyze, and predict the response of the urban system at the neighborhood level. This approach reveals antecedent and predictive patterns, trends, and correlations at the physical level of each city area, leading to improvements in urban functionality, resilience, and resident quality of life. © 2024 by the authors.
Author Keywords artificial intelligence; citizens reports; digital twins; GeoDataFrame; predictive analytics; Python; smart cities; sustainable urban development; urban resilience


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