Enhancing Urban Resilience: Smart City Data Analyses, Forecasts, and Digital Twin Techniques at the Neighborhood Level

Andreas F. Gkontzis (), Sotiris Kotsiantis, Georgios Feretzakis and Vassilios S. Verykios
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Andreas F. Gkontzis: School of Science and Technology, Hellenic Open University, 26335 Patras, Greece
Sotiris Kotsiantis: Department of Mathematics, University of Patras, 26500 Patras, Greece
Georgios Feretzakis: School of Science and Technology, Hellenic Open University, 26335 Patras, Greece
Vassilios S. Verykios: School of Science and Technology, Hellenic Open University, 26335 Patras, Greece

Future Internet, 2024, vol. 16, issue 2, 1-44

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.

Keywords: smart cities; digital twins; predictive analytics; artificial intelligence; urban resilience; sustainable urban development; GeoDataFrame; Python; citizens reports (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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