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

Author

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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)

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.

Suggested Citation

  • Andreas F. Gkontzis & Sotiris Kotsiantis & Georgios Feretzakis & Vassilios S. Verykios, 2024. "Enhancing Urban Resilience: Smart City Data Analyses, Forecasts, and Digital Twin Techniques at the Neighborhood Level," Future Internet, MDPI, vol. 16(2), pages 1-44, January.
    • Handle: RePEc:gam:jftint:v:16:y:2024:i:2:p:47-:d:1329741
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    References listed on IDEAS

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    1. M. R. Mahendrini Fernando Ariyachandra & Gayan Wedawatta, 2023. "Digital Twin Smart Cities for Disaster Risk Management: A Review of Evolving Concepts," Sustainability, MDPI, vol. 15(15), pages 1-25, August.
    2. Bernd W. Wirtz & Wilhelm M. Müller & Florian W. Schmidt, 2021. "Digital Public Services in Smart Cities – an Empirical Analysis of Lead User Preferences," Public Organization Review, Springer, vol. 21(2), pages 299-315, June.
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    Cited by:

    1. Mohsen Hatami & Qian Qu & Yu Chen & Hisham Kholidy & Erik Blasch & Erika Ardiles-Cruz, 2024. "A Survey of the Real-Time Metaverse: Challenges and Opportunities," Future Internet, MDPI, vol. 16(10), pages 1-52, October.

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