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AI-assisted Real-Time Spatial Delphi: integrating artificial intelligence models for advancing future scenarios analysis

Author

Listed:
  • Yuri Calleo

    (University College Dublin)

  • Amos Taylor

    (University of Turku)

  • Francesco Pilla

    (University College Dublin)

  • Simone Zio

    (University “G. d’Annunzio”)

Abstract

The Real-Time Spatial Delphi represents an innovative method tailored to navigate the complexities of uncertain spatial issues. Adopted in Future Studies contexts, this method excels in developing spatial scenarios and leveraging the collaborative insights of experts within a virtual environment to achieve a consensus regarding territorial dynamics. However, while this method yields invaluable spatial insights and statistical metrics, the final outputs often remain confined to expert circles due to their technical complexity. In addition, the outcomes often lack direct policy implications, as they primarily provide an expansive overview of potential future scenarios. In response to these challenges, this paper proposes integrating text-to-image models and generative pre-trained transformers, into the Real-Time Spatial Delphi process. By adopting these advanced tools during the visioning and planning phases, the method endeavors to transform spatial judgments into visually immersive scenarios, while concurrently crafting actionable policy recommendations suitable for evaluation. To validate the approach, we present a case study in the environmental context, for the cities of Cork, Galway, and Limerick, located in Ireland. Through this application, we contribute to Futures Studies by illustrating the method’s capacity to envision plausible futures in the form of real images, considering the formulation of policies to support decision-making.

Suggested Citation

  • Yuri Calleo & Amos Taylor & Francesco Pilla & Simone Zio, 2025. "AI-assisted Real-Time Spatial Delphi: integrating artificial intelligence models for advancing future scenarios analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 59(2), pages 1427-1459, April.
  • Handle: RePEc:spr:qualqt:v:59:y:2025:i:2:d:10.1007_s11135-025-02073-2
    DOI: 10.1007/s11135-025-02073-2
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    References listed on IDEAS

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    1. Makridakis, Spyros, 1996. "Reply to comments on "Forecasting: its role and value for planning and strategy"," International Journal of Forecasting, Elsevier, vol. 12(4), pages 555-557, December.
    2. Di Zio, Simone & Castillo Rosas, Juan Daniel & Lamelza, Luana, 2017. "Real Time Spatial Delphi: Fast convergence of experts' opinions on the territory," Technological Forecasting and Social Change, Elsevier, vol. 115(C), pages 143-154.
    3. Rowe, Gene & Wright, George, 1999. "The Delphi technique as a forecasting tool: issues and analysis," International Journal of Forecasting, Elsevier, vol. 15(4), pages 353-375, October.
    4. Kim, Hyunuk & Ahn, Sang-Jin & Jung, Woo-Sung, 2019. "Horizon scanning in policy research database with a probabilistic topic model," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 588-594.
    5. Giuffrida, Nadia & Calleo, Yuri & Di Zio, Simone & Pilla, Francesco & Ottomanelli, Michele, 2024. "Spatial participatory planning for urban logistics: A GIS-enhanced Real-Time Spatial Delphi approach," Research in Transportation Economics, Elsevier, vol. 108(C).
    6. Makridakis, Spyros, 1996. "Forecasting: its role and value for planning and strategy," International Journal of Forecasting, Elsevier, vol. 12(4), pages 513-537, December.
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