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In recent years, data-driven artificial intelligence (AI) approaches have gained prominence in territorial planning and economic development, enabling policymakers to analyse large datasets and formulate evidence-based strategies. This study aims to develop and apply a data-driven AI approach to analyse past funded projects to support decision-making processes and the development of strategic actions in Italy’s inner territories, focusing on the Valsesia SNAI Inner Area and the enhancement of the Agri-food chains and rural development sector. The study employs data mining techniques such as Latent Dirichlet Allocation (LDA) topic modeling and clustering to analyse thematic and financial data from the “OpenCoesione projects” dataset. Findings highlight that while infrastructure investments are substantial, funding for research, innovation, and business competitiveness in the agri-food sector remains underdeveloped. The study underscores the importance of private-public financing mechanisms and strategic investment to enhance regional development. Conducted within the Branding4Resilience (B4R) project by the Politecnico di Torino Research Unit, this research can contribute to optimizing SNAI strategy implementation and broader territorial policies, fostering the competitiveness of agri-food SMEs and supporting sustainable socio-economic growth in Valsesia.

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Data-Driven AI Approach to Address Territorial Strategies. Why Investing in Agri-Food Sector to Enhance the Valsesia Inner Area

  • 会议论文

  • 发表日期：

    2026年

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  • Diana Rolando,
  • Alice Barreca,
  • Giorgia Malavasi,
  • Manuela Rebaudengo

摘要

In recent years, data-driven artificial intelligence (AI) approaches have gained prominence in territorial planning and economic development, enabling policymakers to analyse large datasets and formulate evidence-based strategies. This study aims to develop and apply a data-driven AI approach to analyse past funded projects to support decision-making processes and the development of strategic actions in Italy’s inner territories, focusing on the Valsesia SNAI Inner Area and the enhancement of the Agri-food chains and rural development sector. The study employs data mining techniques such as Latent Dirichlet Allocation (LDA) topic modeling and clustering to analyse thematic and financial data from the “OpenCoesione projects” dataset. Findings highlight that while infrastructure investments are substantial, funding for research, innovation, and business competitiveness in the agri-food sector remains underdeveloped. The study underscores the importance of private-public financing mechanisms and strategic investment to enhance regional development. Conducted within the Branding4Resilience (B4R) project by the Politecnico di Torino Research Unit, this research can contribute to optimizing SNAI strategy implementation and broader territorial policies, fostering the competitiveness of agri-food SMEs and supporting sustainable socio-economic growth in Valsesia.