Artificial Intelligence and Big Data for Disaster Risk Reduction: A Bibliometric Mapping - Nuryanty, Cut Dian; Hananingtyas, Izza (Agile Governance and Innovation Measurement Journal, 2026)
The integration of artificial intelligence (AI), machine learning (ML), and big data into disaster governance represents a transformative paradigm shift in how states and institutions anticipate, manage, and respond to disasters. Despite growing scholarly interest, the trajectory and thematic coherence of this interdisciplinary field remain insufficiently mapped. This study aims to systematically map the intellectual landscape of AI and big data research in disaster governance by identifying dominant thematic clusters, tracing the temporal evolution of research priorities, and uncovering structural gaps between technical innovation and governance readiness. This study addresses that gap through a bibliometric analysis of 251 publications retrieved from the Scopus database (2006–April 2026), employing keyword co-occurrence network analysis and thematic mapping using VOSviewer and RStudio. Three dominant thematic clusters were identified: AI-driven smart governance systems, machine learning-based predictive analytics, and deep learning for real-time sensing. The findings reveal a clear evolution from foundational data-mining methods (2020–2021) to the integration of IoT and social media analytics (2022–2023), and, most recently, to precision approaches including spatio-temporal risk assessment and remote sensing (2024–2025). Critically, the analysis uncovers a structural gap between technical innovation and governance readiness, particularly in terms of institutional adoption, ethical frameworks, and digital equity. These findings contribute an evidence-based roadmap that advances both theoretical understanding of the AI–governance nexus in disaster risk reduction and practical guidance for policymakers designing adaptive, collaborative, and ethically grounded strategies in the data-driven era. Future research should address cross-national governance frameworks, digital equity in AI deployment, and the integration of community-responsive data systems into national disaster risk reduction ecosystems.