Abstract
Understanding population movement during natural disasters is critical for emergency response, resource allocation, and risk mitigation. This study proposes a kernel-based flow extraction framework to infer directional human mobility from aggregated, trajectory-free mobile network data. By integrating kernel density estimation (KDE) and a modified gravity model, the method generates spatiotemporal vector fields that capture flow dynamics without requiring individual tracking. The framework is applied to a case study of the 2025 ML 6.4 (Mwg 5.8) Dapu earthquake in southern Taiwan, using mobile communication data aggregated in 500 m × 500 m grids at 10-minute intervals. The analysis reveals significant mobility changes following the seismic event, with directional shifts transitioning from outward dispersal to inward convergence, especially in densely populated zones. Temporal peaks in anomalous flow patterns were detected within the first hour post-event, with variations across seismic intensity levels and population densities. These findings highlight the spatial heterogeneity of behavioral responses to disasters and underscore the utility of the proposed approach for identifying critical zones of disruption. The framework offers a scalable, privacy-preserving tool for real-time disaster monitoring and supports the development of early warning systems and urban resilience planning.
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Data availability
The datasets used and analysed during the current study available from the corresponding author on reasonable request.
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Funding
This work was supported by National Science and Technology Council (Grant No. NSTC 114-3114-Y-865-001). The financial contributions are gratefully acknowledged.
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M.W.H.: Writing draft, calculations, visualization; C.Y.L., M.C.K.: Data pre-processing; W.S.L., T.Y.C.: review, editing; all authors participated in discussions and approved the final manuscript.
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Huang, MW., Lin, CY., Ke, MC. et al. Analysis of human flow during a natural disaster utilizing trajectory-free mobile network data: a case study of earthquake. Sci Rep (2026). https://doi.org/10.1038/s41598-026-36255-1
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DOI: https://doi.org/10.1038/s41598-026-36255-1
