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arXiv · 2509.03642

Multilayer networks characterize human-mobility patterns by industry sector for the 2021 Texas winter storm

Melissa Butler·Alisha Khan·Francis Osei Tutu Afrifa·Yingjie Hu·Dane Taylor
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Abstract

Understanding human mobility during disastrous events is crucial for emergency planning and disaster management. We develop a methodology to construct time-varying, multilayer networks where edges encode observed movements between spatial regions (census tracts) and network layers encode movement categories by industry sectors (e.g., schools, hospitals). Using the 2021 Texas winter storm as a case study, we find that people markedly reduced movements to ambulatory healthcare services, restaurants, and schools, but prioritized movements to grocery stores and gas stations. Additionally, we study the predictability of nodes' in- and out-degrees in the multilayer networks, which encode movements into and out of census tracts. Inward movements prove harder to predict than outward movements, especially during the storm. Our findings on the reduction, prioritization, and predictability of sector-specific movements aim to support mobility-related decisions during future extreme weather events.

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Melissa Butler, Alisha Khan, Francis Osei Tutu Afrifa, Yingjie Hu, Dane Taylor. 2025-09-03. Multilayer networks characterize human-mobility patterns by industry sector for the 2021 Texas winter storm. https://arxiv.org/abs/2509.03642

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