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.

翻译：理解灾难性事件期间的人类移动模式对于应急规划和灾害管理至关重要。我们开发了一种构建时变多层网络的方法，其中边编码空间区域（人口普查区）之间观测到的移动，网络层则按行业部门（例如学校、医院）对移动类别进行编码。以2021年得克萨斯州冬季风暴为案例研究，我们发现人们显著减少了前往流动医疗服务机构、餐馆和学校的移动，但优先保持了前往杂货店和加油站的移动。此外，我们研究了多层网络中节点入度和出度的可预测性，这些度值编码了进出人口普查区的移动。结果表明，向内移动比向外移动更难预测，尤其是在风暴期间。我们关于特定行业移动的减少、优先级排序和可预测性的研究发现，旨在为未来极端天气事件中与移动相关的决策提供支持。