For an approaching disaster, the tracking of time-sensitive critical
information such as hurricane evacuation notices is challenging in the United
States. These notices are issued and distributed rapidly by numerous local
authorities that may spread across multiple states. They often undergo frequent
updates and are distributed through diverse online portals lacking standard
formats. In this study, we developed an approach to timely detect and track the
locally issued hurricane evacuation notices. The text data were collected
mainly with a spatially targeted web scraping method. They were manually
labeled and then classified using natural language processing techniques with
deep learning models. The classification of mandatory evacuation notices
achieved a high accuracy (recall = 96%). We used Hurricane Ian (2022) to
illustrate how real-time evacuation notices extracted from local government
sources could be redistributed with a Web GIS system. Our method applied to
future hurricanes provides live data for situation awareness to higher-level
government agencies and news media. The archived data helps scholars to study
government responses toward weather warnings and individual behaviors
influenced by evacuation history. The framework may be applied to other types
of disasters for rapid and targeted retrieval, classification, redistribution,
and archiving of real-time government orders and notifications.

本研究应用自然语言处理技术，通过网络抓取方法及时对地方发布的飓风撤离通知进行检测和追踪，并利用 Web GIS 系统重新分发实时撤离通知，从而为高级政府机构和新闻媒体提供实时数据，帮助学者研究政府对天气警报的反应和撤离历史对个人行为的影响。该框架还可应用于其他类型的灾害，实现快速和有针对性的检索、分类、分发和存档实时政府命令和通知。