Information Retrieval and Classification of Real-Time Multi-Source
  Hurricane Evacuation Notices
Tingting Zhao Shubo Tian Jordan Daly Melissa Geiger Minna Jia Jinfeng Zhang
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.