Skip to Main Content
 
Translator disclaimer

Hurricanes are one of the most common natural hazards in the United States. To reduce fatalities and economic losses, coastal states and counties take protective actions, including sheltering in place and evacuation away from the coast. Not everyone adheres to hurricane evacuation warnings or orders. In reality, evacuation rates are far less than 100 percent and are estimated using posthurricane questionnaire surveys to residents in the affected area. To overcome limitations of traditional data collection methods that are costly in time and resources, an increasing number of natural hazards studies have used social media data as a data source. To better understand social media users’ evacuation behaviors, this article investigates whether activity space, social network, and long-term sentiment trends are associated with individuals’ evacuation decisions by measuring and comparing Twitter users’ evacuation decisions during Hurricane Matthew in 2016. We find that (1) evacuated people have larger long-term activity spaces than nonevacuated people, (2) people in the same social network tend to make the same evacuation decision, and (3) evacuated people have smaller long-term sentimental variances than nonevacuated people. These results are consistent with previous studies based on questionnaire and survey data and thus provide researchers a new method to study human behavior during disasters. Key Words: big data, disaster management, evacuation, hurricane, social media.

飓风是美国最常见的自然灾害之一。为了降低生命与经济损失, 沿海各州与各郡县採取保护行动, 包括就地避难和撤离海岸。但并非每个人都遵飓风撤离的警告或命令。实际上, 撤离率远低于百分之百, 并运用飓风过后对灾区居民进行的问卷调查进行评估。为了克服耗费时间与资源的传统数据搜集方法之限制, 有越来越多的自然灾害研究使用社交媒体数据作为数据来源。为了更佳理解社交媒体用户的撤离行为, 本文通过测量并比较推特使用者在2016年马修飓风期间的撤离决定, 探讨活动空间、社交网络和长期的情感趋势是否与个人的撤离决定有关。我们发现, (1)撤离的人较不撤离的人而言, 拥有较大的长期活动空间, (2)处于相同社会网络的人, 倾向做出相同的撤离决定, 以及(3)撤离者较不撤离者拥有较小的长期情感变异。这些研究结果与过往根据问卷和调查的数据之研究相符, 因此提供研究灾害期间人类行为的崭新方法。关键词:大数据, 灾害管理, 撤离, 飓风, 社交媒体。

Los huracanes son una de las amenazas naturales más comunes en los Estados Unidos. Para disminuir fatalidades y pérdidas económicas, los estados y condados costeros adoptan acciones protectoras, lo cual incluye refugios en el lugar y evacuación lejos del litoral. No toda la gente responde a las advertencias y órdenes de evacuación relacionadas con huracanes. En realidad, las tasas de evacuación están bien por debajo del 100 porciento y se calculan mediante cuestionarios de encuesta aplicados a los residentes después del huracán en el área afectada. Para salvar las limitaciones de los métodos tradicionales de recolección de datos, que son costosos en tiempo y recursos, un creciente número de estudios sobre amenazas naturales están usando datos de los medios sociales como su fuente de información. Para entender mejor las conductas de evacuación de los usuarios de los medios sociales, este artículo investiga si el espacio de la actividad, la red social y las tendencias del sentimiento a largo plazo están asociadas con las decisiones de evacuación de los individuos, midiendo y comparando las decisiones de evacuación de los usuarios de Twitter durante el Huracán Matthew en 2016. Descubrimos que (1) la gente evacuada tiene espacios de actividad a largo plazo más grandes que la gente que no se evacuó, (2) la gente que usa la misma red social tiende a tomar la misma decisión de evacuación, y (3) la gente evacuada tiene varianzas sentimentales de largo plazo más pequeñas que las de la gente no evacuada. Estos resultados son consistentes con estudios anteriores basados en datos derivados de cuestionario y observación, y por tanto suministran a los investigadores un nuevo método para estudiar el comportamiento humano durante desastres naturales.

Acknowledgments

We thank the anonymous reviewers for their insightful and constructive comments that significantly improved the article.

Supplemental Material

Supplemental data for this article can be accessed here.

Additional information

Funding

This study was funded by the University of South Carolina (USC) through the ASPIRE (Advanced Support for Innovative Research Excellence) program (13540-17-44820, 13540-18-48955) and the USC College of Arts and Sciences Faculty Initiatives.

Notes on contributors

Yuqin Jiang

YUQIN JIANG is a PhD student in the Department of Geography at the University of South Carolina, Columbia, SC 29208. E-mail: . Her research field is GIScience with a focus on spatial networks, geospatial big data analytics, and visualization with applications to disaster management, human dynamics, and transportation.

Zhenlong Li

ZHENLONG LI is an Assistant Professor in the Department of Geography at the University of South Carolina, Columbia, SC 29208. E-mail: . His primary research field is GIScience with a focus on big data processing and analytics, spatial computing, and geospatial cyberinfrastructure with applications to disaster management, climate data analysis, and human mobility.

Susan L. Cutter

SUSAN L. CUTTER is Carolina Distinguished Professor and Director of the Hazards & Vulnerability Research Institute at the University of South Carolina, Columbia, SC 29208. E-mail: . Her research interests are in environmental hazards, especially the development of geospatial resilience and vulnerability metrics, and the inherent social and spatial inequality of hazard impacts and disaster recovery.
 

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.