EPJ Data Science ( IF 3.6 ) Pub Date : 2020-09-29 , DOI: 10.1140/epjds/s13688-020-00247-6 Lingzi Hong , Vanessa Frias-Martinez
Evacuations are a common practice to mitigate the potential risks and damages made by natural disasters. However, without proper coordination and management, evacuations can be inefficient and cause negative impact. Local governments and organizations need to have a better understanding of how the population responds to disasters and evacuation recommendations so as to enhance their disaster management processes. Previous studies mostly examine responses to evacuations at the individual or household level by using survey methods. However, population flows during disasters are not just the aggregation of individuals’ decisions, but a result of complex interactions with other individuals and the environment. We propose a method to model evacuation flows and reveal the patterns of evacuation flows at different spatial scales. Specifically, we gathered large-scale geotagged tweets during Hurricane Irma to conduct an empirical study. First, we present a method to characterize evacuation flows at different geographic scales: the state level, considering evacuation flows across southern states affected by Irma; the urban/rural area level, and the county level. Then we demonstrate results on the predictability of evacuation flows in the most affected state, Florida, by using the following environmental factors: the destructive force of the hurricane, the socioeconomic context, and the evacuation policy issued for counties. Feature analyses show that distance is a dominant predictive factor with counties that are geographically closer generally having larger evacuation flows. Socioeconomic levels are positively related to evacuation flows, with popular destinations associated to higher socioeconomic levels. The results presented in this paper can help decision makers to better understand population evacuation behaviors given certain environmental features, which in turn will aid in the design of efficient and informed preparedness and response strategies.
中文翻译:
飓风艾玛期间的疏散流建模和预测
疏散是减轻自然灾害潜在风险和损害的一种常见做法。但是,如果没有适当的协调和管理,疏散效率低下,并可能造成负面影响。地方政府和组织需要对民众如何应对灾害和疏散建议有更好的了解,以增强其灾害管理流程。先前的研究大多使用调查方法在个人或家庭层面上检查对疏散的反应。但是,灾难期间的人口流动不仅是个人决策的汇总,而且是与其他个人和环境复杂互动的结果。我们提出了一种对疏散流进行建模并揭示不同空间尺度上疏散流模式的方法。特别,我们在伊尔玛飓风期间收集了大量带有地理标记的推文,以进行实证研究。首先,我们提出了一种在不同地理尺度上表征疏散流量的方法:在州一级,考虑跨受Irma影响的南部各州的疏散流量;城乡地区一级和县一级。然后,我们通过使用以下环境因素证明在受影响最严重的州佛罗里达州的疏散流量可预测性的结果:飓风的破坏力,社会经济背景以及针对各县发布的疏散政策。特征分析表明,距离是主要的预测因素,在地理上较近的县通常有较大的疏散流量。社会经济水平与疏散流量正相关,与较高的社会经济水平相关的热门目的地。本文给出的结果可以帮助决策者更好地了解给定某些环境特征的人口疏散行为,从而有助于设计有效且知情的准备和响应策略。