Replication Data for: The Road More Traveled: Evacuation Networks from 10 disasters in the US and Japan

When crisis strikes, where do evacuees go? This question greatly affects how policymakers and first responders should allocate their time, funds, and resources after disaster. While past research compared evacuation rates of cities within the same disaster, evacuation rates among different types of disasters remains under-examined. This mixed methods study compares evacuation patterns from 7640 cities among 10 major disasters in the US and Japan between 2019 and 2020, combining social network analysis, modeling, and case studies. This study highlights that evacuation to some hazards is more alike than others; storms, floods, and power outages trigger clustered evacuation networks, while fires, tornadoes, and power outages result in highly dispersed evacuation networks. Further, cities with similar demographic traits in terms of wealth, population, and social capital tend to see more evacuation between them, with an especially pronounced effect for cities with similar levels of social capital. By uncovering the different shapes and drivers of evacuation networks across different disasters, scholars can clarify where evacuees go and which kinds of cities need additional support after crisis.

危机突发之际，疏散人员将去往何方？这一问题直接影响灾后政策制定者与应急救援人员对时间、资金及各类资源的调配决策。过往研究多聚焦于同一场灾害情境下不同城市的疏散率对比，但针对不同灾害类型间的疏散率差异，相关探讨仍较为匮乏。本项混合方法研究结合社会网络分析（Social Network Analysis）、建模（Modeling）与案例研究方法，对比了2019至2020年间美国与日本10场重大灾害中7640座城市的疏散行为模式。研究表明，不同灾害对应的疏散模式存在显著分化：风暴、洪涝与停电事件会催生集聚型疏散网络，而火灾、龙卷风与停电事件则会形成高度分散的疏散网络。此外，在财富、人口规模与社会资本等人口统计特征维度上相似度较高的城市间，疏散往来更为频繁，其中社会资本水平相近的城市，该效应尤为显著。通过揭示不同灾害下疏散网络的差异化形态与驱动机制，学界可进一步明确灾后疏散人员的流向，以及哪些类型的城市需要在灾后获得额外支援。