智利野火背景下的疏散模式与社会经济分层
Evacuation patterns and socioeconomic stratification in the context of wildfires in Chile
AI总结：
本研究利用高清手机记录，采用结合回归不连续性与双重差分法的因果推断方法，分析智利瓦尔帕莱索野火期间的疏散模式，发现社会经济地位影响疏散时长、距离与去向，为应急规划提供参考。
摘要：
气候变化正在改变野火的发生频率和强度，导致疏散事件增多，扰乱了人类出行与社会经济结构。这些扰乱影响了人们获取资源、就业和住房的途径，加剧了社区内部已有的脆弱性。理解气候变化、野火、疏散模式与社会经济因素之间的相互作用，对于制定有效的减灾与适应策略至关重要。为应对这一挑战，我们利用高清手机记录，分析了2024年2月2日至3日智利瓦尔帕莱索野火期间的疏散模式。该数据使我们能够追踪灾区内个人的移动情况，为了解人们在严重野火背景下如何应对大规模疏散提供了视角。我们采用结合回归不连续性与双重差分法的因果推断方法，观测野火期间的疏散行为，重点关注社会经济分层。该方法通过比较受灾人群在事件前后的疏散模式，同时考虑灾害阈值处的潜在趋势与不连续性，从而分离出野火对不同社会经济群体的影响。我们发现，许多人夜间离家在外，其中社会经济地位最低阶层的人群离家时间最长。总体而言，人们在疏散期间缩短了出行距离，且社会经济地位最低群体的移动距离最短。起初，由于人们匆忙寻找避难所，移动变得更为随机，但最终会向社会经济地位相近的区域聚集。我们的研究结果表明，社会经济差异对疏散动态存在影响，为应急响应规划提供了有用的见解。
英文摘要：
Climate change is altering the frequency and intensity of wildfires, leading to increased evacuation events that disrupt human mobility and socioeconomic structures. These disruptions affect access to resources, employment, and housing, amplifying existing vulnerabilities within communities. Understanding the interplay between climate change, wildfires, evacuation patterns, and socioeconomic factors is crucial for developing effective mitigation and adaptation strategies. To contribute to this challenge, we use high-definition mobile phone records to analyse evacuation patterns during the wildfires in Valparaíso, Chile, that took place between February 2-3, 2024. This data allows us to track the movements of individuals in the disaster area, providing insight into how people respond to large-scale evacuations in the context of severe wildfires. We apply a causal inference approach that combines regression discontinuity and difference-in-differences methodologies to observe evacuation behaviours during wildfires, with a focus on socioeconomic stratification. This approach allows us to isolate the impact of the wildfires on different socioeconomic groups by comparing the evacuation patterns of affected populations before and after the event, while accounting for underlying trends and discontinuities at the threshold of the disaster. We find that many people spent nights away from home, with those in the lowest socioeconomic segment stayed away the longest. In general, people reduced their travel distance during the evacuation, and the lowest socioeconomic group moved the least. Initially, movements became more random, as people sought refuge in a rush, but eventually gravitated towards areas with similar socioeconomic status. Our results show that socioeconomic differences play a role in evacuation dynamics, providing useful insights for response planning.
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