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Patterns of population displacement during mega-fires in California detected using Facebook Disaster Maps
Environmental Research Letters ( IF 6.7 ) Pub Date : 2020-07-06 , DOI: 10.1088/1748-9326/ab8847
Shenyue Jia 1 , Seung Hee Kim 1, 2 , Son V Nghiem 3 , Paul Doherty 4 , Menas C Kafatos 1
Affiliation  

Facebook Disaster Maps (FBDM) is the first platform providing analysis-ready population change products derived from crowdsourced data targeting disaster relief practices. We evaluate the representativeness of FBDM data using the Mann-Kendall test and emerging hot and cold spots in an anomaly analysis to reveal the trend, magnitude, and agglommeration of population displacement during the Mendocino Complex and Woolsey fires in California, USA. Our results show that the distribution of FBDM pre-crisis users fits well with the total population from different sources. Due to usage habits, the elder population is underrepresented in FBDM data. During the two mega-fires in California, FBDM data effectively captured the temporal change of population arising from the placing and lifting of evacuation orders. Coupled with monotonic trends, the fall and rise of cold and hot spots of population revealed the areas with the greatest population drop and potential places to house the displaced residents. A comparison between the Mendocino Complex and Woolsey fires indicates that a densely populated region can be evacuated faster than a scarcely populated one, possibly due to the better access to transportation. In sparsely populated fire-prone areas, resources should be prioritized to move people to shelters as the displaced residents do not have many alternative options, while their counterparts in densely populated areas can utilize their social connections to seek temporary stay at nearby locations during an evacuation. Integrated with an assessment on underrepresented communities, FBDM data and the derivatives can provide much needed information of near real-time population displacement for crisis response and disaster relief. As applications and data generation mature, FBDM will harness crowdsourced data and aid first responder decision-making.

中文翻译:

使用 Facebook Disaster Maps 检测到加利福尼亚特大火灾期间的人口迁移模式

Facebook Disaster Maps (FBDM) 是第一个提供分析就绪的人口变化产品的平台,这些产品源自针对救灾实践的众包数据。我们使用 Mann-Kendall 检验和异常分析中出现的热点和冷点来评估 FBDM 数据的代表性,以揭示美国加利福尼亚州门多西诺火灾和伍尔西火灾期间人口流离失所的趋势、规模和聚集。我们的结果表明,FBDM 危机前用户的分布与来自不同来源的总人口非常吻合。由于使用习惯,老年人口在 FBDM 数据中的代表性不足。在加利福尼亚发生的两次特大火灾中,FBDM数据有效地捕捉到了疏散命令下达和解除引起的人口时间变化。再加上单调的趋势,人口冷热区的升降,揭示了人口降幅最大的地区和可能安置流离失所者的地方。Mendocino Complex 和 Woolsey 火灾之间的比较表明,人口稠密的地区可以比人口稀少的地区更快地疏散,这可能是因为交通便利。在人烟稀少的火灾多发地区,由于流离失所的居民没有很多选择,应优先安排资源将人们转移到避难所,而在人口稠密地区,他们在疏散期间可以利用他们的社会关系在附近寻求临时停留. 结合对代表性不足社区的评估,FBDM 数据及其衍生物可以为危机响应和救灾提供近乎实时的人口迁移信息。随着应用程序和数据生成的成熟,FBDM 将利用众包数据并帮助急救人员做出决策。
更新日期:2020-07-06
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