Department of Architecture and Building Science, Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan.
International Research Institute of Disaster Science, Tohoku University, Sendai 980-8572, Japan.
Int J Environ Res Public Health. 2022 Nov 30;19(23):16051. doi: 10.3390/ijerph192316051.
Recently, global climate change has led to a high incidence of extreme weather and natural disasters. How to reduce its impact has become an important topic. However, the studies that both consider the disaster's real-time geographic information and environmental factors in severe rainstorms are still not enough. Volunteered geographic information (VGI) data that was generated during disasters offered possibilities for improving the emergency management abilities of decision-makers and the disaster self-rescue abilities of citizens. Through the case study of the extreme rainstorm disaster in Zhengzhou, China, in July 2021, this paper used machine learning to study VGI issued by residents. The vulnerable people and their demands were identified based on the SOS messages. The importance of various indicators was analyzed by combining open data from socio-economic and built-up environment elements. Potential safe areas with shelter resources in five administrative districts in the disaster-prone central area of Zhengzhou were identified based on these data. This study found that VGI can be a reliable data source for future disaster research. The characteristics of rainstorm hazards were concluded from the perspective of affected people and environmental indicators. The policy recommendations for disaster prevention in the context of public participation were also proposed.
近年来，全球气候变化导致极端天气和自然灾害频发。如何降低其影响已成为一个重要议题。然而，考虑到灾害实时地理信息和环境因素的研究仍然不足。灾害发生时产生的自愿地理信息 (VGI) 数据为提高决策者的应急管理能力和公民的灾害自救能力提供了可能。通过对 2021 年 7 月中国郑州极端暴雨灾害的案例研究，本文使用机器学习研究了居民发布的 VGI。根据 SOS 信息识别出弱势群体及其需求。通过结合社会经济和建成环境要素的开放数据，分析了各种指标的重要性。根据这些数据，确定了郑州市灾害多发中心五个行政区内有避难资源的潜在安全区域。本研究发现，VGI 可以成为未来灾害研究的可靠数据源。从受灾人群和环境指标的角度总结了暴雨灾害的特征。还提出了在公众参与背景下预防灾害的政策建议。
