Identifying Evacuation Needs and Resources Based on Volunteered Geographic Information: A Case of the Rainstorm in July 2021, Zhengzhou, China

Jingyi Gao (), Osamu Murao, Xuanda Pei and Yitong Dong
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Jingyi Gao: Department of Architecture and Building Science, Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan
Osamu Murao: International Research Institute of Disaster Science, Tohoku University, Sendai 980-8572, Japan
Xuanda Pei: Department of Earth Science, Graduate School of Science, Tohoku University, Sendai 980-8578, Japan
Yitong Dong: Department of Architecture and Building Science, Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan

IJERPH, 2022, vol. 19, issue 23, 1-21

Abstract: 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.

Keywords: disaster prevention; volunteered geographic information; evacuation needs; rainstorm; latent dirichlet allocation model; random forest (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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