Welcome to the official website of Journal of Disaster Research.

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.

Myanmar is a thriving country in Southeast Asia and is facing future earthquake risks caused by the Sagaing Fault. Under these circumstances, Yangon must implement earthquake risk reduction measures in future development. Applying the building collapse risk evaluation method proposed by the Tokyo Metropolitan Government, and analyzing current and future urban conditions of Yangon City based on available datasets, this study aimed to (1) evaluate present urban vulnerability focusing on building collapse risk, (2) clarify its future expansion tendency based on residential area development conditions from 2004 to 2018, and (3) estimate future building collapse risk in terms of future urban expansion limitation with urban function and building vulnerability in order to obtain useful information on earthquake risk reduction for future development in Yangon. Mainly, this research clarified as follows: (1) The inventory provided by YCDC (Yangon City Development Committee) showed that wooden buildings and RC accounted for 93.8% of all buildings in Yangon. (2) In order to understand the present urban vulnerability of Yangon based on the Tokyo Metropolitan Government’s method, 567 objective wards were categorized into five ranks according to the building collapse risk value. It indicated that building collapse risk in the Dawpon and Tharkayta Townships, located on the west side of Pazundaung Creek, were the highest. Some newly developed outskirts areas, such as Hlaingtharyar or Dala, also appeared as vulnerable with Ranks 4 and 5. (3) Yangon’s urban development conditions from 2004 to 2018 were visually clarified. Then, the relationships between the number of buildings, residential district area, and population according to townships were analyzed to estimate future development. (4) Finally, two types of urban development scenarios were set: Scenario A based on urban expansion limitation and urban function, and Scenario B based on building vulnerability. Then, the future building collapse risk trend from 2014 until 2040 was estimated. It was found that the Sub-center System would deter future urban sprawl in the future more than the Super CBD Single-core System, and the number of damaged buildings can be reduced by 43.5% at most in Dagon Seikkan.