Three academic papers from the team of Disaster Risk Reduction Knowledge Service (DRRKS) of IKCEST have been published, which enhanced the DRRKS academic influence.
Three academic papers from the team of Disaster Risk Reduction Knowledge Service (DRRKS) of IKCEST have been published, which enhanced the DRRKS academic influence, with the titles of “Disaster Risk Reduction Knowledge Service: A Paradigm Shift from Disaster Data Towards Knowledge Services” “Using Social Media to Mine and Analyze Public Sentiment during a Disaster: A Case Study of the 2018 Shouguang City Flood in China”, and “Research on disaster data management technology and platform progress and the demand it faces”, respectively.
“Disaster Risk Reduction Knowledge Service: A Paradigm Shift from Disaster Data Towards Knowledge Services”
Juanle Wang*, Kun Bu, Fei Yang, Yuelei Yuan, Yujie Wang, Xuehua Han, Haishuo Wei. Disaster Risk Reduction Knowledge Service: A Paradigm Shift from Disaster Data Towards Knowledge Services. Pure and Applied Geophysics. 2019.
Abstract: Earthquakes have caused tremendous damage in China and around the world; the Wenchuan earthquake that occurred in China 10 years ago is among the deadliest earthquakes in history. The importance of earthquake monitoring and seismic data analysis is now recognized in China. However, the effective dissemination of earthquake-related disaster risk reduction (DRR) knowledge to decision-makers and the public has not been adequately addressed. Under the auspices of the United Nations Educational, Scientific, and Cultural Organization (UNESCO), the International Knowledge Centre for Engineering Science and Technology (managed by the Chinese Academy of Engineering) launched the Disaster Risk Reduction Knowledge Service in 2016. This service, based on the development of disaster metadata standards, was constructed to share disaster information and provide thematic knowledge services; it facilitates the integration and sharing of disaster data, disaster maps, expert opinions, institutional knowledge, research literature, and videos. A series of earthquake DRR knowledge services applications have been implemented using this knowledge service platform, including (1) a global earthquake daily distribution map service, (2) a spatiotemporal map of historical earthquakes in the One Belt One Road region, (3) a Wenchuan earthquake disaster relief knowledge service, and (4) a thematic knowledge service for disaster relief work and contingency planning during the Jiuzhaigou earthquake. In the long term, this system will support the conversion of disaster data into DRR knowledge and provide services for international organizations, government institutions, research and educational institutions, enterprises, and the general public.
“Using Social Media to Mine and Analyze Public Sentiment during a Disaster: A Case Study of the 2018 Shouguang City Flood in China”
Xuehua Han, Juanle Wang*, Using Social Media to Mine and Analyze Public Sentiment during a Disaster: A Case Study of the 2018 Shouguang City Flood in China, Interna tional Journal of Geo-Information, 2019, 8, 185.
Abstract: Social media has been applied to all natural disaster risk-reduction phases, including pre-warning, response, and recovery. However, using it to accurately acquire and reveal public sentiment during a disaster still presents a significant challenge. To explore public sentiment in depth during a disaster, this study analyzed Sina-Weibo (Weibo) texts in terms of space, time, and content related to the 2018 Shouguang flood, which caused casualties and economic losses, arousing widespread public concern in China. The temporal changes within six-hour intervals and spatial distribution on sub-district and city levels of flood-related Weibo were analyzed. Based on the Latent Dirichlet Allocation (LDA) model and the Random Forest (RF) algorithm, a topic extraction and classification model was built to hierarchically identify six flood-relevant topics and nine types of public sentiment responses in Weibo texts. The majority of Weibo texts about the Shouguang flood were related to “public sentiment”, among which “questioning the government and media” was the most commonly expressed. The Weibo text numbers varied over time for different topics and sentiments that corresponded to the different developmental stages of the flood. On a sub-district level, the spatial distribution of flood-relevant Weibo was mainly concentrated in high population areas in the south-central and eastern parts of Shouguang, near the river and the downtown area. At the city level, the Weibo texts were mainly distributed in Beijing and cities in the Shandong Province, centering in Weifang City. The results indicated that the classification model developed in this study was accurate and viable for analyzing social media texts during a disaster. The findings can be used to help researchers, public servants, and officials to better understand public sentiments towards disaster events, to accelerate disaster responses, and to support post-disaster management.
“Research on disaster data management technology and platform progress and the demand it faces”
Xuehua Han, Juanle Wang*. Earthquake Information Extraction and Comparison from Different Sources Based on Web Text. International Journal of Geo-Information. 2019, 8, 252.
Abstract: Natural disasters pose a serious threat to human survival and sustainable development and become a common challenge of all countries. As one of the most important supporting conditions, the management of disaster data and platform construction are the fundamental guarantee for scientific research and practical application of disaster risk reduction. This paper summarizes current progress of disaster data management technologies such as disaster data acquisition, integration, sharing and visualization, and disaster data platforms, analyzes the shortcomings of disaster data platforms, and points out main demand about developments of disaster data platform in the following five aspects:(1) Disaster data classification and coding and uniform standards; (2) Metadata-based disaster data discovery and inter-platform related access; (3) Multi-type disaster data integration and visualization; (4) Diversification of disaster data sharing services and online collaboration; (5) Disaster data resource aggregation focusing on key areas.
Provided by the IKCEST Disaster Risk Reduction Knowledge Service System
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