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Yuanyuan Li, Wenguo Weng, Tao Chen, & Hongyong Yuan. (2014). A Chinese earthquake database for casualty modelling. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 493–497). University Park, PA: The Pennsylvania State University.
Abstract: In order to conduct empirical casualty modelling in China, Chinese historical earthquake events is the essential basis. However, commonly used casualty databases that focus on Chinese earthquakes and provide comprehensive information rarely exist. Regarding this situation, we derived an earthquake casualty database of Mainland China from authorized Chinese published data sources. The casualty database records 520 earthquake events with magnitude 5.0 and greater where at least one casualty is recorded in the time span from 186 BC through December 2011. Each earthquake case contains information on seismic parameters, deaths tolls, number of heavy injuries and light injuries, as well as areas and population of affected regions from intensity VI to intensity IV. Compared with PAGER-CAT, this casualty database provides 146 unique earthquake events and provides more detailed information on heavy injuries and light injures, as well as areas and exposure population of affected regions. This casualty database is an essential supplement for global casualty databases and provides a basis for earthquake casualty modelling on post-earthquake risk estimation in China.
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Andrea Zielinski, & Ulrich Bügel. (2012). Multilingual analysis of twitter news in support of mass emergency events. In Z.Franco J. R. L. Rothkrantz (Ed.), ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management. Vancouver, BC: Simon Fraser University.
Abstract: Social media are increasingly becoming a source for event-based early warning systems in the sense that they can help to detect natural disasters and support crisis management during or after disasters. In this work-in-progress paper we study the problems of analyzing multilingual twitter feeds for emergency events. The present work focuses on English as “lingua franca” and on under-resourced Mediterranean languages in endangered zones, particularly Turkey, Greece, and Romania Generally, as local civil protection authorities and the population are likely to respond in their native language. We investigated ten earthquake events and defined four language-specific classifiers that can be used to detect earthquakes by filtering out irrelevant messages that do not relate to the event. The final goal is to extend this work to more Mediterranean languages and to classify and extract relevant information from tweets, translating the main keywords into English. Preliminary results indicate that such a filter has the potential to confirm forecast parameters of tsunami affecting coastal areas where no tide gauges exist and could be integrated into seismographic sensor networks. © 2012 ISCRAM.
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