Abstract
Data play an important role in disaster mitigation applications, and the integrated employment of multidisciplinary data promotes the development of disaster science. Therefore it is very useful to identify the multidisciplinary data usage in the research of disaster events. In order to discover the correlation between multidisciplinary data and disaster research, three earthquake events, the Tangshan earthquake, the Wenchuan earthquake, and the Haidi earthquake were selected as typical study cases for this paper. A knowledge model for literature data mining was applied to analyze the correlation between earthquake events and multidisciplinary data types. The results indicate that high-cited papers show different data usage trends when compared with whole-set papers and also that data usage for the three earthquake events varies. According to analysis results, the factors that influence multidisciplinary data usage include the characteristics of spatial and temporal elements as well as differing interests of the data users.
Highlights
The development of data acquisition technologies has resulted in the accumulation of massive amounts of data
In disaster mitigation research, data play a key role; the integrated use of multidisciplinary data significantly promotes the development of disaster science
In order to discover the correlation between various kinds of data and specific disaster events, we propose a literature-based analysis model for domain knowledge discovery
Summary
The development of data acquisition technologies has resulted in the accumulation of massive amounts of data. In disaster mitigation research, data play a key role; the integrated use of multidisciplinary data significantly promotes the development of disaster science. According to their various research objectives, researchers select very different types of data for their work. The research approaches and models relevant to our study include literature-based analysis, knowledge discovery, and data mining models. The approach model employed in our study is the specific application of literature-based knowledge discovery and data mining. Many researchers have been working with similar approaches and data mining techniques to develop specific applications. The analysis model proposed in our study is a datacentric knowledge discovery model. Data-centric models are structured as sequences of steps that focus on performing manipulation and analysis of data and information surrounding the data, such as domain knowledge and extracted results (Kurgan et al, 2006)
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