摘要
Open data strategies are being adopted in disaster-related data particularly because of the need to provide information on global targets and indicators for implementation of the Sendai Framework for Disaster Risk Reduction 2015–2030.In all phases of disaster risk management including forecasting,emergency response and post-disaster reconstruction,the need for interconnected multidisciplinary open data for collaborative reporting as well as study and analysis are apparent,in order to determine disaster impact data in timely and reportable manner.The extraordinary progress in computing and information technology in the past decade,such as broad local and wide-area network connectivity(e.g.Internet),highperformance computing,service and cloud computing,big data methods and mobile devices,provides the technical foundation for connecting open data to support disaster risk research.A new generation of disaster data infrastructure based on interconnected open data is evolving rapidly.There are two levels in the conceptual model of Linked Open Data for Global Disaster Risk Research(LODGD)Working Group of the Committee on Data for Science and Technology(CODATA),which is the Committee on Data of the International Council for Science(ICSU):data characterization and data connection.In data characterization,the knowledge about disaster taxonomy and data dependency on disaster events requires specific scientific study as it aims to understand and present the correlation between specific disaster events and scientific data through the integration of literature analysis and semantic knowledge discovery.Data connection concepts deal with technical methods to connect distributed data resources identified by data characterization of disaster type.In the science community,interconnected open data for disaster risk impact assessment are beginning to influence how disaster data are shared,and this will need to extend data coverage and provide better ways of utilizing data across domains where innovation and integration are now necessarily needed.
基金
This work was supported by the Strategic Priority Research Program of Chinese Academy of Sciences[grant number XDA19020201].
