Gap analysis on open data interconnectivity for disaster risk research

Abstract

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. ARTICLE HISTORY

Real-Time Impact Analysis and Response using a New Disaster Metrics: 2018 Sulawesi (Indonesia) Earthquake and Tsunami

“…However, evidence is shown from a search of literature that there is a lack of real-time data 27 for researchers and various stakeholders, which is the main gap in natural disaster impact needs and response. Concerns of the accessibility of real-time data from various sources [27][28][29] include: autonomy, global safety, and also factoring in the cost of corruption and increasing transparency of disaster risk reduction, response, and recovery.…”

Fire Risk Assessment in Dense Urban Areas Using Information Fusion Techniques

“…Each class contains a variety of information gathered and integrated from different sources using spatial analysis. These variables were selected using literature reviews [2,5,12,16,17,21] and structures in Iranian buildings and housing research centers (BHRC), including building fire protection rules and passive defense rules in buildings [26,27]. Moreover, several hours of interviews were conducted to employ the experiences of the firefighting forces in this research.…”

An Empirical Evaluation of Data Interoperability—A Case of the Disaster Management Sector in Uganda

“…Vast amounts of hazard/disaster-related data existing today are typically dispersed geographically and owned by various entities making it difficult to access and reuse for disaster management. As such, Open Data initiatives aiming at increasing the availability of machine-readable data provided under an open license and facilitate data reuse have been advanced [14][15][16]. The FAIR principles defined by [2] provide a guide for ensuring such open disaster related data is findable, accessible, interoperable and reusable (FAIR) by machines/humans to support disaster management functions and so become as valuable as is possible [17].…”