Big Data Governance Mode Analysis Method for Urban Disaster Risk Response with Case Support
Big data applications encounter several problems,such as data imbalance,insufficient sharing,and a low level of precision and intelligence,in the field of urban disaster risk response(DRR).The construction and improvement of big data governance mode needs relevant knowledge support urgently;however,it faces the dilemma of lack of knowledge caused by scenario complexity and fragmented knowledge distribution.From the perspective of best practice theory,con-sidering the knowledge learning of historical cases,this paper proposes a DRR big data governance mode analysis method based on case support.The core of the case-based method is to retrieve and transfer the governance modes that can be used for the target scenario through scenario similarity matching and build the available mode set.Targeting the retrieved avail-able modes,the mode application effect data are combined to diagnose the mode application problems and select the gover-nance modes.Subsequently,the integration of multi-case modes is completed from the perspective of management com-plexity and implementation cost to generate high-quality modes that can effectively solve the actual big data application problems.The rationality of the proposed analysis method is analyzed through a case study of urban community fire pre-vention in Puyang City,Henan Province.The use case results indicate that the proposed method can accurately transfer and apply historical experience,which is conducive to integrating fragmented knowledge,solving the problem of knowledge scarcity in complex scenarios,and constantly improving the value of big data application and governance mode in the DRR field.
disaster risk responsebig data governance mode analysisknowledge learningscenario complexitycase sup-port
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