This research is to assess the relationship Big Data attributes and disaster response process. The hypothesis are designed to form decision making between situation awareness and disaster response by defining major attribute of Big Data(Volume, Variety, Velocity, Complexity). It is proved whether there is a moderating effect in cause-and-effect relationship by visualizing Big Data.To test the hypotheses, it was conducted a questionnaire survey of civil servants in charge of disaster-related government employees, and collected 320 data(without 12 undependable responses).The research findings are suggested the attributes of accumulation, expandability, flexibility, real-time, analytical, combination of Big Data have a strong effect on disaster manager's situation awareness.

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A crisis alert is issued if a disaster occurs or a symptom of a disaster is detected. To examine the cases in which crisis alerts were issued, there are various types, such as sequential issues from the level “Attention” through the level of “Serious” according to changes in the crisis(COVID-19), the issue of the level “Caution-Serious” with the spread of damage after maintaining the level “Attention” (African Swine Fever), and the issue of the level “Serious” due to large-scale damage as soon as a disaster occurs (Fire). The crisis alert is the starting point for the control of crises, and the response method should differ depending on the crisis of the disaster. This study developed a crisis warning judgment cube based on the possibility of identifying crisis symptoms, the possibility of linked damage, and the possibility of expanding the scope of damage. In addition, by classifying 41 types of disasters into 8 types according to the current laws and institutions, a crisis alert operation plan suitable for the characteristics was presented. The damage of disasters may increase or decrease depending on how the crises are controlled.

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