Visualization of Recovery Situation in Disaster Area by Using Web Reservation Data

Public policy should be decided quickly on the basis of scientific data. However, in urgent situations, e.g., disaster recovery, it takes too long to administer a social survey and collect the results. We have therefore developed a social data collection technique that utilizes the Web reservation data of hotels and bullet trains and propose using this technique to support policymaking in real time. One challenge with using Web data is the difficulty of grasping the real situation due to problems with duplication, so we came up with a method of integrating Web reservation data. We built a social data collection infrastructure using Web data and then compared the integrated data with social survey data of Kyoto and Sendai. Results showed that the integrated data fit the social survey data within 10%. Using these data can show the resilience of hotels and shinkansen. By performing analysis on the basis of this collected data, we can support more timely policymaking. This infrastructure is effective both normal situation and disaster situation.