This paper presents a rapid or real-time estimation method of the economic value of direct stock damages caused by significant earthquakes in Japan. The result will contribute to both the government and private sectors’ early decision-making, particularly for provisional budget allocation. First, we developed a simple but evidence-based model for estimating stock losses explained by a representative earthquake hazard factor and an exposure factor, i.e., seismic intensity and existing stock of physical assets. The key characteristic of our estimation model is that the dependent variable is prefectural damage amount. Still, the explanatory variables come from municipal sources: we overcome this data availability problem through our estimation process. Second, we carefully checked the model’s specification, estimation, and performance to be soundly applied to a real-time assessment of future earthquake events. We also explain the automated measuring of the prefectural direct loss value and its distribution to every 250 m mesh. Finally, we show two examples of the application of our model; one is the case of the 2018 Northern Osaka Earthquake, and the other is the anticipated Tokyo inland earthquake.

Whenever a natural disaster occurs, a damage assessment must be conducted to determine the extent of the damage caused, in order to quickly and effectively undertake disaster response, recovery, and reconstruction efforts. It is important to consider not only natural phenomena, but the impact of the damage on local communities as well (which is a pressing concern at any disaster site). Although a conventional, field-survey-based disaster assessment can yield solid information, it still takes time to gauge the overall implications. While an SNS system can facilitate information collection in real time, it is riddled with problems such as unreliability, and the challenge of handling vast amounts of data. In this study we analyzed Twitter content that was generated after the 2018 Hokkaido Eastern Iburi Earthquake and was related to disaster response efforts at the site of the disaster, and used it to test an approach that combines and utilizes natural language processing and geo-informatics for disaster assessment. We then verified the use of this process in two different disaster response scenarios. In this paper, we discuss some possible approaches to disaster assessment that utilize SNS information analysis technology.

In this study, an education program for heavy rainfall risk management was developed using the Analysis, Design, Development, Implementation, and Evaluation (ADDIE) model of instructional design (ID) to enhance the disaster response capabilities of schoolchildren to encourage them to think and act responsibly to protect themselves during a disaster following heavy rainfall. The program’s effectiveness was evaluated by its implementation at Nagaoka Municipal Senju Elementary School in Nagaoka City, Niigata Prefecture, which was devastated by the heavy rainfall caused by 2019 Typhoon No.19. The learning effect was confirmed throughout the program. Furthermore, the program has improved because of its implementation and evaluation.

Earthquakes damage physical assets, such as houses, public infrastructure, fields, factories, facilities, as well as inventory of timbers, crops, or products. A direct damage amount is the primary evidence for financial measures to restore and reconstruct the affected areas. Therefore, from a policy perspective, it is essential to estimate it quickly and accurately. Cui et al. have proposed a simple method for estimating direct damages [1, 2]. This study aimed to build a prototype of automatic estimation system and discuss its social implementation. As a result, we succeeded in estimating three earthquakes – the 2018 Osaka Prefecture Northern Earthquake, 2018 Hokkaido Iburi Earthquake, and 2019 Yamagata-oki Earthquake – damage amounts automatically and defining some technical requirements for development. On the other hand, it is necessary to replace the Minryoku index, which is used for Cui’s estimation method and no longer being updated, by new physical assets quantity index, which is continuously updatable. Moreover, the estimation accuracy must be evaluated and improved in finer units of space.

In this study, we developed a tsunami disaster risk reduction (DRR) education program for children with little or no memory/experience of the Great East Japan Earthquake. The objective was to strengthen their disaster response capacity and enable them to think and act to protect their lives from tsunami disasters. The development of this program employed the ADDIE model of Instructional Design in learning theory. Based on the GIGA school concept promoted by Japan, information and communications technology (ICT)-based education and DRR education were integrated into the program from a geographical perspective. Using the ICT-based teaching materials, YOU@RISK Tsunami Disaster Edition, empirical learning was introduced. The town of Shichigahama in Miyagi Prefecture, which was devastated by the tsunami during the Great East Japan Earthquake, was selected as the study target. The study implemented and verified the program with local elementary school students to assess its effectiveness.