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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.

Consideration of influences stemming from differences in local features and disaster characteristics are necessary to grasp economic damage (both from direct damage and indirect damage) quickly and correctly. An econometric approach to estimate economic damage attains correct estimation of economic damage, when it is difficult to find the time and financial support for conducting largescale investigations and analysis of the vast quantity of data. Moreover, the amount of economic damage by natural disaster types cannot be divided, so the economic damage for every disaster cannot be acquired. Using the data of the Chilean Earthquake Tsunami that influenced Japan by a tsunami but not an earthquake in 1960, this study proposed a continuity compensation formula with the influential factors of tsunami height and relationship between the direct economic damage by the tsunami and the financial resources of the people. Moreover, we propose an analysis method for a reconstruction plan making use of the formula to estimate the amount of direct economic damage, and conducted a basic study toward evaluating the effects of reconstruction plans after the Tohoku Earthquake.