In the years that have passed since the 2011 Great East Japan earthquake, many new findings, insights and suggestions have been made in disaster observation, sensing, simulation, and damage determination on the damage scene. Based on the lessons, challenges for disaster mitigation against future catastrophic natural disasters such as the anticipated Tokyo metropolitan and Nankai Trough earthquakes are made on how we will share visions of potential impact and how we will maximize society’s disaster resilience.

Much of the “disaster big data” obtained is related to the dynamic flow of large populations, vehicles and goods inside and outside affected areas. This has dramatically facilitated our understanding of how society has responded to unprecedented catastrophes.

The key question is how we will use big data in establishing social systems that respond promptly, sensibly and effectively to natural disasters how this understanding will affect adversity and resilience.

Researchers from a wide variety of fields are now working together under the collaborative JST CREST project entitled “Establishing the most advanced disaster reduction management system by fusion of real-time disaster simulation and big data assimilation.” One objective of this project is to identify potential disaster scenarios related to earthquake and tsunami progress in a chained or compound manner and to create new techniques for responsive disaster mitigation measures enabling society to recover.

This special issue on disaster and big data consists of 11 papers detailing the recent progress of this project. As an editor of this issue, I would like to express our deep gratitude for the insightful comments and suggestions made by the reviewers and the members of the editorial committee.

A project titled “Establishing the advanced disaster reduction management system by fusion of real-time disaster simulation and big data assimilation,” was launched as Core Research for Evolutional Science and Technology (CREST) by the Japan Science and Technology Agency (JST). Intended to save as many lives as possible in future national crises involving earthquake and tsunami disasters, the project works on a disaster mitigation system of the big data era, based on cooperation of large-scale, high-resolution, real-time numerical simulations and assimilation of real-time observation data. The world’s most advanced specialists in disaster simulation, disaster management, mathematical science, and information science work together to create the world’s first analysis platform for real-time simulation and big data that effectively processes, analyzes, and assimilates data obtained through various observations. Based on quantitative data, the platform designs proactive measures and supports disaster operations immediately after disaster occurrence. The project was launched in 2014 and is working on the following issues at present.

Sophistication and fusion of simulations and damage prediction models using observational big data: Development of a real-time simulation core system that predicts the time evolution of disaster effect by assimilating of location information, fire information, and building collapse information which are obtained from mobile terminals, satellite images, aerial images, and other new observation data in addition to sensing data obtained by the undersea high-density seismic observation network.

Latent structure analysis and major disaster scenario creation based on a huge amount of simulation results: Development of an analysis and extraction method for the latent structure of a huge amount of disaster scenarios generated by simulation, and creation of severe scenarios with minimum “unexpectedness” by controlling disaster scenario explosion (an explosive increase in the number of predicted scenarios).

Establishment of an earthquake and tsunami disaster mitigation big data analysis platform: Development of an earthquake and tsunami disaster mitigation big data analysis platform that realizes analyses of a huge number of disaster scenarios and increases in speed of data assimilation, and clarifies the requirements for operation of the platform as a disaster mitigation system.

The project was launched in 2014 as a 5-year project. It consists of element technology development and system fusion, feasibility study as a next-generation disaster mitigation system (validation with/without introduction of the developed real-time simulation and big data analysis platform) in the affected areas of the Great East Japan Earthquake, and test operations in affected areas of the Tokyo metropolitan earthquake and the Nankai Trough earthquake.

Various property-damage estimation models have been created on the basis of damage observation data obtained in the aftermath of large earthquakes. In this paper, these independent models are integrated to establish a city-damage simulation model. In addition, two new fire-spread indices are proposed from the viewpoint of firefighting and refuge.

It is very important in disaster prevention planning to estimate the level of human damage after large earthquakes under various scenarios that takes into account the day of week, the time of the disaster, weather conditions, earthquake intensity, etc. There have been many previous studies based on the spatial characteristics of urban areas about evaluating protection against fires, evacuation risks, and the safety of evacuation routes to designated areas. However, no study so far has integrated models of property damage (building collapse, fire spread, and street blockage) and human behavior (rescue activities, firefighting activities, and wide-area evacuation behavior), and carries out simulations in order to analyze human damage in detail. In this paper, we present a survey of previous studies of the methods of evaluating urban-area characteristics, rescue and firefighting activities, and wide-area evacuation, all of which have been discussed as separate issues. We summarize the findings within the respective fields, their methods of evaluation and modeling, and identify their issues. Based on this survey, we point out that the construction of an integrated simulation model requires six important activities. They are to: 1) carry out evaluations on a microscopic scale at the block or street level; 2) use an evaluation index that allows a direct grasp of the expected level of human damage; 3) take into consideration many detailed and concrete disaster scenarios; 4) take into consideration the interactions among rescue participants, firefighting participants and wide-area evacuees, along with the effects of property damage; 5) incorporate the concept of time; and 6) set up comparative scenarios that allow the quantitative evaluation of the effects of various measures or policies. Therefore, it is necessary to construct a model based on the concept of multi-agent simulation (MAS).

There are increasing expectations that social sensing, especially the analysis of social media text as a source of information for COP (Common Operational Picture), is useful for decision-making about responses to disasters. This paper reports on a geo-information and content analysis of three million Twitter texts sampled from Japanese Twitter accounts for one month before and after the 2011 Great East Japan Earthquake disaster. The results are as follows. 1) The number of Twitter texts that include geotag (latitude and longitude information) is too small for reliable analysis. However, a method of detecting the tweet’s location from the tweet’s text using GeoNLP (an automatic technology to tag geo-information from natural language text) is able to identify geo-information, and we have confirmed that many tweets were sent from stricken areas. 2) A comparison of Twitter data distribution before and after the disaster occurred does not identify clearly which areas were significantly affected by the disaster. 3) There were very few Twitter texts that included information about the damage in affected areas and their support needs.

Rapid growth in communication bandwidth has enabled novel uses of mobile wireless technologies in areas such as smartphone-based user participatory sensing for disaster detection and mitigation. In this manuscript, we discuss novel approaches to resolve fundamental problems that currently hamper the effective utilization of user participatory sensing in this critical application domain. Our approaches to address major challenges related to energy efficiency, collaboration, privacy, ease of deployment, and robustness of communication can be integrated with external systems in a complementary manner to overcome the limitations of current disaster detection and mitigation systems that rely on expensive stationary devices.

Real-time estimation of people distribution immediately after a disaster is directly related to disaster reduction and is also highly beneficial in society. Recently, traffic estimation research has been actively performed using data assimilation techniques for observation data obtained from mobile phones. However, there has been no research on data assimilation technique using real-time gridded aggregated observation data obtained from mobile phones, which are available and can be used to estimate population flow and distribution in a metropolitan area during a large-scale disaster. In this research, population distribution in an urban area during a disaster was estimated using gridded aggregated observation data obtained from mobile phones, using particle filter. The experimental results indicated that the particle filters enabled high-precision real-time estimation in the Kanto district.

The object-based method we developed to estimate building damage uses high-resolution synthetic aperture radar (TerraSAR-X) data from the 2011 Tohoku earthquake and tsunami. The damage function we developed involves the relationship between changes in the sigma nought values of pre- and postevent TerraSAR-X data and the damage ratio of washed-away buildings. We confirmed that the function performed as expected by estimating the number of washed-away buildings in homogeneous areas, agreeing well with ground truth data verified by a Pearson fs correlation coefficient of 0.99. The same damage function applied at another test site yielded a Pearson’s correlation coefficient of 0.98. These results are sufficient to ensure transferability. We then simplified and semiautomated these processes in an ArcGIS environment, estimating building damage in the city of Sendai within 26 minutes.

The Mw9.0 earthquake hitting the Tohoku area on Japan’s Pacific coast on March 11, 2011, triggered huge tsunamis and a Fukushima Daiichi nuclear power plant breakdown. Due to high radiation levels, plant damage could only be assessed from satellite images. Our study involves four very-high-resolution (VHR) TerraSAR-X/TanDEX-X SAR intensity images taken under different acquisition conditions and used to try and determine reactor building damage. Layover and radar shadow areas were specified first based on building footprint and height, then backscattering patterns in these areas were modeled by introducing sectional views of the target building. Criteria for detecting damage from individual SAR scenes were used to compare simulated backscattering patterns to actual SAR intensity images. Damage to other reactor buildings was then identified based on these criteria. Results were confirmed by comparisons to two optical VHR WorldView-2 images and ground photos.

The real-time traffic state estimation we propose uses a state-space model considering the variability of the fundamental diagram (FD) and sensing data. Serious congestion was caused by vehicle evacuation in many Sanriku coast cities following the great East Japan earthquake on March 11, 2011. Many of the vehicles in these congested queues were caught in the enormous tsunami after the earthquake [1]. Safe, efficient evacuation and rescue and restoration require that dynamic traffic states be monitored in real time especially in natural disasters. Variational theory (VT) based on kinematic wave theory is used for the system model, with probe vehicle and traffic detector data used to for measurement data. Our proposal agrees better with simulated benchmark traffic states than deterministic VT results do.

Studies on disaster countermeasures utilize extensive simulations of earthquake, tsunami, people evacuation, and other targets, generating enormous amounts of data. The continuing development of computational capability has facilitated the increase of the simulation data size and the utilization of such “big data” has become a serious problem. With this background, the present study proposes, from the viewpoint of information science, the simulation data warehouse approach for the interactive analysis of large simulation data and describes a method of realizing a data warehouse. An objective of this study is to integrate different simulation data sets and enable exploratory analysis of multiple accumulated simulation data with high-speed response by data preprocessing. Further, the developed prototype system architecture and a case example of its use are explained.

We have developed a data mining system of parallel distributed processing system which is applicable to the large-scale and high-resolution numerical simulation of ground motion by transforming into ground motion indices and their statistical values, and then visualize their values for the seismic hazard information. In this system, seismic waveforms at many locations calculated for many possible earthquake scenarios can be used as input data. The system utilizes Hadoop and it calculates the ground motion indices, such as PGV, and statistical values, such as maximum, minimum, average, and standard deviation of PGV, by parallel distributed processing with MapReduce. The computation results are being an output as GIS (Geographic Information System) data file for visualization. And this GIS data is made available via the Web Map Service (WMS). In this study, we perform two benchmark tests by applying three-component synthetic waveforms at about 80,000 locations for 10 possible scenarios of a great earthquake in Nankai Trough to our system. One is the test for PGV calculation processing. Another one is the test for PGV data mining processing. A maximum of 10 parallel processing are tested for both cases. We find that our system can hold the performance even when the total tasks is larger than 10. This system can enable us to effectively study and widely distribute to the communities for disaster mitigation since it is built with data mining and visualization for hazard information by handling a large number of data from a large-scale numerical simulation.

Underground spaces have been variously used. Excluding underground floors of individual buildings, underground space in Japan is mainly used for streets, railways, and parking. Stores are often grouped along underground passages to underground railways and parking near main urban terminals.

An accidental underground gas explosion at Shizuoka Station in 1980 led to disaster prevention measures in such spaces, forcing stricter safety standards. Following this was the 1999 Hakata underground mall inundation by the Mikawa River, which has further broadened the attention to the underground space and its inundation risk. Inundation damages in underground malls and spaces had occurred repeatedly since then, however, we believe that the 2012 inundation damage to underground spaces in New York city caused by Hurricane Sandy triggered further reviews of disaster prevention measures against underground spaces in Japan.

Recently, small inundation damages often occurred in underground malls in Japan. With our praying these would not be prior events for possible large disasters, we publish this special issue considering that publishing disaster prevention measures and researches for underground spaces is increasingly important worldwide.

This special issue features inundation damage caused by Hurricane Sandy, Japan’s law systems on antiflood measures in underground spaces, antiflood measures of the subway in Tokyo Metropolitan Area, current situations of antiflood measures in underground spaces.

We would like to express our sincere thanks to those who contributed reports and research papers to this issue.

Hurricane Sandy caused critical damage to subterranean infrastructure in New York and also claimed 285 human lives across the Eastern Seaboard. The storm surge impact easily overwhelmed existing pumping systems, devastating power supply and paralyzing transport. Despite extensive preparations and pre-storm public information efforts, inundation and underground flooding caused causalities. The size of the disaster, sheer scope of damage and multifaceted response spanning the onset through to the recovery phase provides useful lessons for Japan, given its vulnerability to similar storm surges and flooding disasters, such as the Ise Bay Typhoon of 1959. Given this, a delegation composed of members of the Ministry of Land, Infrastructure, Transport and Tourism (MLIT) and Researchers from Japan’s Universities and Academic Societies working in disaster prevention conducted two surveys in 2013 and 2014. This involved hearing from emergency management officers in New York, Washington D.C and coastal communities about their experiences evacuating vulnerable residents and protecting critical infrastructure. The author of this paper was a member of both delegations. Based on fieldwork from these joint surveys and other materials, this paper outlines the scope of the damage that a storm of Sandy’s size was capable of inflicting, and looks at lessons applicable to Japan for preventing similar damage to infrastructure and human life in future storm surge events, and discusses how New York is attempting to become a more resilient city in preparation for the next flooding or storm surge disaster.

Underground spaces are enclosed spaces that collect aboveground floodwater, which causes the inundation height to rise rapidly and thus makes them extremely dangerous spaces from the standpoint of water disasters. Due to the changing rainfall patterns in recent years, incidences of inundation damage to underground spaces have increased. In this situation, the Ministry of Land, Infrastructure, Transport, and Tourism of Japan revised the Flood Control Act in May 2015. This paper provides an overview of this latest revision.

This article includes the natural disaster measures taken by Tokyo Metro. In addition to those taken by the former Teito Rapid Transit Authority, Tokyo Metro has been taking new measures – based on experiences from the Great East Japan Earthquake and a large-scale flood damage simulation recently released by the government – in preparation for the inland earthquake expected in the capital and flooding from the Arakawa River.

Cities often have numerous underground spaces such as subways and shopping malls. These hold the possibility of being inundated in a disaster. The reasons of these disasters are urbanization and global warming. The vulnerability of underground spaces to extreme flood and evacuation from there are treated in this paper by using the results obtained by numerical methods in densely urbanized area, Osaka, Japan. It is found that up to 60% of water would flood underground spaces if no counter measures were taken. It was also confirmed such areas should be evacuated before inundation.

In this paper panic and crowd disaster are discussed. In order to prevent heavy casualties in an underground space, it is crucial to determine whether panic escape behavior as a collective phenomenon would occur at the time of an earthquake, flood, and fire, etc. in such a space.

There has yet to be an occurrence of collective panic in an underground space. Panic escape behavior and crowd disaster have the characteristic that despite the low rate of incidence they may cause many casualties, once they happen. Therefore, it is crucial that the measures are taken against panic and crowd surge in the underground in the urban area, although they never occurred in the past.

In this study, the characteristics of evacuation activities from underground areas, under normal conditions and during an earthquake, were investigated in areas around Nagoya station, to calculate the time required to evacuate. An evacuation experiment (along with a questionnaire survey) and evacuation simulation were used, and the effects of signposting were quantitatively evaluated by accurately recording the experiment results and inputting obtained data under a simulation. As a result, basic data related to evacuation from underground areas were obtained, including walking speeds in horizontal and vertical directions, and it was shown that signposting is especially effective when there are a large number of visitors who are not familiar with the area.

Japan uses underground spaces more widely than in other developed countries. Underground spaces around terminal train stations and areas under station plazas are used in developing public pathways with stores. One such area, managed by a single underground city manager, has a floor area of about 80,000m². In 1999, the Mikasa River near an underground area in front of Hakata Station flooded the underground area. The importance of antiflood measures for underground area was pointed out after the Hakata disaster and Japan’s Flood Control Act was partly amended, but measures have not been implemented satisfactorily.

In this paper, the author reviews the current situation in Japan’s underground areas for flood disaster and based on the awareness surveys of underground managers and users, the researches on systemizing antiflood measures for underground areas and the applications of a disaster prevention action plan (TimeLine) for protecting persons from flooding are shown using an example of the underground shopping area in front of Nagoya Station as a case study.

Natural disasters due to local heavy rains and storms have been increasing over and under the ground owing to climate change. Since 2009, NTT DOCOMO, INC. has been working on a nationwide weather observation network, the Environmental Sensor Network, by installing weather observation devices in wireless stations nationwide. In addition, the company has assumed that weather observation data are the trigger factor that may cause disasters and promoted the provision of comprehensive observation data, including river water levels, directly related to disasters such as floods. This paper presents an outline of the environmental sensor network as well as its functions and applications to the disaster prevention field.

Although the importance of Disaster Risk Reduction (DRR) has been recognized internationally as exemplified by the UN World Conference on DRR, funding for emergency relief teams and relief supplies accounts for approximately 66% of the total amount paid for emergency management as a part of international cooperation. Stable economic development requires improvement of DRR and also the mitigation of damage. This paper: 1. Identifies the challenges for enhancing the capacity for emergency management in China; and 2. Examines methods of development cooperation that could help to resolve the above-mentioned challenges. The challenges in China have been brought to light through the Project “Japan-China Cooperation Plan of Earthquake First-Aid Capacity Training” to enhance capacity for earthquake emergency management under JICA’s Technical Cooperation. The timeline analysis for emergency management and after action review were used to assess the China’s emergency management framework and systems. The system and framework of China were also examined by comparing international systems and frameworks such as ISO22320, Japanese DRR system and the United States system. Based on this, curriculum and texts were developed for the National Earthquake Response Support Service (NERSS) to train the personnel engages in earthquake risk management in China. Guidance curricula were developed for the beginners and the intermediate user, which effectiveness was verified by instructional design.

One method of improving the earthquake resistance of shear walls protecting existing nuclear power plants is to retrofit the wall by installing additional rebars with concrete to produce a monolithic structure. We conducted a static loading test to confirm the structural performance of the walls when in-plane and out-of-plane shear forces act on the concrete joint faces produced by this retrofitting method. The test specimens consisted of those in which the concrete joint face was treated differently and a monolithically cast specimen for comparative purposes. The test results showed that the different treatments of the concrete joint face had very little effect on the strength or deformation performance, which were confirmed to be the same as those of the monolithically cast specimen.

To improve the earthquake resistance of antiseismic cylindrical walls of existing nuclear power plants, new seismic retrofitting method is planned in which concrete is poured on one side of the wall after installing additional rebars and shear connectors, to produce a monolithic structure with increased thickness. The loading tests were carried out to verify the validity of this reinforcement method. The specimens consisted of two types: one in which the amounts of inner and outer reinforcements of the doubly arranged wall reinforcements were varied, simulating the retrofitted condition, and one for comparison, in which the inner and outer steel reinforcements were evenly distributed. The experimental results showed no substantial differences in the behaviors of the two specimens up to the ultimate state, each displaying roughly equivalent shear strength.