APPLICATION OF CELL PHONE STATISTICS FOR ESTIMATING STRANDED PEOPLE BEHAVIOR AFTER SEVERE EARTHQUAKE IN TOKYO

In this paper, we present a walking home simulation as anticipated after a large earthquake, and analyze people’s behaviors, walking and stopping, including the crowding of facilities by those unable to walk all the way home. For creating the necessary data for this simulation, we construct a method to estimate the spatiotemporal distribution of people with detailed individual information such as sex-age classification, and home location, by assembling population distribution data (Mobile Spatial Statistics and Person Trip survey data). The walking home simulation results verified significant variations in the crowding of facilities for stranded people due to differences in the day of the week and the time of the earthquake. Locations in Tokyo with insufficient numbers of facilities for stranded people were identified and some spatiotemporal characteristics of crowding, such as changes in crowding with time elapsed since the earthquake, were described

Modeling of population distribution in space and time to support disaster risk management

Despite its importance for Disaster Risk Management (DRM), the mapping of human distribution and population exposure has lagged behind hazard modeling and mapping. Assessing population exposure to actual or potential disasters can benefit all phases of the disaster management cycle, e.g. risk and impact assessment, mitigation, preparedness (including early warning and evacuation), and response. This assessment requires geo-information on population distribution at a range of spatial and temporal scales, as disasters can strike at any time and with little warning, and affect from local to global areas. This thesis comprises contributions of population distribution modeling to advancing Disaster Risk Management and Reduction efforts by: (i) developing geospatial models that improve population distribution datasets at a range of relevant spatial and temporal scales and resolutions; (ii) applying those data to (real) disaster risk scenarios by combining geospatial population layers with geophysical hazard maps; (iii) using spatial analysis for quantitatively and qualitatively assessing human exposure to specific hazards and levels, for cartographic representations and visualization, and for showing contributions to DRM. We conclude that since impacts of hazards and disasters are place and time dependent, several DRM and Disaster Risk Reduction phases and activities would benefit from relying on more spatially-detailed and time-specific assessments of population exposure, at a range of relevant spatio-temporal scales (local to global). Also, improving population distribution data for human exposure assessment requires addressing challenges present in input data and geospatial modeling. While at local scale in data rich environments more detailed and sophisticated models can be developed with acceptable uncertainty, scaling up such approaches to the global domain requires addressing different challenges, such as limitations in data availability, quality, and concepts in order to maximize the range of uses of population data, especially for supporting ongoing international development agreements. Finally, geospatial information on population distribution constitutes crucial baseline data for risk analysis and DRM across a range of hazards and threats, and investing in improving data benefits population-related analyses by detailing and revealing a sharper picture, with the aim of ‘leaving no one behind’