Abstract
When a large-scale natural disaster occurs, it is necessary to collect damage information within about 10 minutes so that disaster-relief operations and wide-area support (depending on the the scale of the natural disaster) can be initiated. A high-performance method for "spatio-temporal join" which joins time-series grid data (such as results of simulations of natural disasters like tsunamis and fire spreading after a large-scale earthquake) and time-series point data representing people flows is proposed and applied to estimate damage situations following a natural disaster. The results of a performance evaluation of the method show that the response time for joining 100,000 point data and 250,000 grid data is about 50 seconds. They also show that it is possible to apply the proposed method to a real environment in which it is necessary to join one-million point data and hundreds of thousands of grid data within 10 minutes.
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