R-Tree Representations of Disaster Areas Based on Probabilistic Estimation

Abstract

In order to realize a navigation system for refugees in disaster areas, we must reduce computation costs required in setting escape routes. Thus, in this paper, we propose a method for reducing the costs by grasping whole danger regions in a disaster area from a global perspective. At first, we estimate future changes of dangerous regions by a simple way and link all regions with Danger Levels. Then, we index estimated dangerous regions by extended R-tree. In this step, we link the Danger Levels with depths of the extended R-tree and each Danger Level is managed at each depth of the extended R-tree. Finally, we show how our approach effects in setting escape routes.