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Rapid Cost Estimation for Storm Recovery Using Geographic Information System.

机译:使用地理信息系统快速估算风暴恢复的成本。

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摘要

The present research introduces a new approach to estimate the recovery costs of public property in the aftermath of a storm, by integrating Geographic Information Systems (GIS). Estimating recovery costs for a disaster is a current concern for emergency responders. This work focuses on applying economic indicators, population data, and storm event tracking to GIS for rapidly estimating recovery costs. Firstly, recovery costs of historical events are normalized and adjusted for inflation, wealth, and population. Geospatial analysis is used to predict, manage, and learn political boundaries and population density. Secondly, rapid recovery cost estimation is accomplished by defining population, personal income, and gross domestic product. Finally, a jurisdiction fiscal capacity (JFC) is calculated illustrating the economic capability of jurisdictions to finance public property recovery, based on their economy size. The variability of estimated absolute errors between cost estimates and actual normalized costs are also examined. The results reveal that JFC is a more suitable metric for rapidly estimating recovery costs of public properties than the method presently followed by the Federal Emergency Management Agency. This new approach effectively aids the local government in providing quick cost guidance to recovery responders, while offering the ability to construct accurate recovery cost estimates.
机译:本研究通过集成地理信息系统(GIS)引入了一种新方法来估计风暴过后的公共财产回收成本。估计灾难的恢复成本是紧急响应人员当前的关注点。这项工作的重点是将经济指标,人口数据和风暴事件跟踪应用于GIS,以快速估算恢复成本。首先,将历史事件的恢复成本归一化,并根据通货膨胀,财富和人口进行调整。地理空间分析用于预测,管理和了解政治边界和人口密度。其次,通过定义人口,个人收入和国内生产总值来完成快速恢复成本估算。最后,根据辖区的经济规模,计算出辖区财政能力(JFC),以说明辖区为公共财产追回提供资金的经济能力。还检查了成本估算与实际标准化成本之间的估算绝对误差的变异性。结果表明,与联邦紧急事务管理局目前采用的方法相比,JFC是一种更适合快速评估公共财产回收成本的指标。这种新方法有效地帮助了当地政府,为恢复响应者提供了快速的成本指导,同时提供了构建准确的恢复成本估算的能力。

著录项

  • 作者

    Berrios-Montero, Rolando A.;

  • 作者单位

    The George Washington University.;

  • 授予单位 The George Washington University.;
  • 学科 Engineering.
  • 学位 Ph.D.
  • 年度 2016
  • 页码 150 p.
  • 总页数 150
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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