G-DIF: A geospatial data integration framework to rapidly estimate post-earthquake damage
G-DIF: A geospatial data integration framework to rapidly estimate post-earthquake damage
复制标题
G-DIF：快速估计震后损失的地理空间数据集成框架
作者：
S. Loos;D. Lallemant;J. Baker;J. McCaughey;S. Yun;N. Budhathoki;F. Khan;R. Singh
虽然地震后产生了前所未有的大量建筑物损坏数据，但利益相关者没有一种系统的方法来综合和评估损坏信息，因此留下了许多未使用的数据集。我们提出了一个地理空间数据集成框架(G-DIF)，它使用回归克立格法将稀疏的精确实地调查样本与来自预报或遥感的空间穷尽但不确定的损害数据结合在一起。该框架可以在地震后实施，以产生对损失的空间分布估计，更重要的是，它还可以产生损失的不确定性。一个带有2015年尼泊尔地震后收集的真实数据的示例应用程序说明了回归克里格法如何结合各种数据集-并降低非信息性来源的权重-反映其适应数据类型和质量的特定于上下文的变化的能力。通过对现场调查次数的敏感性分析，证明该方法只需几次调查，就能提供比标准工程预测更准确的结果。
While unprecedented amounts of building damage data are now produced after earthquakes, stakeholders do not have a systematic method to synthesize and evaluate damage information, thus leaving many datasets unused. We propose a Geospatial Data Integration Framework (G-DIF) that employs regression kriging to combine a sparse sample of accurate field surveys with spatially exhaustive, though uncertain, damage data from forecasts or remote sensing. The framework can be implemented after an earthquake to produce a spatially distributed estimate of damage and, importantly, its uncertainty. An example application with real data collected after the 2015 Nepal earthquake illustrates how regression kriging can combine a diversity of datasets—and downweight uninformative sources—reflecting its ability to accommodate context-specific variations in data type and quality. Through a sensitivity analysis on the number of field surveys, we demonstrate that with only a few surveys, this method can provide more accurate results than a standard engineering forecast.
影响因子：
5
作者：
E. Booth;Keiko Saito;R. Spence;G. Madabhushi;R. Eguchi
通讯作者：
E. Booth;Keiko Saito;R. Spence;G. Madabhushi;R. Eguchi