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Using open building data in the development of exposure data sets for catastrophe risk modelling

机译：在暴露数据集的开发中使用开放式建筑数据进行巨灾风险建模

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One of the necessary components to perform catastrophe risk modelling isinformation on the buildings at risk, such as their spatial location,geometry, height, occupancy type and other characteristics. This is commonlyreferred to as the exposure model or data set. When modelling large areas,developing exposure data sets with the relevant information about everyindividual building is not practicable. Thus, census data at coarse spatialresolutions are often used as the starting point for the creation of suchdata sets, after which disaggregation to finer resolutions is carried outusing different methods, based on proxies such as the populationdistribution. While these methods can produce acceptable results, they cannotbe considered ideal. Nowadays, the availability of open data is increasingand it is possible to obtain information about buildings for some regions.Although this type of information is usually limited and, therefore,insufficient to generate an exposure data set, it can still be very useful inits elaboration. In this paper, we focus on how open building data can beused to develop a gridded exposure model by disaggregating existing censusdata at coarser resolutions. Furthermore, we analyse how the selection of thelevel of spatial resolution can impact the accuracy and precision of themodel, and compare the results in terms of affected residential buildingareas, due to a flood event, between different models.
机译：进行灾难风险建模的必要组成部分之一是有关处于风险中的建筑物的信息，例如其空间位置，几何形状，高度，占用类型和其他特征。通常将其称为曝光模型或数据集。在对大型区域进行建模时，使用有关每个建筑物的相关信息来开发暴露数据集是不可行的。因此，通常将粗略的空间分辨率的人口普查数据用作创建此类数据集的起点，然后基于代理（例如人口分布），使用不同的方法将其分解为更精细的分辨率。尽管这些方法可以产生可接受的结果，但不能认为它们是理想的。如今，开放数据的可用性正在增加，并且有可能获取某些地区的建筑物信息。尽管此类信息通常受到限制，因此不足以生成暴露数据集，但在进行详细说明时仍然非常有用。在本文中，我们重点介绍如何通过以较粗的分辨率分解现有的人口普查数据来利用开放式建筑数据来开发网格化的暴露模型。此外，我们分析了空间分辨率级别的选择如何影响模型的准确性和精确性，并比较了不同模型之间因洪水事件而受影响的住宅建筑面积的结果。

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