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

• Type: Article in International Scientific Journal
• Date: 2016

Authors

Figueiredo, R (Author) Other; Martina, M (Author) Other

Journal

Title: Natural Hazards and Earth System Sciences

Vol. 16

Pages: 417-429

ISSN: 1561-8633

Publisher: Copernicus

Indexing

ISI Web of Knowledge - 7 Citations

Scopus - 19 Citations

Other information

Authenticus ID: P-00R-CGR

DOI: 10.5194/nhess-16-417-2016

Abstract (EN): One of the necessary components to perform catastrophe risk modelling is information on the buildings at risk, such as their spatial location, geometry, height, occupancy type and other characteristics. This is commonly referred to as the exposure model or data set. When modelling large areas, developing exposure data sets with the relevant information about every individual building is not practicable. Thus, census data at coarse spatial resolutions are often used as the starting point for the creation of such data sets, after which disaggregation to finer resolutions is carried out using different methods, based on proxies such as the population distribution. While these methods can produce acceptable results, they cannot be considered ideal. Nowadays, the availability of open data is increasing and 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 in its elaboration. In this paper, we focus on how open building data can be used to develop a gridded exposure model by disaggregating existing census data at coarser resolutions. Furthermore, we analyse how the selection of the level of spatial resolution can impact the accuracy and precision of the model, and compare the results in terms of affected residential building areas, due to a flood event, between different models.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 13