{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T18:06:04Z","timestamp":1773425164100,"version":"3.50.1"},"reference-count":17,"publisher":"Copernicus GmbH","issue":"2","license":[{"start":{"date-parts":[[2016,2,10]],"date-time":"2016-02-10T00:00:00Z","timestamp":1455062400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Nat. Hazards Earth Syst. Sci."],"abstract":"<jats:p>Abstract. 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.<\/jats:p>","DOI":"10.5194\/nhess-16-417-2016","type":"journal-article","created":{"date-parts":[[2016,2,10]],"date-time":"2016-02-10T02:33:01Z","timestamp":1455071581000},"page":"417-429","source":"Crossref","is-referenced-by-count":38,"title":["Using open building data in the development of exposure data sets for catastrophe risk modelling"],"prefix":"10.5194","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2807-3119","authenticated-orcid":false,"given":"R.","family":"Figueiredo","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6283-2732","authenticated-orcid":false,"given":"M.","family":"Martina","sequence":"additional","affiliation":[]}],"member":"3145","published-online":{"date-parts":[[2016,2,10]]},"reference":[{"key":"ref1","unstructured":"Autorit\u00e0 di Bacino del fiume Po: Piano stralcio per l'Assetto\nIdrogeologico (PAI) \u2013 7.II, Allegato\u00a03 \u2013 Metodo di delimitazione delle\nfasce fluviali, Parma, 1999."},{"key":"ref2","unstructured":"Bing Maps Team: Over 100 New Streetside and 3D Cities Go Live on Bing Maps\n(Blog post), available at:\nhttp:\/\/blogs.bing.com\/maps\/2014\/08\/20\/over-100-new-streetside-and-3d-cities-go-live-on-bing-maps\/,\nlast access: 18\u00a0March\u00a02015, 2014."},{"key":"ref3","unstructured":"Bossard, M., Feranec, J., and Otahel, J.: CORINE land cover technical guide\n\u2013 Addendum\u00a02000, European Environment Agency, Copenhagen, 2000."},{"key":"ref4","doi-asserted-by":"crossref","unstructured":"Chen, K., McAneney, J., Blong, R., Leigh, R., Hunter, L., and Magill, C.:\nDefining area at risk and its effect in catastrophe loss estimation:\na\u00a0dasymetric mapping approach, Appl. 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