EMCA Seismic exposure model for Turkmenistan

DOI

Multi-resolution exposure model for seismic risk assessment in Turkmenistan. The model has been developed according to the methodology outlined in Pittore, Haas and Silva (2019) "Multi-resolution Probabilistic Modelling of Residential Exposure and Vulnerability for Seismic Risk Applications", Earthquake Spectra. The model is aggregated over a Central Voronoidal Tessellation (CVT) composed of geo-cells covering the territory of Turkmenistan (provided as a separate file). The model prior is based on user-elicited knowledge.

The following specific modelling parameters have been employed: Two exposure models are provided, with prior strength pw 10 and 100. Both models have epsilon=0.001 (see publication indicated in the metadata for details on the modelling process) For each geo-cell the model includes the expected number of buildings , total occupancy and replacement cost for each of the 15 building types defined in the EMCA taxonomy (see Pittore et al, 2019b), plus the buildings that are belonging to other, non specified typologies (described by building type OTH). Each geo-cell also includes the area of the geo-cell itself in squared km.

The data package contains three components:
1) exposure models in .csv
2) exposure models in .xml - the file is encoded in NRML 0.5 format and is compatible with the GEM openquake processing engine
3) shapefile of the tessellation that aggregates the exposure model. The field "cell_id" is the linkage with the exposure models

Identifier
DOI https://doi.org/10.5880/GFZ.2.6.2019.004
Related Identifier https://doi.org/10.1177/8755293020951582
Related Identifier https://doi.org/10.5880/GFZ.2.6.2019.006
Metadata Access http://doidb.wdc-terra.org/oaip/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=oai:doidb.wdc-terra.org:7116
Provenance
Creator Pittore, Massimiliano ORCID logo; Wieland, Marc (ORCID: 0000-0002-1155-723X); Haas, Michael; Silva, Vitor
Publisher GFZ Data Services
Contributor Pittore, Massimiliano; Wieland, Marc; Haas, Michael; Silva, Vitor; Pittore Massimiliano
Publication Year 2019
Rights CC BY 4.0; http://creativecommons.org/licenses/by/4.0/
OpenAccess true
Contact Pittore, Massimiliano (GFZ German Research Centre for Geosciences, Potsdam, Germany); Pittore Massimiliano (GFZ German Research Centre for Geosciences, Potsdam, Germany)
Representation
Resource Type Dataset
Version 1.0
Discipline Geosciences
Spatial Coverage (53.036W, 35.046S, 66.901E, 42.632N); Time span of the overall RRVS surveys