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  • 1
    Publication Date: 2016-01-01
    Electronic ISSN: 2308-1708
    Topics: Geography
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  • 2
    Publication Date: 2021-11-17
    Description: Abstract
    Description: The datasets in this collection include input and output components of the seismic exposure model developed within the framework of the Earthquake Model Central Asia and used for seismic risk assessment. In particular the collection includes: - A dataset of around 7’000 individual building observations in Kyrgyzstan and Tajikistan collected using the Remote Rapid Visual Survey (RRVS) methodology developed at GFZ, along with the class schema used to map the individual taxonomic observations into vulnerability-related building classes. These are used to develop suitable prior distribution and to constrain locally the resulting exposure models - The seismic exposure models for the following central Asian countries: Kazakhstan , Kyrgyz Republic, Tajikistan, Turkmenistan and Uzbekistan, aggregated over a set of heterogeneous tessellations (geo-cells) The methodology employed for the development of the exposure models is described in Pittore, M., Haas, M., and Silva, V. (2020) “Multi-resolution Probabilistic Modelling of Residential Exposure and Vulnerability for Seismic Risk Applications”, Earthquake Spectra. Two versions of the models obtained with two different parameter settings are included. The models are provided in .csv and in .xml (nrml 0.5) format, for compatiliby with the OpenQuake hazard and risk assessment engine.
    Keywords: Earthquake Risk ; taxonomy ; RRVS ; GEM ; EMCA ; Central Asia ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 NATURAL HAZARDS 〉 EARTHQUAKES ; geological process 〉 seismic activity ; risk 〉 natural risk ; safety 〉 risk assessment 〉 disaster preparedness ; safety 〉 risk assessment 〉 natural risk analysis ; safety 〉 risk assessment 〉 risk exposure
    Type: Dataset , Dataset
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  • 3
    Publication Date: 2021-11-17
    Description: Abstract
    Description: The dataset contains a set of structural and non-structural attributes collected using the GFZ RRVS methodology in Kyrgyzstan and Tajikistan, within the framework of the projects EMCA (Earthquake Model Central Asia), funded by GEM, and "Assessing Seismic Risk in the Kyrgyz Republic", funded by the World Bank. The survey has been carried out between 2012 and 2016 using a Remote Rapid Visual Screening system developed by GFZ and employing omnidirectional images and footprints from OpenStreetMap. The attributes are encoded according to the GEM taxonomy v2.0 (see https://taxonomy.openquake.org). The following attributes are defined (not all are observable in the RRVS survey): code, description: lon, longitude in fraction of degrees lat, latitude in fraction of degrees object_id, unique id of the building surveyed MAT_TYPE,Material Type MAT_TECH,Material Technology MAT_PROP,Material Property LLRS,Type of Lateral Load-Resisting System LLRS_DUCT,System Ductility HEIGHT,Height YR_BUILT,Date of Construction or Retrofit OCCUPY,Building Occupancy Class - General OCCUPY_DT,Building Occupancy Class - Detail POSITION,Building Position within a Block PLAN_SHAPE,Shape of the Building Plan STR_IRREG,Regular or Irregular STR_IRREG_DT,Plan Irregularity or Vertical Irregularity STR_IRREG_TYPE,Type of Irregularity NONSTRCEXW,Exterior walls ROOF_SHAPE,Roof Shape ROOFCOVMAT,Roof Covering ROOFSYSMAT,Roof System Material ROOFSYSTYP,Roof System Type ROOF_CONN,Roof Connections FLOOR_MAT,Floor Material FLOOR_TYPE,Floor System Type FLOOR_CONN,Floor Connections For each building an EMCA vulnerability class has been assigned following the fuzzy scoring methodology described in Pittore et al., 2018. The related class definition schema (as a .json document) is included in the data package.
    Keywords: Earthquake Risk ; taxonomy ; RRVS ; GEM ; EMCA ; Central Asia ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 NATURAL HAZARDS 〉 EARTHQUAKES ; geological process 〉 seismic activity ; risk 〉 natural risk ; safety 〉 risk assessment 〉 disaster preparedness ; safety 〉 risk assessment 〉 natural risk analysis ; safety 〉 risk assessment 〉 risk exposure
    Type: Dataset , Dataset
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