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  • 1
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    Customised focus maps and resultant CVT-based aggregation entities for Lima and Callao (Peru) (2021)
    GFZ Data Services
    Details
    Publication Date: 2021-10-07
    Description: Abstract
    Description: This data publication is composed by two main folders: (1) “Focus_map_construction” and (2) “CVT_models”. The first one contains the individual raster inputs (tsunami inundation and population distribution) that are combined to construct two different focus maps for the cities of Lima and Callao (Peru). The reader can find a more complete description about the focus map concept in Pittore (2015). These raster focus maps are used as inputs to generate variable-resolution CVT (Central Voronoi Tessellation) geocells following the method presented in Pittore et al., (2020). They are vector-based data (ESRI shapefiles) that are stored in the second folder. These resultant CVT-geocells are used by Gomez-Zapata et al., (2021) as spatial aggregation boundaries to represent the residential building portfolio for the cities of Lima and Callao (Peru).
    Keywords: spatial aggregation areas ; CVT ; Central Voronoi Tessalations ; focus map ; geocells ; raster ; RIESGOS ; Scenario-based multi-risk assessment in the Andes region ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 NATURAL HAZARDS 〉 EARTHQUAKES ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 NATURAL HAZARDS 〉 TSUNAMIS
    Type: Dataset , Dataset
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    DATA GFZ
  • 2
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    Scripts to generate (1) attribute-based fuzzy scores for SARA and HAZUS building classes, and (2) probabilistic inter-scheme compatibility matrices. An application on the residential building stock of Valparaiso (Chile) for seismic risk applications (2021)
    GFZ Data Services
    Details
    Publication Date: 2021-10-26
    Description: Abstract
    Description: This repository is composed of two main folders: (1) “Exposure_fuzzy_scores” and (2) “Inter-scheme_mapping”. The first one contains an ipython notebook with a complete description of two earthquake building schemes: SARA and HAZUS in terms of faceted attributes contained in the GEM V.2.0 taxonomy. Both schemes have already been proposed for exposure modelling at the third administrative division “commune” in Chile in earlier works. They are inputs for the use of a Python script (contained in the second folder) to calculate an inter-scheme compatibility matrix, that uses SARA as the source and HAZUS as the target schemes. These models and data are supplement material to Gomez-Zapata et al. (2021).
    Description: Other
    Description: Licence Statement: Data: Creative Commons Attribution 4.0 International License (CC BY 4.0) Code: Apache License, Version 2.0 (January 2004) Copyright © 2021 Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences, Potsdam, Germany Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at https://www.apache.org/licenses/LICENSE-2.0. Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
    Keywords: exposure modelling ; building schemes ; compatibility matrix ; faceted taxonomy ; RIESGOS ; Scenario-based multi-risk assessment in the Andes region
    Type: Software , Software
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    DATA GFZ
  • 3
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    EMCA Seismic exposure model for Tajikistan (2019)
    GFZ Data Services
    Details
    Publication Date: 2021-11-17
    Description: Abstract
    Description: Multi-resolution exposure model for seismic risk assessment in Tajikistan. The model has been developed according to the methodology outlined in Pittore, Haas and Silva (2020) "Multi-resolution Probabilistic Modelling of Residential Exposure and Vulnerability for Seismic Risk Applications", Earthquake Spectra (submitted). The model is aggregated over a Central Voronoidal Tessellation (CVT) composed of geo-cells covering the territory of Tajikistan (provided as a separate file). The model integrates around 1'000 building observations (see related dataset Pittore et al. 2019a). The following specific modelling parameters have been employed: Prior strength=10, 100 Epsilon=0.001 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
    Keywords: Earthquake Risk ; taxonomy ; RRVS ; GEM ; EMCA ; Central Asia ; 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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    DATA GFZ
  • 4
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    EMCA Seismic exposure model for the Kyrgyz Republic (2019)
    GFZ Data Services
    Details
    Publication Date: 2021-11-17
    Description: Abstract
    Description: Multi-resolution exposure model for seismic risk assessment in the Kyrgyz Republic. 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 1'175 geo-cells covering the territory of the Kyrgyz Republic. The model integrates around 6'000 building observations (see related dataset Pittore et al. 2019). 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
    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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    DATA GFZ
  • 5
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    EMCA Seismic exposure model for Uzbekistan (2019)
    GFZ Data Services
    Details
    Publication Date: 2021-11-17
    Description: Abstract
    Description: Multi-resolution exposure model for seismic risk assessment in Uzbekistan. 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 Uzbekistan (provided as a separate file). The model prior is based on empirical observations in Kyrgyzstan and Tajikistan as well as 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
    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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    DATA GFZ
  • 6
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    EMCA Seismic exposure model for Kazakhstan (2019)
    GFZ Data Services
    Details
    Publication Date: 2021-11-17
    Description: Abstract
    Description: Multi-resolution exposure model for seismic risk assessment in Kazakhstan. 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 Kazakhstan (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
    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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    DATA GFZ
  • 7
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    EMCA Seismic exposure model for Turkmenistan (2019)
    GFZ Data Services
    Details
    Publication Date: 2021-11-17
    Description: Abstract
    Description: 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
    Keywords: Earthquake Risk ; taxonomy ; RRVS ; GEM ; EMCA ; Central Asia ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 NATURAL HAZARDS 〉 EARTHQUAKES
    Type: Dataset , Dataset
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    DATA GFZ
  • 8
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    Earthquake Model Central Asia: seismic exposure modelling (2019)
    GFZ Data Services
    Details
    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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    DATA GFZ
  • 9
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    Remote Rapid Visual survey (RRVS) for exposure modelling in Kyrgyzstan and Tajikistan (2019)
    GFZ Data Services
    Details
    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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    DATA GFZ
  • 10
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    Spatial representation of direct loss estimates on the residential building stock of Lima (Peru) from decoupled earthquake and tsunami scenarios on variable resolutions exposure models (2021)
    GFZ Data Services
    Details
    Publication Date: 2022-02-15
    Description: Abstract
    Description: This data repository contains the spatial distribution of the direct financial loss computed expected for the residential building stock of Metropolitan Lima (Peru) after the occurrence of six decoupled earthquake and tsunami risk scenarios (Gomez-Zapata et al., 2021a; Harig and Rakowsky, 2021). These risk scenarios were independently calculated making use of the DEUS (Damage Exposure Update Service) available in https://github.com/gfzriesgos/deus. The reader can find documentation about this programme in (Brinckmann et al, 2021) where the input files required by DEUS and outputs are comprehensively described. Besides the spatially distributed hazard intensity measures (IM), other inputs required by DEUS to computed the decoupled risk loss estimates comprise: spatially aggregated building exposure models classified in every hazard-dependent scheme. Each class must be accompanied by their respective fragility functions, and financial consequence model (with loss ratios per involved damage state). The collection of inputs is presented in Gomez-Zapata et al. (2021b). The risk estimates are computed for each spatial aggregation areas of the exposure model. For such a purpose, the initial damage state of the buildings is upgraded from undamaged (D0) to any progressive damage state permissible by the fragility functions. The resultant outputs are spatially explicit .JSON files that use the same spatial aggregation boundaries of the initial building exposure models. An aggregated direct financial loss estimate is reported for each cell after every hazard scenario. It is reported one seismic risk loss distribution outcome for each of the 2000 seismic ground motion fields (GMF) per earthquake magnitude (Gomez-Zapata et al., 2021a). Therefore, 1000 seismic risk estimates from uncorrelated GMF are stored in “Clip_Mwi_uncorrelated” and 1000 seismic risk estimates from spatially cross-correlated GMF (using the model proposed by Markhvida et al. (2018)) are stored in “Clip_ Mwi_correlated”. It is worth noting that the prefix “clip” of these folders refers to the fact that, all of the seismic risk estimates were clipped with respect to the geocells were direct tsunami risk losses were obtained. This spatial compatibility in the losses obtained for similar areas and Mw allowed the construction of the boxplots that are presented in Figure 16 in Gomez-Zapata et al., (2021). The reader should note that folder “All_exposure_models_Clip_8.8_uncorrelated_and_correlated” also contains another folder entitled “SARA_entire_Lima_Mw8.8” where the two realisations (with and without correlation model) selected to produce Figure 10 in Gomez-Zapata et al., (2021) are stored. Moreover, the data to produce Figure 9 (boxplots comparing the variability in the seismic risk loss estimates for this specific Mw 8.8, are presented in the following .CSV file: “Lima_Mw_8.8_direct_finantial_loss_distributions_all_spatial_aggregations_Corr_and_NoCorr.csv”. Naturally, 1000 values emulating the 1000 realisations are the values that compose the variability expressed in that figure. Since that is a preliminary study (preprint version), the reader is invited to track the latest version of the actually published (if so) journal paper and check the actual the definitive numeration of the aforementioned figures.
    Keywords: tsunami risk ; earthquake risk ; risk scenario ; physical vulnerability ; loss ; deterministic risk ; fragility function ; RIESGOS ; Scenario-based multi-risk assessment in the Andes region ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 NATURAL HAZARDS 〉 EARTHQUAKES ; EARTH SCIENCE 〉 HUMAN DIMENSIONS 〉 NATURAL HAZARDS 〉 TSUNAMIS ; EARTH SCIENCE SERVICES 〉 HAZARDS MANAGEMENT
    Type: Dataset , Dataset
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    DATA GFZ
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