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development_of_indicators_of_social_vulnerability [2016/10/05 15:39]
Miguel Toquica
development_of_indicators_of_social_vulnerability [2016/10/06 09:35]
Miguel Toquica
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 In the second period, data was collected from the most recent census available for a given country and classified according to the indicators taxonomy suggested in Power et al. (2013). Also, an appraisal of national censuses in South America, at various levels of geography, to understand data availability and scalability in order to guide the collection of data was performed. The following table is an example of the themes, subthemes and main variables used in the SARA project. For a complete set of variables per country please refer to the country tabs below.\\ In the second period, data was collected from the most recent census available for a given country and classified according to the indicators taxonomy suggested in Power et al. (2013). Also, an appraisal of national censuses in South America, at various levels of geography, to understand data availability and scalability in order to guide the collection of data was performed. The following table is an example of the themes, subthemes and main variables used in the SARA project. For a complete set of variables per country please refer to the country tabs below.\\
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 Table 2\\ Table 2\\
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 {{ :​social_vulnerability:​sara_variable_sample.png |}}  {{ :​social_vulnerability:​sara_variable_sample.png |}} 
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-=== Social Vulnerability Index: Weighting and Aggregation ===+==== Social Vulnerability Index: Weighting and Aggregation ​====
 Central to the construction of composite indicators is the need to meaningfully combine different data dimensions in a manner in which consideration is given to weighting and aggregation procedures. The method of aggregation that we employed is the summation of equally weighted sub-index scores. Here, variable scores in each sub-index (e.g. population, economy, etc.) were averaged to reduce the influence of the varying number of variables in each sub-index. Each sub-component was then summed to derive a final composite score. Since there are between three and five sub-components,​ depending on the data available for each country, the summed score of the composite indicator ranges between 0 and 5 (0 being the least socially vulnerable and 5 being the most). As a subsequent step, the composite social vulnerability scores were MIN-MAX rescaled to produce a final composite score between zero and one (0 being the least socially vulnerable and 1 being the most vulnerable). ​ Central to the construction of composite indicators is the need to meaningfully combine different data dimensions in a manner in which consideration is given to weighting and aggregation procedures. The method of aggregation that we employed is the summation of equally weighted sub-index scores. Here, variable scores in each sub-index (e.g. population, economy, etc.) were averaged to reduce the influence of the varying number of variables in each sub-index. Each sub-component was then summed to derive a final composite score. Since there are between three and five sub-components,​ depending on the data available for each country, the summed score of the composite indicator ranges between 0 and 5 (0 being the least socially vulnerable and 5 being the most). As a subsequent step, the composite social vulnerability scores were MIN-MAX rescaled to produce a final composite score between zero and one (0 being the least socially vulnerable and 1 being the most vulnerable). ​
  
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-=== Integrated Risk Index === +==== Integrated Risk Index ====
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-====== Integrated Risk Index per SARA countries ======+
  
 The evaluation of integrated risk is composed of the modeling of losses for each county (or first order impacts) and the models of social vulnerability (described in section above) that can be described as a potential aggravation coefficient of the first order impacts. Here, a physical risk index utilizes the probabilistic seismic hazard, the exposure, and vulnerability models; it combines them using the OpenQuake-engine to calculate numerous risk metrics: average annual losses, loss exceedance curves, risk maps for different return periods and loss curves at various spatial resolutions (Yepes et al. 2017). This methodology is explained in detail in...  Modelling the Residential Building Inventory in South America for Seismic Risk Assessment (Yepes et al. 2015), and the probabilistic seismic risk assessment of the residential building stock in South America (Yepes et al. 2016). The evaluation of integrated risk is composed of the modeling of losses for each county (or first order impacts) and the models of social vulnerability (described in section above) that can be described as a potential aggravation coefficient of the first order impacts. Here, a physical risk index utilizes the probabilistic seismic hazard, the exposure, and vulnerability models; it combines them using the OpenQuake-engine to calculate numerous risk metrics: average annual losses, loss exceedance curves, risk maps for different return periods and loss curves at various spatial resolutions (Yepes et al. 2017). This methodology is explained in detail in...  Modelling the Residential Building Inventory in South America for Seismic Risk Assessment (Yepes et al. 2015), and the probabilistic seismic risk assessment of the residential building stock in South America (Yepes et al. 2016).
 To derive an estimate of integrated risk, a total risk index was constructed via the mathematical combination of the social vulnerability index with the estimates of average annual loss. To accomplish the latter, the average annual loss values rescaled using the MIN-MAX method. Carreño et al. (2007; 2012) provide the aggregation method due to its simplicity, its successful use-case applications within literature (see Carreño et al. 2007; 2012; Fernandez et al. 2007; Khazai et al. 2008; Khazai and Bendimerad, 2011), and it’s method of treating the social vulnerability as an aggravation coefficient to the physical risk estimate. Here, the direct potential impact of an earthquake is denoted as Rt=Rf(1+F) where  Rt is a total earthquake risk index, Rf is a physical risk index, which in this case is the average annual loss estimates for each country, and F is a social fragility index that was modeled here using social vulnerability. The aggregation scheme is a method to derive a total risk index or the potential impact of an earthquake that is obtained from the compounding of the physical risk index by an impact factor based on the socioeconomic characteristics within the country’s social systems (e.g. social vulnerability index). The integrated risk indices are outlined in the section below.\\ To derive an estimate of integrated risk, a total risk index was constructed via the mathematical combination of the social vulnerability index with the estimates of average annual loss. To accomplish the latter, the average annual loss values rescaled using the MIN-MAX method. Carreño et al. (2007; 2012) provide the aggregation method due to its simplicity, its successful use-case applications within literature (see Carreño et al. 2007; 2012; Fernandez et al. 2007; Khazai et al. 2008; Khazai and Bendimerad, 2011), and it’s method of treating the social vulnerability as an aggravation coefficient to the physical risk estimate. Here, the direct potential impact of an earthquake is denoted as Rt=Rf(1+F) where  Rt is a total earthquake risk index, Rf is a physical risk index, which in this case is the average annual loss estimates for each country, and F is a social fragility index that was modeled here using social vulnerability. The aggregation scheme is a method to derive a total risk index or the potential impact of an earthquake that is obtained from the compounding of the physical risk index by an impact factor based on the socioeconomic characteristics within the country’s social systems (e.g. social vulnerability index). The integrated risk indices are outlined in the section below.\\
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 +{{:​social_vulnerability:​sara_integrated_risk_full.jpg?​100 |}}
 +  * [[Argentina|SVIR Argentina:​]] Social vulnerability and integrated risk index for Argentina
 +  * [[Bolivia|SVIR Bolivia:]] Social vulnerability and integrated risk index for Bolivia
 +  * [[Chile|SVIR Chile:]] Social vulnerability and integrated risk index for Chile
 +  * [[Colombia|SVIR Colombia:]] Social vulnerability and integrated risk index for Colombia
 +  * [[Ecuador|SVIR Ecuador:]] Social vulnerability and integrated risk index for Ecuador
 +  * [[Peru|SVIR Peru:]] Social vulnerability and integrated risk index for Peru
 +  * [[Venezuela|SVIR Venezuela:​]] Social vulnerability and integrated risk index for Venezuela.\\
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 +  [[start| Back to the SARA Project main page]]\\
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-  * [[Argentina|Research Topic 1 (RT1):]] Assessing the Current State of Practice in Seismic Hazard Analysis in South America (SA) 
-  * [[Bolivia|Research Topic 2 (RT2):]] Building a harmonised database of ‘hazardous’ crustal faults 
-  * [[Chile|Research Topic 3 (RT3):]] Modelling Subduction Zones in South America 
-  * [[Colombia|Research Topic 4 (RT4):]] The South American Earthquake Catalogue 
-  * [[Ecuador|Research Topic 6 (RT6):]] A South American Strong Motion Database and Selection of ground motion prediction equations (GMPEs) for seismic hazard analysis in South America 
-  * [[Peru|Research Topic 7 (RT7):]] Creation of a new PSHA input model for South America and calculation of results 
-  * [[Venezuela|Research Topic 7 (RT7):]] Creation of a new PSHA input model for South America and calculation of results 
-  [[start| Back to the SARA Project main page]]=== References ==== 
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-Burton, C., Silva, V. (2014) “Integrated risk modeling within the Global Earthquake Model (GEM): test case application for Portugal” Second Conference on Earthquake Engineering and Seismology. Istanbul Aug. 25-29 2014 
  
-Carreño ML, Cardona OD, Barbat AH (2007) “Urban seismic risk evaluation: a holistic approach”. Natural Hazards 40(1): 132–137.+=== References ===
  
-Power, C., Daniell, J., Khazai, B., Burton, C., Oberacker, C. (2013) “National level Socio-Economic Vulnerability Database – Data Collection, Harmonisation and Analysis” Socio Economic Vulnerability and Integrated Risk Project. CEDIM. Global Earthquake Model 
  
 +  * Burton, C., Silva, V. (2014) “Integrated risk modeling within the Global Earthquake Model (GEM): test case application for Portugal” Second Conference on Earthquake Engineering and Seismology. Istanbul Aug. 25-29 2014\\
 +  * Carreño ML, Cardona OD, Barbat AH (2007) “Urban seismic risk evaluation: a holistic approach”. Natural Hazards 40(1): 132–137.\\
 +  * Power, C., Daniell, J., Khazai, B., Burton, C., Oberacker, C. (2013) “National level Socio-Economic Vulnerability Database – Data Collection, Harmonisation and Analysis” Socio Economic Vulnerability and Integrated Risk Project. CEDIM. Global Earthquake Model
  
   * Audemard, F.A., Machette, M., Cox, J., Dart, R., Haller, K. (2000). Map and Database of Quaternary faults in Venezuela, U.S. Geological Survey Open-File Report 00-108. [[http://​pubs.usgs.gov/​of/​2000/​ofr-00-0018/​|US Open-File Report]]   * Audemard, F.A., Machette, M., Cox, J., Dart, R., Haller, K. (2000). Map and Database of Quaternary faults in Venezuela, U.S. Geological Survey Open-File Report 00-108. [[http://​pubs.usgs.gov/​of/​2000/​ofr-00-0018/​|US Open-File Report]]
  • development_of_indicators_of_social_vulnerability.txt
  • Last modified: 2016/11/10 15:31
  • by Miguel Toquica