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development_of_indicators_of_social_vulnerability [2016/11/04 12:27]
Miguel Toquica
development_of_indicators_of_social_vulnerability [2016/11/10 15:31]
Miguel Toquica
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 To develop the social vulnerability indicators, a total of 430 variables at the sub-national level (P3) for Argentina, Bolivia, Chile, Colombia, Ecuador, Perú, and Venezuela were collected, pre-processed,​ categorized,​ and compiled into a database of social vulnerability indicators for each respective country. The data was obtained from the most recently available national censuses on population in each country. Each country database contains approximately 50 to 70 indicators. Variations in the number of indicators and the enumeration units, as well as the data source for each country are outlined in Table 1 below.\\ To develop the social vulnerability indicators, a total of 430 variables at the sub-national level (P3) for Argentina, Bolivia, Chile, Colombia, Ecuador, Perú, and Venezuela were collected, pre-processed,​ categorized,​ and compiled into a database of social vulnerability indicators for each respective country. The data was obtained from the most recently available national censuses on population in each country. Each country database contains approximately 50 to 70 indicators. Variations in the number of indicators and the enumeration units, as well as the data source for each country are outlined in Table 1 below.\\
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-Table 1 +Table 1. SARA countries sub-national administrative organization 
- +^Country^Sub-national division^Subdivision count^Indicators collected^ ​                                                                    ​ 
-^ Country ​   ^ Sub-national division ​         ^ Subdivision count  ^ Indicators collected ​ ^ Data Source ​                                                                      +|Argentina|Departamento,​ Partido, Comuna|527|57|Argentina Instituto Nacional de Estadisticas y Censo  (INDEC) ​ - Censo 2010| 
-| Argentina ​ | Departamento,​ Partido, Comuna ​ | 527                | 57                    | Argentina Instituto Nacional de Estadisticas y Censo  (INDEC) ​ - Censo 2010       ​+|Bolivia|Municipio|341|68|Instituto Nacional de Estadistica (INE)  de Bolivia - Censo 2012| 
-| Bolivia ​   | Municipio ​                     | 341                | 68                    | Instituto Nacional de Estadistica (INE)  de Bolivia - Censo 2012                  +|Chile|Comuna|342|68|Instituto Nacional de Estadistica de Chile (INE) - Censo 2002| 
-| Chile      | Comuna ​                        ​| 342                | 68                    | Instituto Nacional de Estadistica de Chile (INE) - Censo 2002                     ​+|Colombia|Municipio|1114|60|Colombia Departamento Administrativo Nacional de Estadistica (DANE) - Censo 2005| 
-| Colombia ​  ​| Municipio ​                     | 1114               ​| 60                    | Colombia Departamento Administrativo Nacional de Estadistica (DANE) - Censo 2005  +|Ecuador|Parroquia|1024|56|Instituto Nacional de Estadística y Censos (INEC) - Censo 2010| 
-| Ecuador ​   | Parroquia ​                     | 1024               ​| 56                    | Instituto Nacional de Estadística y Censos (INEC) - Censo 2010                    +|Peru|Distritos|1833|65|Instituto Nacional de Estadistica e Informatica (INEI) - Censo 2007| 
-| Peru       ​| Distritos ​                     | 1833               ​| 65                    | Instituto Nacional de Estadistica e Informatica (INEI) - Censo 2007               ​+|Venezuela|Parroquia|1130|47|Instituto Nacional de Estadística (INE), Censo 2011|
-| Venezuela ​ | Parroquia ​                     | 1130               ​| 47                    | Instituto Nacional de Estadística (INE), Censo 2011                               ​|+
  
 Since it is difficult to measure the social vulnerability of populations relatively, variables were collected as proxy measures to represent the concept. Here, a step was taken to ensure the relevance of the data within the domain of social and economic vulnerability research. A literature review exceeding 400 articles was conducted ​ to ensure the relevance of all data that was collected and compiled into databases. It is within this context that we collected variables within the population, economy, infrastructure,​ health, and education dimensions by adhering to the taxonomic classification developed in Risk and resiliency indicators, EMI topical report (Khazai et al. 2011) for the selection of socio-economic indicators typically used in social vulnerability assessments. A hierarchical approach (see Figure 2) was utilized in which variables were collected within components, yet classified into their corresponding sub-components (e.g. Population variables were collected and subclassified into corresponding population structure and vulnerable populations sub-components).\\ Since it is difficult to measure the social vulnerability of populations relatively, variables were collected as proxy measures to represent the concept. Here, a step was taken to ensure the relevance of the data within the domain of social and economic vulnerability research. A literature review exceeding 400 articles was conducted ​ to ensure the relevance of all data that was collected and compiled into databases. It is within this context that we collected variables within the population, economy, infrastructure,​ health, and education dimensions by adhering to the taxonomic classification developed in Risk and resiliency indicators, EMI topical report (Khazai et al. 2011) for the selection of socio-economic indicators typically used in social vulnerability assessments. A hierarchical approach (see Figure 2) was utilized in which variables were collected within components, yet classified into their corresponding sub-components (e.g. Population variables were collected and subclassified into corresponding population structure and vulnerable populations sub-components).\\
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 ==== Integrated Risk Index ==== ==== Integrated Risk Index ====
  
-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 inModelling 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.\\
 \\ \\
  • development_of_indicators_of_social_vulnerability.txt
  • Last modified: 2016/11/10 15:31
  • by Miguel Toquica