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development_of_indicators_of_social_vulnerability [2016/10/05 16:14]
Miguel Toquica
development_of_indicators_of_social_vulnerability [2016/11/10 15:26]
Miguel Toquica
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 \\ \\
 Table 1 Table 1
- +^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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 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.\\
 \\ \\
-Table 2\\+Table 2
  
-{{ :​social_vulnerability:​sara_variable_sample.png ​|}} +Index of social Vulnerability  
 +^Theme^Sub-theme^Description^SARA variables^ 
 +|Population|Vulnerable population|Describes populations that are at risk or have needs distinct from the majority of the population|Female population|  
 +| | | |Population living in overcrowded dwellings|  
 +| | | |Native indigeneous population|  
 +| | | |Population with no birth certificate| 
 +| | | |Age dependance|  
 +| |Population structure|Categorizes the strcuture of the countries'​ population|Total population| 
 +| | | |Population density (inhabitants/​km2)|  
 +| | | |Number of households| 
 +| | | |Number of people per household| 
 +|Economy|Labour market|Describes the labor demographics of a country|Population non economically active|  
 +| | | |Population employeed in the manufacturing industry (15-64)|  
 +| | | |Population employed in the hotels/​restaurant sector|  
 +| | | |Population employeed in the commercial industry (15-64)|  
 +| | | |Population unemployed| 
 +| | | |Population looking for employment| 
 +| | | |Dependency rate| 
 +| | | |Economically active population (EAP)|  
 +| |Income distribution and poverty|Describes the distribution of wealth and the incidence of poverty in a country|Population with unsatisfied basic needs| 
 +| | | |Household with less than US$100 monthly income|  
 +| | | |Household with US$100 - 200 monthly income | 
 +| | | |GINI index| 
 +| | | |Total population in poverty|  
 +|Infrastructure|Transport and communication|Describes the communication services and transport capacities of a nation|Mobile cellular subscriptions|  
 +| | | |Population with no computer access|  
 +| | | |Mobile cellular subscriptions|  
 +| | | |Household with computer and internet|  
 +| |Energy, water and sanitation|Describes the access to energy and water resources and availability and status of sanitation for a country’s population|Households with access to improved water source|  
 +| | | |No natural gas public distribution| 
 +| | | |Households with no electric energy access|  
 +| | | |No sewage system|  
 +| | | |Household with no bathroom|  
 +| | | |Household with poor provision of public services|  
 +|Education|Education outcome|Describes the results of education and measures how successful the students and the educational system are|Illiteracy rate| 
 +| | | |Education level completed primary| 
 +| | | |Education level secondary | 
 +| | | |Education level completed (superior, technical, university) | 
 +| | | |Population with no formal education | 
 +| |Education access|Categorizes as the accessibility of a country’s population to education|Children (Age 7-11) not enrrolled in schoool|  
 +| | | |Population enrolled in education institution | 
 +|Health|Healthcare resources|Describes the healthcare resources and accessibility by the population of those resources for maintaining and improving health|Hospital , clinics per 1000 population| 
 +| | | |Population going to private health centers| 
 +| | | |Population going to public health centers| 
 +| | | |Distance to the nearest healtcare center|  
 +| |Healthcare status|Categorizes the current health condition of a country’s population|Population with no healthcare|  
 +| | | |Population registered to national healthcare|  
 +| | | |Population with private healthcare insurance|  
 +| | | |Economically active population (EAP) without health insurance|
 \\ \\
  
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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.\\
- +\\ 
-  * [[Argentina|SVIR_Argentina:]] Social vulnerability and integrated risk index for Argentina+{{:​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   * [[Bolivia|SVIR Bolivia:]] Social vulnerability and integrated risk index for Bolivia
   * [[Chile|SVIR Chile:]] Social vulnerability and integrated risk index for Chile   * [[Chile|SVIR Chile:]] Social vulnerability and integrated risk index for Chile
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   * [[Ecuador|SVIR Ecuador:]] Social vulnerability and integrated risk index for Ecuador   * [[Ecuador|SVIR Ecuador:]] Social vulnerability and integrated risk index for Ecuador
   * [[Peru|SVIR Peru:]] Social vulnerability and integrated risk index for Peru   * [[Peru|SVIR Peru:]] Social vulnerability and integrated risk index for Peru
-  * [[Venezuela|SVIR Venezuela:​]] Social vulnerability and integrated risk index for Venezuela +  * [[Venezuela|SVIR Venezuela:​]] Social vulnerability and integrated risk index for Venezuela.\\ 
-  [[start| Back to the SARA Project main page]]=== References ====+   
 +  ​ 
 +  [[start| Back to the SARA Project main page]]\\ 
 +  
 + 
 + 
 +=== References === 
  
   * 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\\   * 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\\
  • development_of_indicators_of_social_vulnerability.txt
  • Last modified: 2016/11/10 15:31
  • by Miguel Toquica