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development_of_indicators_of_social_vulnerability [2016/11/08 15:26]
Miguel Toquica [Integrated Risk Index]
development_of_indicators_of_social_vulnerability [2016/11/10 15:31] (current)
Miguel Toquica
Line 20: Line 20:
 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.\\
 \\ \\
-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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  • Last modified: 2016/11/08 15:26
  • by Miguel Toquica