6. Calculating and classifying the indices
Calculating the index of social vulnerability to flooding from standardised indicators involved the following five steps (summarised Figure 4):
- Indicators were grouped into 14 domains pertaining to themes such as health, income, social networks or housing characteristics (see Table 2).
- The standardised indicators (z-scores) were equally weighted within domains. This was done to avoid over-representing the domains with a larger number of indicators. For example, if there were 3 indicators within a domain, each of them had a weighting of 0.33; if there were 2 indicators, each of them had a weighting of 0.5. The weightings of the indicators are included in Appendix 1.
- The weighted indicators were added together to develop the dimensions of sensitivity, exposure, and ability to prepare, respond and recover.
- The dimensions of sensitivity, exposure, and ability to prepare, respond and recover were standardised and summed to form the vulnerability index.
- The vulnerability index was then standardised.
The index of disadvantage was then calculated by adding the standardised vulnerability index to the standardised hazard exposure index (Figure 4). The final step was the standardisation of the disadvantage index.
Figure 4. A schematic diagram showing the process of developing the indices of social vulnerability to flooding and flood disadvantage
This approach to assessing social vulnerability to flooding is based on equal weightings of the domains. This was discussed with the Steering Group and the case study local authorities. It has been recognised, based on the consultations with stakeholders within the ClimateJust project that it is next to impossible to achieve a consensus on the weighting of domains when involving stakeholders from different sectors or various local authority departments.
Therefore, in addition to the indices developed within this project, the users of the flood disadvantage dataset are encouraged to develop their own weightings if replicating the method. In the future, we recommend development of an online tool which would allow the users to freely select the indicators they see as suitable to their vulnerability assessment and enter the weights that are relevant to their locality or area of work.
The final outputs of the assessment of social vulnerability to flooding and flood disadvantage are maps, developed for Scotland as a whole as well as for the individual local authorities. The standardised indices of social vulnerability to flooding and flood disadvantage are classified using standard deviation (SD) value, which, for standardised indices, equals 1. The average values of social vulnerability to flooding and flood disadvantage are understood as those that concentrate within -/+0.5 SD from the mean (average) value. The larger the positive or negative value of the index, the further the values are from the mean value. The classes are summarised in Table 7.
Table 7. Classes of social vulnerability to flooding and flood disadvantage
|Value of the standardised index||Level of vulnerability / disadvantage|
|1.5 - 2.5||Extremely high|
|0.5 - 1.5||Relatively high|
|-0.5 - 0.5||Average|
|-1.5 - -0.5||Relatively low|
|-2.5 - -1.5||Extremely low|
The method of classification based on SD was chosen in order to allow comparisons to the 'average' Scottish neighbourhood. The purpose of the assessment carried out for the entire country was to enable identification of the extreme and acute levels of vulnerability and disadvantage as locations where further investigation should be targeted, followed by action. The classification based on SD was also used in the NFRA (SEPA, 2011) to identify different levels of social vulnerability.
Whilst the classification based on SD allows identification of the areas with values far removed from the Scottish mean, this method may be less useful for users in local authorities, in particular in locations with little variation in vulnerability and disadvantage (e.g. where most of the neighbourhoods are close to average, or only range from relatively low to relatively high). The datasets containing the indicators (the simple spreadsheet and the spatial dataset) allow the end users in local authorities to apply their own classification. For example they could base them on quantiles, natural breaks or equal intervals to classify the data in a manner that would allow the identification of differences between data zones in terms of their vulnerability or disadvantage at the local level. In addition, the average values are provided in the simple spreadsheet with the data, not only for Scotland but also for each individual local authority.
Email: Carol Brown