The composite index, presented below, is a manipulation of individual variables to produce an aggregate measure of resilience in the face of climate extremes. The combination of multiple indicators encompassing all four of the resilience environments – social, built, natural and economic – presents a holistic overview of a community’s resilience level. The composite resilience index is a number ranging from 0 to 100, which signifies the resilience of a population. All raw data variables are converted into comparable scales utilizing percentages, per capita and density functions to ensure standardization.
Where C represents the indicator category for the Social (S), Built (B), Natural (N) and Economic (E) environments, i represents the indicator and J represents the total number of indicators within the respective category C. Wi represents the weighting factor utilised for each indicator.
For example, for the case study application of the Greater Brisbane region, which was presented at the Floodplain Management Association National Conference in 2015, the indicators utilized are listed below and the expanded variation of the index becomes the following (with scaling factors shown):
Legend:
S = Social Environment Indicator B = Built Environment Indicator N = Natural Environment Indicator E = Economic Environment Indicator W = Weighting Factor Social Environment: Social resilience allows individuals and communities to adapt to extreme circumstances and lessen their impact through mobility, individual-individual and individual - community interactions. Built Environment: The built environment refers to human made space. Resilience in the built environment is enhanced through the provision of emergency services, essential infrastructure and access and evacuation potential. Natural Environment: The natural environment encompasses flora and fauna (including humans) and their interaction with the natural landscape. The geographical location and natural features of a site has a significant impact on the vulnerability of a location. Economic Environment: The economic environment of a community has a significant impact on its resilience. Herein, the economic environment is considered to include factors such as employment, income, productivity, wealth and inequality. |
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Principles
Within the literature, it is evident that any resilience model needs to be built on the foundation of a number of philosophies. In considering this, this resilience model is built on the following key principles:
Simplicity: The ability to reduce the complexity of human society and natural environments presents a major strength in any model to allow ease of measurement. We have attempted to identify the most important relationships between local indicators and community resilience.
Adaptive: It is intended that this model undergoes regular assessment and amendment as the resilience of a community will likely change over time.
Not Stand Alone: This model should be used in conjunction with a range of models and frameworks to assist with building community resilience.
Future Orientated: Where available, the uses of predicate assumptions are important to ensure the model does not remain based on historical assumptions.
Indicator selection has been based on two primary considerations: 1) justification based on the extant literature as to its relevance to resilience; and 2) the availability of regular quality data from nationwide data sources.
Simplicity: The ability to reduce the complexity of human society and natural environments presents a major strength in any model to allow ease of measurement. We have attempted to identify the most important relationships between local indicators and community resilience.
Adaptive: It is intended that this model undergoes regular assessment and amendment as the resilience of a community will likely change over time.
Not Stand Alone: This model should be used in conjunction with a range of models and frameworks to assist with building community resilience.
Future Orientated: Where available, the uses of predicate assumptions are important to ensure the model does not remain based on historical assumptions.
Indicator selection has been based on two primary considerations: 1) justification based on the extant literature as to its relevance to resilience; and 2) the availability of regular quality data from nationwide data sources.
- Case study application of the disaster resilience index to the region of Brisbane in Queensland Australia.
-How the disaster resilience index may be visualized via mapping. |
-Case study application of the disaster resilience index to the Greater Amsterdam region.
-Study conducted in conjunction with Waternet, Royal HaskoningDHV and TU Delft University in the Netherlands. |
-How our disaster resilience index may be used to assist social, economic, technological and political strategies aimed at enhancing resilience.
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