INDICES OF SOCIAL VULNERABILITY TO HAZARDS: MODEL UNCERTAINTY AND SENSITIVITY

Typical social vulnerability index development involves the selection of indicators, collection of associated demographic data, normalization of indicators to a common scale, and summation to a final value. As with any model, changes in input data and algorithms have the potential to significantly influence the output. Although there is broad interest in the need to quantitatively model social vulnerability, there is far less consensus regarding the ideal set of methods to be used for the production of indices. Global uncertainty and sensitivity analyses are useful tools for assessing the sensitivity model outputs to variations input data and methods. Uncertainty analysis measures the overall variation in model output due to variations in input methods, while sensitivity analysis quantifies the proportional contribution of each model factor to the total variation. Applied to index construction, the analyses help examine options and tradeoffs associated with modeling decisions such as indicator selection, data transformation, rescaling, weighting, and aggregation. Uncertainty is introduced into the modeling process whenever the index developer chooses between potential options. Unfortunately, the degree to which these choices affect patterns of modeled social vulnerability is not known. The objective of this research is to perform a detailed examination of the methodological approaches used in the construction of indices of social vulnerability to hazards. Specifically, the goal is to assess the degree to which the propagation of uncertainty through the index construction process influences the statistical and spatial distributions of modeled social vulnerability.