An analysis of methods for identifying social vulnerability to climate change and sea level rise: a case study of Hampton Roads, Virginia

This paper examines methods for quantitatively identifying communities that have high social vulnerability to environmental hazards. We first provide an overview of the existing literature on quantifying social vulnerability. We discuss the strengths and weaknesses of the one of the most common methods, composite indexing using principal component analysis (PCA), in more detail. We present a case study of Hampton Roads, Virginia, that compares PCA-based composite indexing to an alternative method using k-means clustering to identify socially vulnerable communities. We find that PCA-based indexing is particularly sensitive to changes in geographic boundaries and the number of input variables while clustering is less so. However, both methods exhibit the highest levels of consistency when vulnerability is measured by a relatively small number of well-established quantitative indicators. While the cluster method is more intuitive and easier to interpret than composite indexing, it is not well suited for large analyses across a disparate geographic area or where one needs to explicitly rank observations.