Multivariate Analysis in Geography: Some Comments

Scott, J. F.

doi:10.2307/2987784

Cited by 3 publications

(1 citation statement)

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“…Therefore, to appreciate the complex nature of social vulnerability, emergency managers need methods to understand the multivariate characteristics of individuals and communities in tsunami-prone areas. One approach to quantify the multivariate nature of a population is the use of exploratory factor analysis, a data-reduction technique that has been widely used in humangeography research (Clark et al 1974(Clark et al , 1998Mather and Openshaw 1974;Scott 1975). Principal component analysis (PCA), one of the most common multivariate factorial approaches, uncovers the underlying dimensions of a large set of variables and mathematically transforms data into a smaller set of components based on intercorrelated variables.…”

Section: Introductionmentioning

confidence: 99%

Community variations in social vulnerability to Cascadia-related tsunamis in the U.S. Pacific Northwest

2009

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Tsunamis generated by Cascadia subduction zone earthquakes pose significant threats to coastal communities in the U.S. Pacific Northwest. Impacts of future tsunamis to individuals and communities will likely vary due to pre-event socioeconomic and demographic differences. In order to assess social vulnerability to Cascadia tsunamis, we adjust a social vulnerability index based on principal component analysis first developed by Cutter et al. (2003) to operate at the census-block level of geography and focus on community-level comparisons along the Oregon coast. The number of residents from blocks in tsunami-prone areas considered to have higher social vulnerability varies considerably among 26 Oregon cities and most are concentrated in four cities and two unincorporated areas. Variations in the number of residents from census blocks considered to have higher social vulnerability in each city do not strongly correlate with the number of residents or city assets in tsunami-prone areas. Methods presented here will help emergency managers to identify community sub-groups that are more susceptible to loss and to develop riskreduction strategies that are tailored to local conditions.

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Section: Introductionmentioning

confidence: 99%