| Summary: | As coastal areas increase in populations there is an increasing need to determine what community characteristics are most resilient to coastal disasters. This research proposes two methods to quantify community resilience. The factor analysis method results in a weighted additive index model of six variables to derive community resilience. The index places every community in the Gulf of Mexico on a scale from 0-1. The most resilient counties in the Gulf of Mexico region were found to be Hillsborough, FL, Pinellas, FL, Sarasota, FL, Hernando, FL, Okaloosa, FL, Kenedy, TX, and Jefferson, LA with a resilience score of 1. The least resilient counties in the Gulf of Mexico were found to be Cameron, TX and Willacy, TX with a resilience score of below 0.40. The six key variables used to create the resilience index were expenditures for education, median income, percent of the workforce that is female, mean elevation of the parish, percent of the population below 5 years old, and percent of the population that voted in the 2000 presidential election.
The second method is a discriminant analysis method. In this method an a priori grouping based on the number of coastal hazards, property damage, and population change for each county was derived. Twenty-four social, economic, and environmental variables were input into the discriminant analysis to determine if they can be used to explain and define resilience. The discriminant analysis results in a classification accuracy of 94.2%. Counties found to be in the most resilient group were Hancock, MS, Collier, FL, Baldwin, AL, Escambia, FL, Walton, FL, Lee, FL, Charlotte, FL, Manatee, FL, Santa Rosa, FL, Okaloosa, FL. Counties found to be in the least resilient group were Kleberg, TX, Calhoun, TX, San Patricio, TX, Jefferson, TX, Nueces, TX, Kenedy, TX, and Willacy, TX.
This study represents a preliminary attempt in quantifying community resilience. It outlines the methods that can be used to define resilience and offers a general guideline about the variables that might contribute to a communities ability to recover from a coastal disaster. Further refinements with the variables are necessary in future studies.
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