Creating an empirically derived community resilience index of the Gulf of Mexico region
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...
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ndltd-LSU-oai-etd.lsu.edu-etd-06072009-1755252013-01-07T22:52:15Z Creating an empirically derived community resilience index of the Gulf of Mexico region Baker, Ariele Nicole Environmental Studies 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. Nina Lam James Wilkins Margaret Reams LSU 2009-06-09 text application/pdf http://etd.lsu.edu/docs/available/etd-06072009-175525/ http://etd.lsu.edu/docs/available/etd-06072009-175525/ en unrestricted I hereby certify that, if appropriate, I have obtained and attached herein a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to LSU or its agents the non-exclusive license to archive and make accessible, under the conditions specified below and in appropriate University policies, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
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Environmental Studies Baker, Ariele Nicole Creating an empirically derived community resilience index of the Gulf of Mexico region |
description |
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.
|
author2 |
Nina Lam |
author_facet |
Nina Lam Baker, Ariele Nicole |
author |
Baker, Ariele Nicole |
author_sort |
Baker, Ariele Nicole |
title |
Creating an empirically derived community resilience index of the Gulf of Mexico region |
title_short |
Creating an empirically derived community resilience index of the Gulf of Mexico region |
title_full |
Creating an empirically derived community resilience index of the Gulf of Mexico region |
title_fullStr |
Creating an empirically derived community resilience index of the Gulf of Mexico region |
title_full_unstemmed |
Creating an empirically derived community resilience index of the Gulf of Mexico region |
title_sort |
creating an empirically derived community resilience index of the gulf of mexico region |
publisher |
LSU |
publishDate |
2009 |
url |
http://etd.lsu.edu/docs/available/etd-06072009-175525/ |
work_keys_str_mv |
AT bakerarielenicole creatinganempiricallyderivedcommunityresilienceindexofthegulfofmexicoregion |
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1716477740996100096 |