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Slide 1
May 28, 2009
Advancing Coastal Community Resilience
A Brief Project Overview
This investigation was funded by a grant from the National Oceanic and Atmospheric Administration administered by the Coastal Services Center. The views expressed herein are those of the authors and do not necessarily reflect the views of NOAA or any of its sub-agencies.
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Project Goals
Develop a suite of Community Disaster Resilience Indicators for:
Coastal counties along the Gulf Coast
These will be broad-based indicators that are readily available from secondary data sources
Use the results to inform local community CDRI
Local communities and municipalities like Galveston
These will be more specific indicators that communities can readily identify and act upon to shape resiliency in both the short and long term.
Should be shaped by local community input.
2
First Step:Defining DISASTER RESILIENCE
Three common elements emerged from the literature suggesting that disaster resilience should be defined as the ability of a community to:
absorb, deflect or resist disaster impacts
bounce back after being impacted, and
learn from experience and modify its behavior and structure to adapt to future threats
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Second Step:Developing A Conceptual Framework
It was critical to consider all phases of disaster
Mitigation (perceptions and adjustments)
Preparedness (planning and warning)
Response (pre and post impact)
Recovery (restoration and reconstruction)
It was also critical to consider a communitys capital resources
Social
Economic
Physical
Human
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COMMUNITY DISASTER RESILIENCE FRAMEWORK (CDRF)
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Framework Matrix For Indicator Selection
DISASTER PHASES ACTIVITIESCAPITAL DOMAINS INDICATORSI: HAZARD MITIGATIONSocial CapitalEconomic CapitalPhysical CapitalHuman CapitalExample of activities:Building dams, levees, dikes, and floodwalls.Landuse planning to prevent development in hazardous areasStrengthening buildings through building codes and building standards.Protecting natural environment e.g., wetlands Indicator 1Indicator 2Indicator kIndicator 1Indicator 2Indicator kIndicator 1Indicator 2Indicator kIndicator 1Indicator 2Indicator kII: DISASTER PREPAREDNESSExample of activities:Developing response proceduresDesign and installation of warning systems, Developing plans for evacuation Emergency preparations (Exercise & Drills)Training of emergency personnel Stockpiling of resources e.g., medical suppliesIndicator 1Indicator 2Indicator kIndicator 1Indicator 2Indicator kIndicator 1Indicator 2Indicator kIndicator 1Indicator 2Indicator k1
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5
3
6
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7
8
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9
13
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16
10
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Framework Matrix For Indicator Selection
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Example of DISASTER RESPONSE Indicators
DISASTER RESPONSE ACTIVITIESRESILIENCE INDICATORSSocial capitalEconomic capitalPhysical capitalHuman capital(1) Securing the impacted area Financial resources Fire and police stations Police officers Fire fighter personnel (2) Warning Social networks- Friends, relatives, neighbors, and coworkers Financial resources Television Radio Newspapers Internet Telephone Communication language; e.g. English(3) Evacuation Social networks- Friends, relatives, neighbors, and coworkers Financial resources Transportation & Communication networks Personal vehicles Transportation & communication employees Drivers(4) Search & Rescue (SAR) Social networks-Friends, relatives, neighbors, and coworkers Financial resources Fire and police stations Police officers Fire fighter personnel CERT members(5) Providing emergency medical care Social networks-Friends, relatives, neighbors, and coworkers Non-governmental organization (NGOs Financial resources Hospitals Nursing homes Emergency Medical Services (EMS) personals Physicians(6) Sheltering evacuees and other victims Non-governmental organization (NGOs), e.g. Red Cross, Salvation Army Financial resources Schools Public buildings Hotels Housing units Volunteers8
Third Step:Data Collection And Testing
Identified more than 120 capital indicators initially identified
But final number was reduced to 75 indicators: social (9); economic (6), physical (35), and human (25)
Assembling the data for gulf coast counties:
144 coastal counties
Florida 42; Texas 41; Louisiana 38; Mississippi 12; Alabama 8; and Georgia 3.
Combined the indicators into a variety of resiliency indices
Overall County Disaster Resilience Index (CDRI)
Separate indices for mitigation, preparation, response, and recovery
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Standardizing Indicators
Scale adjustment of indicators
Each indicator was converted into a relative measure e.g., percentage or rate (per 1000)
2) Standardizing/normalizing indicators
Each indicator was converted into z-score
Z-score =
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Unit Of Analysis And Data Sources
What is a unit of analysis?
County is a unit of analysis for this study
Why county is chosen as the unit of analysis?
Because most of FEMAs efforts are centered at county level and
With limited resources, county data are easy to collect
Data sources?
U.S. Census data
SHELDUS: Spatial Hazard Events and Losses Database for the U.S
NFIP: National Flood Insurance Program
CDC: Centers for Diseases Control and Prevention
CRA: NOAAs Coastal Risk Atlas
FEMA: Federal Emergency Management Agency
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Study Region
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Reliability Assessment
INDEX ITEMSITEMALPHASocial capital9.659Economic capital6.914Physical capital35.786Human capital25.731Overall CDRI-14.844INDEX ITEMSITEMALPHAHazard Mitigation Sub-index45.862Disaster Preparedness Sub-index25.794Disaster Response Sub-index42.773Disaster recovery Sub-index28.814Overall CDRI-34.979Overall the reliability assessment suggest that the sub-indices and the CDRI exhibited a relatively high level of consistency - suggesting that the measures are reliable
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Mapping Coastal County Resiliency
Spatial Distribution of CDRI Scores
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Spatial Distribution of CDRI Preparation Scores
Mapping Coastal County Resiliency
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Spatial Distribution of CDRI Recovery Scores
Mapping Coastal County Resiliency
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Spatial Distribution of CDRI Mitigation Scores
Mapping Coastal County Resiliency
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Spatial Analysis
LISA Cluster Map for CDRI-1
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Additional Findings
The picture is highly uneven with respect to States: Florida counties had the highest average CDRI scores, followed, not so closely, by Alabama, Georgia, Mississippi, and Louisiana, with Texas counties, on average, at the bottom.
StateCDRIMean ScoreRankFlorida.25391Alabama.00672Georgia-.04793Mississippi-.08604Louisiana-.09815Texas-.1418619
Additional Findings
In general, counties, with comprehensive planning, that adopt hazard relevant building codes and zoning regulations, that participate in FEMA CRS rating, and implement other similar policies, were more disaster resilient.
TOP 10 LIST BOTTOM 10 LIST RankCountyStateScoreRankCountyStateScore1Monroe Florida 1.44135West Feliciana Louisiana -0.612Leon Florida 1.12136Kenedy Texas -0.613Collier Florida 1.03137Vernon Louisiana -0.674Sarasota Florida 1.02138Webb Texas -0.685Franklin Florida 0.90139Cameron Texas -0.726Lee Florida 0.72140Bee Texas -0.737East Baton Rouge Louisiana 0.69141Hidalgo Texas -0.818Baldwin Alabama 0.68142Duval Texas -0.929Fayette Texas 0.68143Willacy Texas -0.9810Okaloosa Florida 0.67144Starr Texas -1.3220
Initial Test Results Are Promising
Theoretical expectations of the relationship between the validity measures and the CDRI
The more disaster resilient a county, the:
Lower the number of flood-related deaths (-)
Lower the level of total property damage (-)
Lower the level of uninsured property damage (-)
Lower the level of social vulnerability (-)
A coastal community located in a high risk areas will display higher levels of disaster resilience (+)
Preformed well in more complex models as well, yielding hypothesized results.
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Construct Validity: Correlations
VALIDITY MEASURECDRI-1(1) Deaths due to flooding -.420***(2) Total flood property damage-.239**(4) Uninsured flood property damage-.223**(5) Social vulnerability index-.308**(6) Wind risk.291**(7) Flood risk.270**(8) Surge risk.141(9) Total risk (wind, flood, and surge).266**Note: * = prob (r) .05; ** = prob (r) .01; *** = prob (r) .10
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Predictive Validity: Regression Analysis
Regression analysis was employed to assess predictive validity of the measure
Specifically the regression analysis was used to determine if the CDRI measure displayed the expected and statistically significant impacts on flood damage and flood-related deaths after controlling for total risk and social vulnerability
Two regression analysis methods were employed:
OLS regression model
Zero-truncated poisson (ZTP) regression model
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Predictive Validity: Regression Analysis
VariableUnstandardized CoefficientStandardized CoefficientStandard errort-valueP-valueCDRI-1-.507-.162.276-1.836.035*Social vulnerability-.120-.314.032-3.701.000Total risk.110.250.0372.925.004Constant6.007.22127.183.000Note: N =144; F-statistic = 7.428; Significance = .000; R 2 = .150; adjusted R 2 = .130; * = one tailed probability
Effect of CDRI-1 on the Total Flood Property Damage
VariableUnstandardized CoefficientStandardized CoefficientStandard errort-valuep-valueCDRI-1.874.274.2383.667.000Social vulnerability.020.052.028.723.471Total risk.233.512.0337.166.000Constant4.969.19625.319.000Effect of CDRI-1 on Insured Flood Property Damage
Note: N =144; F-statistic = 28.296; Significance = .000; R 2 = .403; adjusted R 2 = .388
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Effect of CDRI-1 on Uninsured Flood Property Damage
VariableUnstandardized CoefficientStandardized CoefficientStandard errort-valueP-valueCDRI-1-.614-.207.275-2.236.027Social vulnerability-.117-.333.032-3.684.000Total risk.069.178.0351.937.055Constant6.368.21230.019.000Note: N =144; F-statistic = 6.531; Significance = .000; R 2 = .156; adjusted R 2 = .132
VariableCoefficientStandard errorzP>|z|95% Conf. Interval95% Conf. IntervalCDRI-1-1.915536.2134428-8.970.000-2.333876-1.497196Social vulnerability .3740619015266924.500.000.3441393.4039844Total risk.1615034.014681311.000.000.1327286.1902781Constant1.173595.16241267.230.000.85527221.491918Effect of the CDRI-1 on Deaths due to Flooding
Note: N =22; Chi2 = 1492.74, df = 3, Significance = .001; Pseudo R2 = 0.5563
Predictive Validity: Regression Analysis
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Conclusions thus far :
The overall findings suggest that the CDRI has potential as a measure of community resilience that we hope will facilitating future research and promote disaster resilience
This research was based on secondary data only, future research should attempt to integrated both secondary and primary data.
County is a problematic unit of analysis, particularly for concerned citizens, local officials, and planners.
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Resiliency Workshop
Focus on the local community level indicators
Primary focus on general community priorities and policies that can shape resiliency in the short and long term
And can be readily implemented.
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Workshop Results
2To what extent should LAND USE PLANNING be considered a high priority in your jurisdiction1Extremely high1066.7%2Very high426.7%3Average priority16.7%4Below average priority00.0%5Low priority00.0%N1528
Workshop Results
4To what extent should DISASTER REDUCTION be considered a high priority in your jurisdiction1Extremely high1493.3%2Very high16.7%3Average priority00.0%4Below average priority00.0%5Low priority00.0%N153To what extent should HOUSING be considered a high priority in your jurisdiction1Extremely high853.3%2Very high320.0%3Average priority426.7%4Below average priority00.0%5Low priority00.0%N1529
Workshop Results
2Which 3 statements best capture your belief regarding development regulations1Create desirable patterns of community growth964.3%2Protect environmental quality14100.0%3Reduce hazard impacts1178.6%4Are expensive to implement00.0%5Create land ownership/property rights problems321.4%6Create public/special interest opposition321.4%7Require technical assistance not available locally214.3%8Reduce economic competitiveness00.0%N141Select the top 3 issues that should be considered for community coastal resilience1Economic development213.3%2Land use planning1280.0%3Housing426.7%4Disaster reduction1280.0%5Transportation426.7%6Other infrastructure (water, sewer, electricity)426.7%7Climate change16.7%8Environmental protection640.0%9Recreation00.0%N1530
Workshop Results
4Which 3 statements best capture your belief regarding property acquisition programs1Create desirable patterns of community growth1071.4%2Protect environmental quality1285.7%3Reduce hazard impacts1071.4%4Are expensive to implement214.3%5Create land ownership/property rights problems428.6%6Create public/special interest opposition428.6%7Require technical assistance not available locally00.0%8Reduce economic competitiveness00.0%N143'Which 3 statements best capture your belief regarding building standards2Protect environmental quality1178.6%3Reduce hazard impacts1392.9%4Are expensive to implement214.3%5Create land ownership/property rights problems17.1%6Create public/special interest opposition214.3%7Require technical assistance not available locally321.4%8Reduce economic competitiveness00.0%N1431
Workshop Results
5Which 3 statements best capture your belief regarding incentive tools1Create desirable patterns of community growth1285.7%2Protect environmental quality1285.7%3Reduce hazard impacts964.3%4Are expensive to implement321.4%5Create land ownership/property rights problems214.3%6Create public/special interest opposition214.3%7Require technical assistance not available locally00.0%8Reduce economic competitiveness17.1%N1432
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Deviation
Standard
Value
Mean
Value
Actual
TEXAS
FLORIDA
GEORGIA
ALABAMA
LOUISIANA
MISSISSIPPI
100010020030040050
Miles
Legend
Low resilience
High resilience
TEXAS
FLORIDA
GEORGIA
ALABAMA
LOUISIANA
MISSISSIPPI
100010020030040050
Miles
Legend
Low disaster preparedness
High disaster preparedness
TEXAS
FLORIDA
GEORGIA
ALABAMA
LOUISIANA
MISSISSIPPI
100010020030040050
Miles
Legend
Low disaster recovery
High disaster recovery
TEXAS
FLORIDA
GEORGIA
ALABAMA
LOUISIANA
MISSISSIPPI
100010020030040050
Miles
Legend
Low hazard mitigation
High hazard mitigation
TEXAS
FLORIDA
GEORGIA
ALABAMA
LOUISIANA
MISSISSIPPI
Legend
Not Significant
High-High
Low-Low
Low-High
High-Low
100010020030040050
Miles
