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Advancing Coastal Community Resilience . A Brief Project Overview. May 28, 2009.
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Advancing Coastal Community Resilience A Brief Project Overview May 28, 2009 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.
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.
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
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 community’s capital resources • Social • Economic • Physical • Human
Framework Matrix For Indicator Selection 1 2 3 4 5 6 7 8
Framework Matrix For Indicator Selection 10 15 9 11 13 14 15 16
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
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 =
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 FEMA’s 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: NOAA’s Coastal Risk Atlas • FEMA: Federal Emergency Management Agency
Reliability Assessment • Overall the reliability assessment suggest that the sub-indices and the CDRI exhibited a relatively high level of consistency - suggesting that the measures are reliable
Mapping Coastal County Resiliency Spatial Distribution of CDRI Scores
Mapping Coastal County Resiliency Spatial Distribution of CDRI Preparation Scores
Mapping Coastal County Resiliency Spatial Distribution of CDRI Recovery Scores
Mapping Coastal County Resiliency Spatial Distribution of CDRI Mitigation Scores
Spatial Analysis LISA Cluster Map for CDRI-1
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.
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.
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.
Construct Validity: Correlations Note: * = prob (r) .05; ** = prob (r) .01; *** = prob (r) .10
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
Predictive Validity: Regression Analysis Effect of CDRI-1 on Uninsured Flood Property Damage Note: N =144; F-statistic = 6.531; Significance = .000; R 2 = .156; adjusted R 2 = .132 Effect of the CDRI-1 on Deaths due to Flooding Note: N =22; Chi2 = 1492.74, df = 3, Significance = .001; Pseudo R2 = 0.5563
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.
