250367 Combining Cluster and Factor Analysis to Examine the Effects of Social Vulnerability on Recovery

Monday, October 31, 2011: 3:30 PM

Yoon Soo Park, PhD , Research and Development, Educational Testing Service, Princeton, NJ
Jonathan J. Sury, MPH, CPH , National Center for Disaster Preparedness, Columbia University, New York, NY
David M. Abramson, PhD MPH , National Center for Disaster Preparedness, Columbia University, New York, NY
Tracking post-disaster repopulation is often limited to changes in structural foundations (e.g., houses rebuilt) or census survey data to estimate the rate of recovery. Recently, deliverable address counts at the census block group level from the US Postal Service, have served as a proxy for repopulation, to assess recovery from Hurricane Katrina in Orleans Parish. Although 83% of the Parish was flooded, 71% of the addresses were deliverable in the summer of 2008, increasing to 76% the following year.

Scholars have examined the effects of social vulnerability using principal components analysis based on census data. However, these analytic methods do not have measurement errors associated in the model; therefore, it is difficult to interpret factors in the measure. This study refines these measures by using the extended similarities tree, a nonparametric clustering algorithm that shows overlapping and hierarchical features among variables. This procedure selected key factors from census data that described a community's social vulnerability. Reliability and appropriate fit of the measures were tested using confirmatory factor analysis. Applying the newly derived social vulnerability indices, with deliverable addresses as the outcome, a random-effects growth model and geographically weighted regression were utilized to investigate non-spatial and spatial trends in New Orleans repopulation. Preliminary results revealed areas dominated by middle class, working poor, farming, and transient households had significantly lower rates of repopulation over time. This combination of cluster and factor analytic methods to measure social vulnerability and its effect on repopulation demonstrates a convenient and meaningful method to investigate community recovery.

Learning Areas:
Biostatistics, economics
Public health or related research
Social and behavioral sciences

Learning Objectives:
1. Evaluate the use of census data to examine community recovery in an area affected by a disaster. 2. Demonstrate the application of combining cluster and factor analytic methods to design measures of social vulnerability. 3. Analyze the association between repopulation data (number of deliverable households pre and post disaster) and social vulnerability of communities affected by Hurricane Katrina using longitudinal data analysis and geographically weighted regression.

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I am a Data Manager and Analyst at the National Center for Disaster Preparedness at Columbia University where I conduct research on population recovery from disaster using census data. I am also a doctoral candidate specializing in Measurement and Statistics at Columbia University.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.