Development of Algorithms to Estimate Post-Disaster Population Dislocation--A Research-Based Approach

Abstract

This study uses an empirical approach to develop algorithms to estimate population dislocation following a natural disaster. It starts with an empirical reexamination of the South Dade Population Impact Survey data, integrated with the Miami-Dade County tax appraisal data and 1990 block group census data, to investigate the effects of household and neighborhood socioeconomic characteristics on household dislocation. The empirical analyses found evidence suggesting that households with higher socio-economic status have a greater tendency to leave their homes following a natural disaster. Then one of the statistical models is selected from the empirical analysis and integrated into the algorithm that estimates the probability of household dislocation based on structural damage, housing type, and the percentages of Black and Hispanic population in block groups. This study also develops a population dislocation algorithm using a modified Hazard-US (HAZUS) approach that integrates the damage state probabilities proposed by Bai, Hueste and Gardoni in 2007, accompanied with dislocation factors described in HAZUS to produce structural level estimates. These algorithms were integrated into MAEviz, the Mid-American Earthquake Centers Seismic Loss Assessment System, to produce post-disaster dislocation estimates at either the structure or block group level, whichever is appropriate for the user's planning purposes. Sensitivity analysis follows to examine the difference among the estimates produced by the two newly-developed algorithms and the HAZUS population dislocation algorithm.