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A multiple additive regression tree analysis of three exposure measures during Hurricane Katrina
Author(s) -
Curtis Andrew,
Li Bin,
Marx Brian D.,
Mills Jacqueline W.,
Pine John
Publication year - 2011
Publication title -
disasters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.744
H-Index - 70
eISSN - 1467-7717
pISSN - 0361-3666
DOI - 10.1111/j.1467-7717.2010.01190.x
Subject(s) - hurricane katrina , regression analysis , poison control , statistics , regression , population , occupational safety and health , disadvantaged , environmental health , linear regression , flood myth , econometrics , geography , natural disaster , medicine , mathematics , economics , archaeology , pathology , meteorology , economic growth
This paper analyses structural and personal exposure to Hurricane Katrina. Structural exposure is measured by flood height and building damage; personal exposure is measured by the locations of 911 calls made during the response. Using these variables, this paper characterises the geography of exposure and also demonstrates the utility of a robust analytical approach in understanding health‐related challenges to disadvantaged populations during recovery. Analysis is conducted using a contemporary statistical approach, a multiple additive regression tree (MART), which displays considerable improvement over traditional regression analysis. By using MART, the percentage of improvement in R‐squares over standard multiple linear regression ranges from about 62 to more than 100 per cent. The most revealing finding is the modelled verification that African Americans experienced disproportionate exposure in both structural and personal contexts. Given the impact of exposure to health outcomes, this finding has implications for understanding the long‐term health challenges facing this population.