title: A multiple additive regression tree analysis of three exposure measures during Hurricane Katrina.
abstract: 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.
authors: Department of Geography, University of Southern California, Los Angeles, CA 90089–0255, United States. ajcurtis@usc.edu
authors: Andrew
authors: A
authors: Curtis
authors: Bin
authors: B
authors: Li
authors: Brian D
authors: BD
authors: Marx
authors: Jacqueline W
authors: JW
authors: Mills
authors: John
authors: J
authors: Pine
mesh_terms: D001741:Black or African American / Q000523:psychology* / Q000706:statistics & numerical data; D055867:Cyclonic Storms; D004190:Disasters; D005260:Female; D055868:Floods; D054624:Health Status Disparities; D006798:Housing; D006801:Humans; D008297:Male; D055820:New Orleans; D012044:Regression Analysis; D055883:Structure Collapse / Q000706:statistics & numerical data*