Cirillo, CinziaNejad, MohammadErdogan, Sevgi2020-10-142020-10-142020-02http://hdl.handle.net/11603/19882Data and code for this project can be found at https://github.com/mmnejad/Cnty2PumaNatural or man-made hazards that require evacuation put already vulnerable populations in a more precarious situation. When plans and decisions about evacuation are made, access to a private car is typically assumed, and differences in income levels across a community are rarely taken into account. The result is that carless members of a community can find themselves stranded. Low-income carless residents need alternative transportation means to reach shelters in case of an emergency. Thus, evacuation plans, decisions, and models need necessary information that identifies and locates these populations. In this study, data from the American Community Survey, U.S. Census, Internal Revenue Service, and the National Household Travel Survey are used to generate a synthetic population for Anne Arundel County, Maryland, using the copula concept. Geographic locations of low-income residents are identified within each subarea of the county (census tract) and their car ownership is estimated with a binomial logit model. The developed population synthesis method allows officials to have a more accurate account of populations for emergency planning and identify locations of shelters and triage points as well as planning carless transportation services.26Public Domain Mark 1.0http://creativecommons.org/publicdomain/mark/1.0/Synthetic population, Archimedean copulas, Accessibility, Car-ownership models, Evacuation planning, low income, carlessText