A statistical approach to small area synthetic population generation as a basis for carless evacuation planning

Natural or man-made hazards that require evacuation put already vulnerable populations in a more precarious situation. However, when plans and decisions about evacuation are made, the assumption of access to a private car is typically made and differences in income levels across a community is rarely accounted for. 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 paper, data from the American Community Survey, US Census, Internal Revenue Services and the National Household Travel Survey are used to generate 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 will allow officials to have a more accurate account of disadvantaged populations for emergency planning and identify locations of shelters, triage points as well as planning carless transportation services.

Automated summary

Evacuation plans often overlook low-income carless residents, leaving them stranded in emergencies. To improve emergency planning, identifying and locating these disadvantaged populations is crucial to providing necessary services and facilities.