5 Understanding Risk Communications with Vulnerable Populations: Designing Focus Groups Research Using Data From Multiple Sources

Monday, 23 January 2017
4E (Washington State Convention Center )
Jen Henderson, Virginia Polytechnic Institute and State University, Blacksburg, VA; and H. Lazrus, O. Wilhelmi, R. E. Morss, and A. Dietrich

On October 29, 2012, Hurricane Sandy made landfall on the eastern coast of the United States, causing billions in damages and killing more than 200 people. To better understand how affected populations received, understood, and acted on information disseminated before the storm arrived, a group of researchers from NCAR, Virginia Tech, and Wagner College assembled a methodology appropriate to this inquiry. The focus of the study was on groups of populations that are usually considered vulnerable by the hazards and disaster research community. The methodology included eight focus groups in New York City with Russian-speaking elders, those who live in senior and public housing, Spanish-speaking immigrants, and residents of lower socio-economic neighborhoods. Based on specific research priorities, multiple types of observational information at different scales and from various sources were used to identify these groups. Researchers used census data to provide initial information about population sensitivities, maps of exposure to flooding and storm surge to identify neighborhoods at risk, and local community networks in New York to select populations who might not have had access to hazard communications via digital and social media. This poster highlights how the research team combined multiple layers of observational data in meaningful ways to design the focus group methodology.  This careful and multi-faceted research design resulted in new insight into unique features of vulnerability and revealed how vulnerable groups may draw on their adaptive capacities and social capital to become more resilient during a disaster. Ultimately, the authors demonstrate the complexity of focus group research design and how effective results can be when different types of data are brought together in multi-layered, deliberate ways.
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