Incorporating individual-level information into spatial hazards vulnerability assessments

Authors: Joseph Tuccillo*, University of Colorado, Boulder, Seth Spielman, University of Colorado-Boulder
Topics: Hazards and Vulnerability, Population Geography, Spatial Analysis & Modeling
Keywords: Hazards, Vulnerability, Hurricane, Census, Microdata
Session Type: Paper
Day: 4/5/2019
Start / End Time: 5:00 PM / 6:40 PM
Room: Balcony B, Marriott, Mezzanine Level
Presentation File: No File Uploaded


Critical to measuring place-specific hazard vulnerability is understanding how individuals in a place could themselves be impacted. Individuals (people, households) are vulnerable via intersections of social, economic, and health-related factors that affect their response capacity to the hazard. Mapping specific populations of vulnerable individuals in a hazard-affected area reveals where and which interventions/policies are most appropriate to mitigate hazard exposure, ensure safety, and support recovery efforts. Though inclusion of these insights into vulnerability assessments could benefit areas like hazard planning and emergency response, this is limited by a common reliance on aggregate population data that masks individual-level concerns.
We present a novel workflow for identifying and mapping groups of vulnerable individuals. We allocate individual-level data from the American Community Survey’s (ACS) Public-Use Microdata Sample (PUMS) to small census areas to produce results with both high demographic and high spatial resolution. Such results overcome a longstanding problem of coarse spatial resolution when using PUMS data to assess vulnerability.

We illustrate our methods by first identifying two key vulnerable groups in Coney Island, NYC, during Hurricane Sandy (in poverty, no car, limited English proficiency; frail elderly), then comparing the block group-level distribution of each to a storm surge inundation zone. Our results reveal neighborhood-specific patterns of high-priority block groups in the hazard zone. We validate these findings with a simple uncertainty analysis based on PUMS replicate estimates. We conclude by discussing how our methods lay the groundwork to develop place-based vulnerability metrics that account for impacts to individuals and communities alike.

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