When disasters hit, we’re still largely guessing where help is needed most.
Most tools rank places as “more” or “less” vulnerable—but they don’t tell responders what will actually be needed. Medical support? Food? Housing repair? Those differences matter.
Our new paper in Information Systems Research (Articles in Advance) introduces the Local Impact Vulnerability Assessment (LIVA) a framework for moving from static vulnerability scores to need-specific, hazard-aware prediction.
The core idea is simple: vulnerability isn’t one thing and a single metric doesn’t get to the core of a need. It depends on the hazard and the community. The same event can create very different needs in different places.
By re-framing vulnerability as something that should anticipate specific outcomes, not just assign scores, LIVA is designed to support better decisions about where and how to allocate limited resources. As a framework, it is extendable to any targeted use case above and beyond the examples we incorporate into the paper.
Our framework’s goal is straightforward — improve putting the right help in the right place at the right time.
I deeply grateful for my coauthors, Monica Chiarini Tremblay Rajiv Kohli, Arturo Castellanos, and Yolande Pengetnze, the editorial team Ahmed Abbasi and Heng Xu, and the dedicated reviewers, for all the support, feedback, and challenge to make this project a success.