Algorithm-Based Strategy Shows Promise in Reducing Urban Poverty
When the COVID-19 pandemic hit, aid organizations worldwide struggled to identify vulnerable households quickly and fairly.
Woojin Jung, an assistant professor at the Rutgers School of Social Work, said she has found a better strategy.
“Existing approaches don’t always work during shocks or rapid changes,”
Her team has developed a method that blends sociodemographic data and household surveys with community perceptions and satellite imagery to predict urban poverty.
- Conventional methods of identifying at-risk populations rely on analysis of household demographic data.
- The new strategy puts people at the center of aid targeting.
The findings are published in the journal Sustainable Cities and Society.
Author's summary: Algorithm helps reduce urban poverty.
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