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,”
Jung said.
“We wanted to find a way to identify vulnerable households at speed and scale in urban settings.”
Her team has developed a method that blends sociodemographic data and household surveys with community perceptions and satellite imagery to predict urban poverty.
The findings are published in the journal Sustainable Cities and Society.
Conventional methods of identifying at-risk populations rely on analysis of household demographic data.
Author's summary: Algorithm helps reduce urban poverty.