Poverty rate prediction using multi-modal survey and earth observation
  data
Simone Fobi Manuel Cardona Elliott Collins Caleb Robinson Anthony Ortiz Tina Sederholm Rahul Dodhia Juan Lavista Ferres
This work presents an approach for combining household demographic and living
standards survey questions with features derived from satellite imagery to
predict the poverty rate of a region. Our approach utilizes visual features
obtained from a single-step featurization method applied to freely available
10m/px Sentinel-2 surface reflectance satellite imagery. These visual features
are combined with ten survey questions in a proxy means test (PMT) to estimate
whether a household is below the poverty line. We show that the inclusion of
visual features reduces the mean error in poverty rate estimates from 4.09% to
3.88% over a nationally representative out-of-sample test set. In addition to
including satellite imagery features in proxy means tests, we propose an
approach for selecting a subset of survey questions that are complementary to
the visual features extracted from satellite imagery. Specifically, we design a
survey variable selection approach guided by the full survey and image features
and use the approach to determine the most relevant set of small survey
questions to include in a PMT. We validate the choice of small survey questions
in a downstream task of predicting the poverty rate using the small set of
questions. This approach results in the best performance -- errors in poverty
rate decrease from 4.09% to 3.71%. We show that extracted visual features
encode geographic and urbanization differences between regions.