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.Comment: In 2023 ACM SIGCAS/SIGCHI Conference on Computing and Sustainable
Societies (COMPASS 23) Short Papers Trac