Poverty is one of the most important determinants of adverse health outcomes
globally, a major cause of societal instability and one of the largest causes of lost
human potential. Traditional approaches to measuring and targeting poverty
rely heavily on census data, which in most low- and middle-income countries
(LMICs) are unavailable or out-of-date.
Alternate measures are needed to comp-
lement and update estimates between censuses. This study demonstrates how
public and private data sources that are commonly available for LMICs can be
used to provide novel insight into the spatial distribution of poverty. We evalu-
ate the relative value of modelling three traditional poverty measures using
aggregate data from mobile operators and widely available geospatial data.
Taken together, models combining these data sources providethebest predictive
power (highest
r
2
¼
0.78) and lowest error, but generally models employing
mobile data only yield comparable results, offering the potential to measure
poverty more frequently and at finer granularity. Stratifying models into
urban and rural areas highlights the advantage of using mobile data in urban
areas and different data in different contexts. The findings indicate the possibility
to estimate and continually monitor poverty rates at high spatial resolution in
countries with limited capacity to support traditional methods of datacollection.