Demographic Data Gaps and the Challenges of Population Modeling in Low-resource Settings

Abstract

The lack of demographic data poses challenges to accurate population modeling in low-resource
regions. This study aims to address the gaps within existing data frameworks by analyzing
contemporary modeling techniques and evaluating their reliability in the absence of census data.
A combination of satellite imagery, mobile data, local surveys, and other surveys resulted in the
development of new frameworks. Analysis indicates that while hybrid methods improve
estimates, variability in infrastructure and region can limit their effectiveness. The paper
highlights the innovations needed in data collection and modeling to construct policies that are
more efficient.