Data and code from: Foundations for digital twins: Spatially disaggregated synthetic populations of refugee and IDP settlements

Refugee and internally displaced people (IDPs) settlements are highly
dynamic, with rapid changes to demographic and geographical structures.
Knowledge of the population size, disaggregated by demographic attributes,
is essential to informing humanitarian programming and settlement planning
by humanitarian organisations. However, access to such data, when formal
censuses of settlements have not been conducted creates a significant
barrier. In this paper, we present a methodological framework for
estimating settlement censuses by combining globally available satellite
imagery with aggregate national census data from the population's
country of origin generate a spatially disaggregated synthetic population
of the settlement. The creation of such synthetic populations serves as a
foundational layer for Digital Twins and simulation models, enabling: the
integration of these disparate datasets at different levels of
granularity; and provides decision-makers with a spatially disaggregated
dynamic model of the population to inform response requirements before
sending humanitarian teams into new settlements and later informing survey
methods, or used to simulate scenarios for policy planning and public
health responses among others. We develop and validate our framework in
three diverse contexts: the Zaatari and Cox's Bazar settlements in
Jordan and Bangladesh; and the Kismayo displacement settlement in Somalia.