Despite the availability of multiple global population distribution datasets, these datasets are limited by their lack of demographic depth. Although large area spatial datasets of population distributions currently exist, similar spatial representations of other demographic and socioeconomic characteristics are scarce. Spatial microdata that include detailed demographic information are rarely available for small areas, thus limiting the complex analysis of population subgroups. To address the lack of demographic resolution in existing population distribution datasets, a first step would be to develop large area microdata that can be attached to a country- or global-level population distribution dataset. This can be achieved by reweighting a national level sample so as to estimate the detailed socioeconomic characteristics of populations and households at a small area level. In essence, this modelling approach combines individual or household-level microdata for large spatial areas with spatially disaggregate data in order to create synthetic microdata estimates for small areas.
Methods to build synthetic spatiodemographic microdata are well documented in literature, yet these efforts have been implemented on limited geographic extent in data rich environments. More specifically, these methods have been tailored to fit specific local, regional, or national data sources with no plan or requirement for adaptation for other geography or data. To address this gap, this research will present a generalizable method for developing synthetic spatial microdata which in turn can be used to increase the demographic resolution of global population distribution data.