Global population distributions and the environment : discerning observed global and regional patterns.

Abstract

Between 1990 to 2015, numerous groups used ancillary data about the environment surrounding populations to more accurately map global populations from standard census data. No comprehensive study has been undertaken to characterize the observed relationships between population density and ancillary data. Better understanding these relationships may produce more accurate population maps, focus resources on new datasets with a high probability of modelling importance, and lead to expanded end-user applications. This study examined these relationships by extracting variable importances from 36 independently run, country-specific population models from the WorldPop project's population data. Covariate data describing urban/suburban extents were found to be the most significant predictors of population. Little difference was found in the resolution of urban/suburban data regarding their modelling importance. Further examination of the effect of different definitions of built-/urban-area, methods of quantifying input data quality, and the probability of specific variable classes as significant predictors of population is required.

How accurate are modelled birth and pregnancy estimates? Comparison of four models using high resolution maternal health census data in southern Mozambique

“…The RMSEs were computed with the assumption that the weight of all the residuals is 1 instead of assigning different weights 47. However, it is known that accuracy of disaggregation is also dependent on the non-intuitive relationships between population density and the supporting covariates of the areas being mapped 48…”