Integrating satellite-based human settlement detection probability in spatial population modelling

Music City Center 

Advanced statistical modelling techniques which utilise satellite-based human settlement data within a robust geostatistical modelling framework have been developed to fill small area population data gaps and support development and humanitarian programmes, across many countries of the world. However, the detection of human settlement by remote sensing satellites can be affected by environmental and topographical factors such as canopy, snow or cloud cover, and topographical variations in mountainous landscapes, and similarities between buildings and surrounding landscapes. Here, using a Bayesian statistical hierarchical joint modelling approach, we extend existing geospatial estimation methods by simultaneously modelling human settlement detection probability and population density, to account for false detection rates in satellite observations within a coherent bottom-up population modelling strategy. Our methodology was validated using a simulation study and showed a reduction of between 21% to 49% in relative bias, and a 28% reduction in relative bias when applied to produce gridded population estimates (at 100m-by-100m resolution) for Democratic Republic of Congo.

Bayesian Geospatial Joint Modelling, Small area population estimates, Remote sensing, False positive rates, Geoststitics, INLA-SPDE, Hierarchical models 

Section on Statistical Computing