We will study the use of very high-resolution covariates in top-down population model and its impact on population gridded estimates. We will examine building-footprint derived covariates in Sierra Leone population model. Disclaimer: This post is still being drafted.
Disclaimer: This post is still being drafted.
To obtain high-resolution population estimates, WorldPop developed a top-down method that disagregates population totals into grid cells thanks to ancillary geospatial covariates.
The method consists in fitting a random-forest model (Breiman 2001) at the finest administrative level for which population count is available. The estimated model is then used to predict population density for every grid cell thanks to the geospatial covariates. Finally, the predicted gridded population density layer plays the role of a weighting layer to breakdown the population totals into the grid cells (Stevens et al. 2015).
The geospatial covariates commonly used by WorldPop initative for mapping population globally (WorldPop Research Group et al. 2018) are based1:
Resampled DMSP-OLS night-time lights 2000-2011
Resampled VIIRS night-time lights 2012-2016
SRTM-based elevation per country 2000
SRTM-based slope per country 2000
The input rasters have a resolution ranging from 900m to 90 m at the Equateur (Lloyd et al. 2019).
Distance to European Space Agency Land cover categories
Distance to International Union for Conservation of Nature strict nature reserve and wilderness area edges
Distance to open-water coastline per country
Distance to OpenStreetMap (OSM) major roads, major road intersections and major waterways
Rasters resulting from Euclidian distance measurement can have virtually any spatial resolution. They however might not be representative of localized informative variations for population modelling.
heir distribution
The purpose of this post will be to study the impact of the building-footprint derived covariates on the modelling and subsequently on the gridded estimates in Sierra Leone.
Recently, WorldPop released openly gridded building patterns across entire sub-Saharan Africa at 100m resolution (Dooley and Tatem 2020). These were derived from building footprints extracted from satellite imagery with a spatial accuracy of 6m (Ecopia.AI and Maxar Technologies 2019). In top-down modelling, this data has already been used to produce an alternative global set of gridded population estimates constraining the estimates to settled grid cells2 (Bondarenko et al. 2020).
They can be downloaded at: https://www.worldpop.org/project/categories?id=14↩︎
Settled cells are cells containing at least one building footprint.↩︎
For attribution, please cite this work as
Darin (2021, June 2). Meet Edith: How fine can a top-down population model be? [draft]. Retrieved from https://edarin.github.io/thatsme/posts/2021-06-02-high-resolution-covariates-and-top-down-population-disagregation/
BibTeX citation
@misc{darin2021how, author = {Darin, Edith}, title = {Meet Edith: How fine can a top-down population model be? [draft]}, url = {https://edarin.github.io/thatsme/posts/2021-06-02-high-resolution-covariates-and-top-down-population-disagregation/}, year = {2021} }