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Table 1 Overview of gridded population datasets currently available

From: A two-stage cluster sampling method using gridded population data, a GIS, and Google EarthTM imagery in a population-based mortality survey in Iraq

Dataset Provider(website) Spatial resolution Input population data source Interpolation method Ancillary data Year(s)
GPWv3.0 CIESIN (http://sedac.ciesin.columbia.edu/gpw/) 2.5’(~5 km2) UNPD census data Areal weighting 1 -None 1990,1995, 2000,2005 (projection),2010 (projection), 2015 (projection)
GRUMPv1 CIESIN (http://sedac.ciesin.columbia.edu/gpw/) .5’(~1 km2) UNPD census data Dasymetric mapping 2 -Night-time light imagery-Populated places 2000
LandScanTM ORNL (http://www.ornl.gov/sci/landscan/) .5’(~1 km2) Population Division of the U.S. Census Bureau Smart interpolation 3 -Land cover-Road networks-Digital elevation models-Slope-Satellite imagery 2008
  1. Adapted from [27].
  2. 1 Areal weighting overlays a grid onto sub national administrative unit population data and distributes the population across space according to the proportion of the administrative unit area that is contained within the grid cell [28].
  3. 2 Dasymetric mapping disaggregates sub national population estimates into grid units using ancillary data such as road networks [28, 29].
  4. 3 Smart interpolation disaggregates sub national population estimates to grid cells according to likelihood co-efficients of population occurrence derived from ancillary data such as proximity to roads, slope, land cover [30].