Table 1.
Overview of gridded population datasets currently available
| 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 |
Adapted from [27].
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].
2 Dasymetric mapping disaggregates sub national population estimates into grid units using ancillary data such as road networks [28,29].
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].