Continental and Country Population Grids: Summary Characteristics
|
Dataset |
Source |
Concept |
Method |
Grid Cell Size |
Year(s) Represented |
Continents / Countries Represented |
Distribution Policy |
|
Continental Population Grids |
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|
Eurostat |
Nighttime population (population counted at place of domicile) |
Bottom-up aggregation for most countries; intelligent dasymetric* mapping for others |
1 km (vector grid) |
2011 |
Europe (population data based on country-official estimates) |
Open access |
|
|
European Commission Joint Research Centre (JRC) |
Nighttime population (population counted at place of domicile) |
Intelligent dasymetric* mapping |
100 meter |
2011 |
Europe (population data based on country-official estimates) |
Open access |
|
|
Country Population Grids |
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|
US Census Bureau |
Nighttime population (population counted at place of domicile) |
A classification and regression tree methodology was used to create a percent impervious-area layer based on Landsat data. Dasymetric* mapping was used to distribute population proportionately to impervious areas. Details. |
100 meter |
Country specific |
Haiti (details), Pakistan, Rwanda |
Open access |
|
|
Facebook Connectivity Lab and CIESIN |
Nighttime population (population counted at place of domicile) |
Binary dasymetric* using Digital Globe 0.5 meter imagery to identify houses/ settlements and proportional allocation to distribute population data from subnational census data to the settlement extents. Details. |
30 meter (1 arc-seconds) |
2015 |
Algeria, Argentina, Burkina Faso, Cambodia, Ghana, Guatemala, Haiti, Indonesia, Ivory Coast, Kenya, Madagascar, Malawi, Mexico, Mozambique, Nigeria, The Philippines, Puerto Rico, Rwanda, South Africa, Sri Lanka, Tanzania, Tunisia, Thailand, Uganda |
Open access |
|
|
GeoData, University of Southampton and based on the UK Office for National Statistics (ONS) 2011 Census and Ordnance Survey OpenData. |
Nighttime population (population counted at place of domicile) |
Postcode headcounts are redistributed over a grid based on OS OpenData Vector Map District buildings dataset. Building polygons have been filtered to remove non-residential areas using unpopulated postcode centroids. Details. |
10 meter |
2011 |
United Kingdom |
Open access (Open Database License) |
|
|
Multiple sources |
Depends on country |
Depends on country |
Depends on country |
Depends on country |
Finland, Netherlands, Norway, Portugal |
Open access |
|
* Dasymetric mapping approaches rely on ancillary data to spatially disaggregate census counts from administrative / census units in an effort to develop higher resolution data products that more faithfully represent population distribution on the ground. The simplest approach is binary dasymetric mapping, which uses one other data layer (such as satellite-derived built-up areas or urban extents) to move populations from census units (which are sometimes large) to areas identified as settlements. Other techniques use a variety of ancillary data, including urban extents, land cover data, and slope, as well as spatial “masks” to exclude populations from protected areas or military reserves, to move populations using statistical weighting algorithms to inform final gridded outputs.
Table 4. Global and Continental Urban Extent / Settlement Layers: Summary Characteristics
|
Dataset |
Source |
Concept |
Method |
Imagery Used |
Spatial Resolution |
Year(s) Represented |
Distribution Policy |
|
Global Human Built-up And Settlement Extent (HBASE) Dataset From Landsat, v1 |
NASA Goddard Space Flight Center (distributed via NASA SEDAC) |
Extent of settlements |
Integrates spatial texture and contextual information to map the spatial extent of an urban area (i.e. HBASE) using Landsat imagery, which is then used as an input to impervious surface mapping (GMIS below). Details. |
Landsat |
30 meter, 250 meter, and 1 km |
2010 |
Open access |
|
European Commission Joint Research Centre (JRC) |
Presence of buildings |
Landsat imagery are used to estimate the proportion of building footprint and impervious surfaces within each grid cell. Details. |
Landsat |
38 m |
1975, 1990, 2000, 2014 |
Open access |
|
|
European Commission Joint Research Centre (JRC) |
Multiple classes of settlement based on combination of population density, population size, and density of built-up areas |
Uses GHS-BUILT built up density and GHS-POP population grid as inputs to create classes (urban center, urban cluster, and rural) derived from combinations of population density, size, and density of built-up. Details. |
Landsat |
1 km |
1975, 1990, 2000, 2015 |
Open access |
|
|
Global Man-made Impervious Surface (GMIS) Dataset From Landsat, v1 |
NASA Goddard Space Flight Center (distributed via NASA SEDAC) |
Percent impervious surface per grid cell |
High resolution satellite imagery are used to calculate the percent man-made impervious surface for each 30m Landsat pixel, masking out non-urban areas using HBASE (above). Details. |
Landsat |
30 meter, 250 meter, and 1 km |
2010 |
Open access |
|
Center for International Earth Science Information Network (CIESIN) - Columbia University, International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT) |
Broader footprints of human settlements |
Thresholded DMSP-OLS nighttime lights plus buffered settlement points. Details. |
Defense Meteorological Program Optical Line Scan (DMSP-OLS) nighttime lights |
15 km |
Circa 1995 |
Open access |
|
|
Global Rural Urban Mapping Project: Urban Extent Polygons, v1.01 |
Center for International Earth Science Information Network (CIESIN) - Columbia University, CUNY Institute for Demographic Research (CIDR), International Food Policy Research Institute (IFPRI), The World Bank, and Centro Internacional de Agricultura Tropical (CIAT) |
Broader footprints of human settlements |
Thresholded DMSP-OLS nighttime lights plus buffered settlement points. The gridded data set was converted to polygons and settlement names were associated with each polygon. Details. |
Defense Meteorological Program Optical Line Scan (DMSP-OLS) nighttime lights |
15 km |
Circa 1995 |
Open access |
|
German Aerospace Center (DLR), Earth Observation Center (EOC) |
Very high resolution built up areas |
A total of 180,000 TerraSAR-X and TanDEM-X radar scenes were processed to create the GUF. Details. |
TanDEM-X |
12 meter |
2011 / 2012 |
Commercial / Free for research use |