2017
DOI: 10.1038/sdata.2017.4
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Abstract: High resolution, contemporary data on human population distributions, their characteristics and changes over time are a prerequisite for the accurate measurement of the impacts of population growth, for monitoring changes and for planning interventions. WorldPop aims to meet these needs through the provision of detailed and open access spatial demographic datasets built using transparent approaches. The Scientific Data WorldPop collection brings together descriptor papers on these datasets and is introduced he… Show more

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Cited by 573 publications
(383 citation statements)
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“…Out of these, mean annual rainfall 25 is the major factor limiting woody cover (32%). It is followed by terrain (elevation, 23%) and human population density 26 is ranked third (13%), shortly before soil 27 (sand fraction, 12%) and interannual rainfall variability (12%). Distance to villages (6%) and fire frequency (2%) have a rather low relative weight.…”
Section: Resultsmentioning
confidence: 99%
“…Out of these, mean annual rainfall 25 is the major factor limiting woody cover (32%). It is followed by terrain (elevation, 23%) and human population density 26 is ranked third (13%), shortly before soil 27 (sand fraction, 12%) and interannual rainfall variability (12%). Distance to villages (6%) and fire frequency (2%) have a rather low relative weight.…”
Section: Resultsmentioning
confidence: 99%
“…These covariates were used for both the machine learning models and the polygon-level models. Raster surfaces of population for the years 2005, 2010 and 2015, were created using data from WorldPop (Tatem, 2017) and from GPWv4 (NASA, 2018) where WorldPop did not have values. Population rasters for the remaining years were created by linear interpolation.…”
Section: Methodsmentioning
confidence: 99%
“…Existing historical data are limited to times since medium and fine resolution satellite remote sensing technology became fully operational (i.e., since the 1970s) to produce land cover databases such as the National Land Cover Database (NLCD) 1–3 , population grids such as the Gridded Population of the World (GPW) 4,5 and its accompanying Global Rural-Urban Mapping Project (GRUMP) 6 , the WorldPop population distribution datasets 7,8 , the Global Urban Footprint (GUF) 9 and the LandScan population datasets 10,11 or human settlement layers such as the Global Human Settlement Layer (GHSL) 12,13 . Besides the limited temporal coverage, such data products suffer from low classification accuracy levels in rural and peri-urban settings 14 thus missing relevant settlement activity.…”
Section: Background and Summarymentioning
confidence: 99%