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A data set of distributed global population and water withdrawal from 1960 to 2020.

Denghua YanXin ZhangTianling QinChenhao LiJianyun ZhangHao WangBaisha WengKun WangShanshan LiuXiangnan LiYuheng YangWeizhi LiZhenyu LvJianwei WangMeng LiShan HeFang LiuWuxia BiTing XuXiaoqing ShiZihao ManCongwu SunMeiyu LiuMengke WangYinghou HuangHaoyu LongYongzhen NiuBatsuren DorjsurenMohammed GedefawYizhe LiZihao TianShizhou MuWenyu WangXiaoxiang Zhou
Published in: Scientific data (2022)
Population and water withdrawal data sets are currently faced with difficulties in collecting, processing and verifying multi-source time series, and the spatial distribution characteristics of long series are also relatively lacking. Time series is the basic guarantee for the accuracy of data sets, and the production of long series spatial distribution is a realistic requirement to expand the application scope of data sets. Through the time-consuming and laborious basic processing work, this research focuses on the population and water intake time series, and interpolates and extends them to specific land uses to ensure the accuracy of the time series and the demand of spatially distributed data sets. This research provides a set of population density and water intensity products from 1960 to 2020 distributed to the administrative units or the corresponding regions. The data set fills the gaps in the multi-year data set for the accuracy of population density and the intensity of water withdrawal.
Keyphrases
  • electronic health record
  • big data
  • climate change
  • body mass index
  • high intensity
  • physical activity
  • weight loss
  • deep learning