Journal
SCIENTIFIC DATA
Volume 3, Issue -, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/sdata.2016.5
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Funding
- Google [OICB150153]
- Bill & Melinda Gates Foundation [OPP1106427, 1032350, OPP1119467, OPP1106023, OPP1093011]
- Belgian Science Policy [SR/00/304]
- NIH/NIAID [U19AI089674]
- RAPIDD program of the Science and Technology Directorate, Department of Homeland Security
- Fogarty International Center, National Institutes of Health
- Senior Research Fellowship from Wellcome Trust [095066]
- National Science Fund for Distinguished Young Scholars [81525023]
- Ministry of Science and Technology of China [2012 ZX10004-201, 2014BAI13B05]
- NIH [U19 AI51915]
- Harvard Center for Communicable Disease Dynamics [U54 GM088558]
- Natural Science Foundation of China [41430637, 41329001]
- Chinese Ministry of Education [13JJD790008]
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According to UN forecasts, global population will increase to over 8 billion by 2025, with much of this anticipated population growth expected in urban areas. In China, the scale of urbanization has, and continues to be, unprecedented in terms of magnitude and rate of change. Since the late 1970s, the percentage of Chinese living in urban areas increased from similar to 18% to over 50%. To quantify these patterns spatially we use time-invariant or temporally-explicit data, including census data for 1990, 2000, and 2010 in an ensemble prediction model. Resulting multi-temporal, gridded population datasets are unique in terms of granularity and extent, providing fine-scale (similar to 100 m) patterns of population distribution for mainland China. For consistency purposes, the Tibet Autonomous Region, Taiwan, and the islands in the South China Sea were excluded. The statistical model and considerations for temporally comparable maps are described, along with the resulting datasets. Final, mainland China population maps for 1990, 2000, and 2010 are freely available as products from the WorldPop Project website and the WorldPop Dataverse Repository.
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