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Updating global urbanization projections under the Shared Socioeconomic Pathways.

Shiyin ChenQingxu HuangRaya MuttarakJiayi FangTao LiuChunyang HeZiwen LiuLei Zhu
Published in: Scientific data (2022)
Urbanization level is an important indicator of socioeconomic development, and projecting its dynamics is fundamental for studies related to global socioeconomic and climate change. This paper aims to update the projections of global urbanization from 2015 to 2100 under the Shared Socioeconomic Pathways by using the logistic fitting model and iteratively identifying reference countries. Based on historical urbanization level database from the World Urbanization Prospects, projected urbanization levels and uncertainties are provided for 204 countries and areas every five years. The 2010-2100 year-by-year projected urbanization levels and uncertainties based on the annual historical data from the World Bank (WB) for 188 of countries and areas are also provided. The projections based on the two datasets were compared and the latter were validated using the historical values of the WB for the years 2010-2018. The updated dataset of urbanization level is relevant for understanding future socioeconomic development, its implications for climate change and policy planning.
Keyphrases
  • climate change
  • public health
  • mental health
  • current status
  • emergency department
  • machine learning
  • working memory
  • big data
  • deep learning
  • data analysis
  • case control