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Author (up) Arderne, C.; Zorn, C.; Nicolas, C.; Koks, E.E. url  doi
  Title Predictive mapping of the global power system using open data Type Journal Article
  Year 2020 Publication Scientific Data Abbreviated Journal Sci Data  
  Volume 7 Issue 1 Pages 19  
  Keywords Remote Sensing  
  Abstract Limited data on global power infrastructure makes it difficult to respond to challenges in electricity access and climate change. Although high-voltage data on transmission networks are often available, medium- and low-voltage data are often non-existent or unavailable. This presents a challenge for practitioners working on the electricity access agenda, power sector resilience or climate change adaptation. Using state-of-the-art algorithms in geospatial data analysis, we create a first composite map of the global power system with an open license. We find that 97% of the global population lives within 10 km of a MV line, but with large variations between regions and income levels. We show an accuracy of 75% across our validation set of 14 countries, and we demonstrate the value of these data at both a national and regional level. The results from this study pave the way for improved efforts in electricity modelling and planning and are an important step in tackling the Sustainable Development Goals.  
  Address Institute for Environmental Studies, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2052-4463 ISBN Medium  
  Area Expedition Conference  
  Notes PMID:31941897 Approved no  
  Call Number GFZ @ kyba @ Serial 2816  
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