ArticleScientific data2020
Predictive mapping of the global power system using open data.
Article in Scientific data, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Article
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- Ocean renewable energy for equitable energy access in a Blue Economy.Scientific reports · 2026Article
- Harmonized geospatial data to evaluate the Electric Distribution Networks in the US Northeast.Scientific data · 2025Article
- Globally interconnected solar-wind system addresses future electricity demands.Nature communications · 2025Article
- Mapping 10-m Industrial Lands across 1000+ Global Large Cities, 2017-2023.Scientific data · 2025Article
- Efficient Vertical Structure Correlation and Power Line Inference.Sensors (Basel, Switzerland) · 2024Article
- Geospatial mapping of distribution grid with machine learning and publicly-accessible multi-modal data.Nature communications · 2023Article
- Machine learning-based spatial data development for optimizing astronomical observatory sites in Indonesia.PloS one · 2023Article
- A GIS-based interactive map enabling data-driven decision-making in Nigeria's food supply chain.MethodsX · 2023Article
- Fragmentation of outage clusters during the recovery of power distribution grids.Nature communications · 2022Article
- An all-Africa dataset of energy model "supply regions" for solar photovoltaic and wind power.Scientific data · 2022Article
- Article
- Population cluster data to assess the urban-rural split and electrification in Sub-Saharan Africa.Scientific data · 2021Article
- Smart Characterization of Rogowski Coils by Using a Synthetized Signal.Sensors (Basel, Switzerland) · 2020Article
- Predictive mapping of the global power system using open data.Scientific data · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.