Dataset Information

Predictive mapping of the global power system using open data.

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.

SUBMITTER: Arderne C

PROVIDER: S-EPMC6962213 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Predictive mapping of the global power system using open data.

Arderne C C Zorn C C Nicolas C C Koks E E EE

Scientific data 20200115 1

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 composit ...[more]

PMID: 31941897

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Project description:ObjectivesDespite increases in global health actors and funding levels, health inequities persist. We empirically tested whether global health governance (GHG) operates under the rational actor model (RAM) and characterised GHG power dynamics.DesignWe collected approximately 75 000 tweets of 20 key global health actors, between 2016 and 2020, using Twitter API. We generated priorities from tweets collected using topic modelling. Priorities from tweets were compared with stated priorities from content analyses of policy documents and with revealed priorities from network analyses of development assistance for health funding data. Comparing priorities derived from Twitter, policy documents and funding data, we can test whether GHG operates under RAM and characterise power dynamics in GHG.Participants20 global health actors were identified based on a consensus of three peer-reviewed articles mapping global health networks. All tweets of each actor were collected in 3-month intervals from November 2016 to May 2020. Policy documents and developmental assistance for health (DAH) financial data for each actor were collected for the same period.ResultsWe find all 20 actors and the global health system collectively fulfil the three conditions of RAM based on stated and revealed priorities. We also find compulsory and institutional power asymmetries in GHG. Funding organisations have compulsory power over channels of DAH and implementing institutions they directly fund. Funding organisations also have transitive influence over implementing institutions receiving DAH funding.ConclusionsWe find that there is a correlation between the priorities of large funders and the priorities of health actors. This correlation in conjunction with GHG operating under the RAM and the asymmetric power held by funders raises issues. GHG under the RAM grants large funders majority of the power to determine global health priorities and ultimately influencing outcomes while implementing organisations, especially those that work closest with populations, have little to limited influence in priority-setting.

Dataset Information

Predictive mapping of the global power system using open data.

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Predictive mapping of the global power system using open data.

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