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Data from: Predictive mapping of the global power system using open data

Authors: Arderne, Christopher; NIcolas, Claire; Zorn, Conrad; Koks, Elco E.;

Data from: Predictive mapping of the global power system using open data

Abstract

Three primary global data outputs from the research: grid.gpkg: Vectorized predicted distribution and transmission line network, with existing OpenStreetMap lines tagged in the 'source' column targets.tif: Binary raster showing locations predicted to be connected to distribution grid. lv.tif: Raster of predicted low-voltage infrastructure in kilometres per cell. This data was created with code in the following three repositories: https://github.com/carderne/gridfinder https://github.com/carderne/predictive-mapping-global-power https://github.com/carderne/access-estimator Full steps to reproduce are contained in this file: https://github.com/carderne/predictive-mapping-global-power/blob/master/README.md The data can be visualized at the following location: https://gridfinder.org

Country
Netherlands
Related Organizations
Keywords

power, electricity, infrastructure

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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
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