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Estimate electricity access on a raster basis worldwide
Jupyter Notebook Python
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accessestimator
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README.md
backfit_local_costs.ipynb
example_access_rates.ipynb
example_local_costs.ipynb
requirements.txt
setup.py

README.md

access-estimator

This repository contains the code used to estimate electricity access levels as a gridded raster, using night-time lights satellite imagery, population data, urban levels and national and sub-national statistics. Along with gridfinder, it contributes to the forthcoming paper Predictive mapping of the global power system using open data (currently in review), the overall methodology of which is described in the predictive-mapping-global-power repository.

Usage and installation

Please see predictive-mapping-global-power for usage notes. This repository is not intended to be used as a stand-alone.

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