Algorithm for guessing MV grid network based on NTL. An adaptation of this work from Facebook.
gridfinder uses NASA night time lights imagery to as an indicator of settlements/towns with grid electricity access. Then a minimum spanning tree is calculated for these connect points, using the Dijkstra algorithm and using existing road networks as a cost function.
The algorithm looks as follows in process, guessing the grid network for Uganda:
gridfinder requires the following data sources:
- NASA VIIRS data, monthly and annual composites available here.
- OSM highway data, most easily available using the HOT Export Tool, otherwise BBBike or geofabrik, depending on your needs.
To get to grips with the API and steps in the model, open the Jupyter notebook
example.ipynb. This repository includes the input data needed to do a test run for Burundi, so it should be a matter of openening the notebook and running all cells.
gridfinder requires Python >= 3.5 with the following packages installed:
These additional packages may be necessary depending on your configuration:
And these for using an interactive notebook:
Install with pip
pip install gridfinder
Install from GitHub
Download or clone the repository and install the required packages (preferably in a virtual environment):
git clone https://github.com/carderne/gridfinder.git cd gridfinder pip install -r requirements.txt
You can run
./test.sh in the directory, which will do an entire run through using the test data and confirm whether everything is set up properly. (It will fail if jupyter isn’t installed!)