Algorithm for guessing MV grid location based on NTL
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gridfinder
sample_output
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README.md
example.ipynb
example_simple.ipynb
gridfinder-animated.gif
requirements.txt
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README.md

gridfinder

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 Djikstra algorithm and using existing road networks as a cost function.

The algorithm looks as follows in process, guessing the grid network for Uganda:

Animated algorithm

Input requirements

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.

Model usage

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.

Installation

Requirements

gridfinder requires Python >= 3.5 with the following packages installed:

  • numpy >=1.2.0
  • scikit-image >=0.14.1
  • rasterio >=1.0.13
  • geopandas >=0.4.0

These additional packages may be necessary depending on your configuration:

  • Rtree >= 0.8.3
  • affine >= 2.2.1
  • descartes
  • Pillow >= 5.3.0
  • pyproj >= 1.9.5.1
  • pytz >= 2018.7

And these for using an interactive notebook:

  • IPython
  • jupyter
  • matplotlib
  • seaborn

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 make test in the directory, which will do an entire run through using the test data and confirm whether everything is set up properly.