EDeMOS (Electricity Demand Mapping from Open-Source data)

Workflow steps

1. Create a new environment in Conda

The file edemos_env.yml will allow you to create a new environment in Conda. Running EDeMOS from this environment should avoid any issues with libraries like gdal, h3, h3pandas, and others. To create the environment, run:

conda env create -f edemos_env.yml

You only need to do this the very first time. Thereafter you just need to run:

conda activate edemos_env

before running EDeMOS.

2. Choose country

Set the variable ACTIVE_COUNTRY in config.py.

3. Download data sets

As required for the specific country (see below). Files need to be named and put in folders to match the paths given in the config.py and config_{Country}.py

4. Run data conversion scripts

• read_DHS_hh_to_df.py
• read_DHS_services_to_df.py (select appropriate labels in config file)

5. Adjust census data

In the census data file, the following colummns should be available: HH urban, rural, total, size of HH (urban and rural) and share women

6. Set resolution

This is set in config_{Country}.py. HEX_SIZE=5 for testing, 6 for meaningful results, 7 for best results

7. Run GeoDem.ipynb

To run .ipynb file, you can use jupyter lab, to install it, run:

pip install jupyterlab

In your anaconda prompt, navigate to your working folder. Activate the environment by running:

conda activate edemos_env

Then run

jupyter lab

You can then access the different scripts and run GeoDem.ipynb

Data sets

1. Zambia Census 2022. Zamstats, 2022.
2. Demographic and Health Surveys (DHS). DHS
3. A high-resolution gridded dataset to assess electrification in sub-Saharan Africa ¹.
4. Gridded global Gross Domestic Product and Human Development Index datasets over 1990–2015 ².
5. High-Resolution Electricity Access. set_lightscore_sy_xxxx.tif: Predicted likelihood that a settlement is electrified (0 to 1) ³.
6. Relative Wealth Index (RWI) ⁴.
7. Building footprints ⁵.
8. Energy balance (UN stats)

Cite this work

EDeMOS Zambia ⁶.

Footnotes

1. Falchetta, G., Pachauri, S., Parkinson, S. et al. A high-resolution gridded dataset to assess electrification in sub-Saharan Africa. Sci Data 6, 110 (2019). https://doi.org/10.1038/s41597-019-0122-6. ↩

2. Kummu, M., Taka, M. & Guillaume, J. Gridded global datasets for Gross Domestic Product and Human Development Index over 1990–2015. Sci Data 5, 180004 (2018). https://doi.org/10.1038/sdata.2018.4. ↩

3. Brian Min, Zachary P. O'Keeffe, Babatunde Abidoye, Kwawu Mensan Gaba, Trevor Monroe, Benjamin P. Stewart, Kim Baugh, Bruno Sánchez-Andrade Nuño, “Lost in the Dark: A Survey of Energy Poverty from Space,” Joule (2024), https://doi.org/10.1016/j.joule.2024.05.001. ↩

4. Samapriya Roy, Swetnam, T., & Saah, A. (2025). samapriya/awesome-gee-community-datasets: Community Catalog (3.2.0). Zenodo. https://doi.org/10.5281/zenodo.14757583. ↩

5. Leasure DR, Dooley CA, Bondarenko M, Tatem AJ. 2021. peanutButter: An R package to produce rapid-response gridded population estimates from building footprints, version 1.0.0. WorldPop, University of Southampton. doi: 10.5258/SOTON/WP00717. https://github.com/wpgp/peanutButter ↩

6. Millot, A., Kerekeš, A., Korkovelos, A., Stringer M., and Hawkes A. EDeMOS_Zambia. GitHub repository. Accessed February 10, 2025. https://github.com/ariane-millot/EDeMOS_Zambia. ↩