The Swiss Impact & Prosperity Initiative (SIPI) is reimagining success for Switzerland. It goes beyond GDP and traditional business metrics, aligning economic vitality with societal well-being, ecological integrity, and long-term resilience. SIPI's vision is a Switzerland where prosperity is measured not only by financial growth, but by the resilience of communities, the health of ecosystems, and the inclusive opportunities that drive long-term value creation.
In this repository the code for analysis of existing beyond-GDP metrics is developed. The goal is to extract a small number of highly expressive, mutually minimally correlated metrics.
- Clone the github repository to your system.
- If you have not already done so, download and install Docker (https://www.docker.com/get-started/). If your working under MacOS or Windows, I recommend to use Docker Desktop.
- Run docker (e.g. by launching docker desktop)
- In your command line/terminal, check that docker runs properly by executing "docker version". You should see both Client and Server versions.
- Cd into the project repository "sipi-data-analysis"
- Build the docker image by running
docker build -t sipi-data-analysis .. You should now see how the system and python packages are being installed.
The dockerfile exposes jupyter lab to the port 8888. Therefore, you can launch juypter lab by running
docker run -p 8888:8888 -v $(pwd):/analysis_app sipi-data-analysis
from inside the sipi-data-analysis project folder. In your command line/terminal you should now see jupyter running. In order to open jupyter lab in your browser just copy-paste the URL including the access token to a new browser tab.
REMARK: Make sure you include the -v $(pwd):/analysis_app flag as otherwise any modifcations made in the jupyter notebook won't be visible from outside the docker container. As a result, you won't be able to easily use Git to track your edits. Including this flag will mounts the current host directory (i.e. "$(pwd)") to the container path "/analysis_app", so all the changes inside the Docker container will be saved directly to your real local file system.
In order to simply run the pipeline as a user, follow these steps:
- Start Docker + Jupyter (see instructions above).
- Open the main jupyter notebooks (i.e. monet_data_analysis.ipynb or wise_data_analysis.ipynb)
- Make sure to set all the parameters governing (over)writing files as per your requirements. See help/docstrings for all the functions and classes if needed.
- Run all cells.
- Verify outputs.
- In your command line, run
docker psto list all running docker containers. Copy the hash value in the NAME column of the container you want to stop. - Execute
docker stop [hash-value]in your command line.
For information on how to augment, extend and build on the existing code base, please read chapter 5 of the PDF document "Documentation_SIPI_Data_Analysis_For_EPS.pdf".
- Fork the repository.
- Switch to the dev branch
git checkout dev. - Create a new branch:
git checkout -b feature/my-feature. - Commit changes and push:
git push origin feature/my-feature. - Open a Pull Request (PR) to
dev. - At least one reviewer must approve before merging.
Please keep dev stable — all work should go through PRs. Do NOT interact with the main branch at any time.
We do not have ownership of the raw data used in this project. The WISE data is publically accessible here and credits go to Liu, Wang, Behrens, et al. (see Liu, Kedi; Wang, Ranran; Behrens, Paul; Schrijver, Inge; Jansen, Annegeke; Rum, Irlan; et al. (2024). A comprehensive Beyond-GDP database to accelerate wellbeing, inclusion, and sustainability research. figshare. Dataset. DOI). The MONET2030 data from the Swiss Federal Statistical Office can be openly accessed here.