python Clustering of Lines And RAsters: a tool to cluster high-resolution spatial data (rasters or polylines connecting Voronoi polygons) into contiguous, homogeneous regions.
- Clustering of one or multiple high-resolution rasters, such as wind resource maps or load density maps
- Supported aggregation functions: average, sum, or density
- Combination of k-means and max-p algorithms, to ensure contiguity
- Clustering of grid data using a hierarchical algorithm
- Flexibility in the number of polygons obtained
This code is useful if:
- You want to obtain regions for energy system models with homogeneous characteristics (e.g. similar wind potential)
- You want to cluster regions based on several characteristics simultaneously
- You want to take into account grid restrictions when defining regions for power system modeling
- Siala, Kais; Mahfouz, Mohammad Youssef: Impact of the choice of regions on energy system models. Energy Strategy Reviews 25, 2019, 75-85.
Thanks goes to these wonderful people (emoji key):
 kais-siala 💻 📖 💡 🤔 🚧 👀 📢 |
 HoussameH 💻 📖 |
 Waleed Sattar Khan 💻 📖 |
 MYMahfouz 💻 🤔 |
 molarana 🎨 |
This project follows the all-contributors specification. Contributions of any kind welcome!
We prefer that you cite the original publication, where the tool was introduced:
- Siala, Kais; Mahfouz, Mohammad Youssef: Impact of the choice of regions on energy system models. Energy Strategy Reviews 25, 2019, 75-85.
If you are using a new feature that was not part of the publication, and would like to cite it:
- Kais Siala, Houssame Houmy, Waleed Sattar Khan, & Mohammad Youssef Mahfouz. (2020, June 1). tum-ens/pyCLARA: python Clustering of Lines And RAsters (Version v1.0.0). Zenodo. http://doi.org/10.5281/zenodo.3872274