# spopt: Spatial Optimization

## Regionalization, facility location, and transportation-oriented modeling

spopt is an open-source Python library for solving optimization problems with spatial data. Originating from the region module in PySAL (Python Spatial Analysis Library), it is under active development for the inclusion of newly proposed models and methods for regionalization, facility location, and transportation-oriented solutions.

###### Mexican State Regional Income Clustering

###### Airbnb Spots Clustering in Chicago

###### Grid Clustering

###### Locating First Aid Stations in Toronto

###### Locating Store Sites in San Francisco

###### Backup Coverage and Predefined Locations

## Development

spopt development is hosted on github.

## Get in touch

If you have a question regarding spopt, feel free to open an issue, a new discussion on GitHub, or join a chat on PySAL’s Discord channel.

## Citing spopt

If you use PySAL-spopt in a scientific publication, we would appreciate citations to the following:

    @misc{spopt2021,
      author    = {Feng, Xin, and
                   Gaboardi, James D. and
                   Knaap, Elijah and
                   Rey, Sergio J. and
                   Wei, Ran},
      month     = {jan},
      year      = {2021},
      title     = {pysal/spopt},
      url       = {https://github.com/pysal/spopt},
      doi       = {10.5281/zenodo.4444156},
      keywords  = {python,regionalization,spatial-optimization,location-modeling}
    }

    @article{spopt2022,
      author    = {Feng, Xin and Barcelos, Germano and Gaboardi, James D. and
                  Knaap, Elijah and Wei, Ran and Wolf, Levi J. and
                  Zhao, Qunshan and Rey, Sergio J.},
      year      = {2022},
      title     = {spopt: a python package for solving spatial optimization problems in PySAL},
      journal   = {Journal of Open Source Software},
      publisher = {The Open Journal},
      volume    = {7},
      number    = {74},
      pages     = {3330},
      url       = {https://doi.org/10.21105/joss.03330},
      doi       = {10.21105/joss.03330},
    }

## Funding

This project is/was partially funded through:

National Science Foundation Award #1831615: RIDIR: Scalable Geospatial Analytics for Social Science Research