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https://github.com/pysal/spopt

Spatial Optimization
https://github.com/pysal/spopt

facility-location location-allocation location-modeling python regionalization resource-planning routing spatial-analysis spatial-optimization transportation

Last synced: about 2 months ago
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Spatial Optimization

Host: GitHub
URL: https://github.com/pysal/spopt
Owner: pysal
License: bsd-3-clause
Created: 2019-03-01T18:56:11.000Z (over 5 years ago)
Default Branch: main
Last Pushed: 2024-07-09T00:39:56.000Z (3 months ago)
Last Synced: 2024-07-09T04:33:21.046Z (3 months ago)
Topics: facility-location, location-allocation, location-modeling, python, regionalization, resource-planning, routing, spatial-analysis, spatial-optimization, transportation
Language: Python
Homepage: https://pysal.org/spopt/
Size: 132 MB
Stars: 287
Watchers: 15
Forks: 44
Open Issues: 21
Metadata Files:
- Readme: README.md
- Contributing: .github/CONTRIBUTING.md
- License: LICENSE.txt

Awesome Lists containing this project

README

        






# `spopt`: Spatial Optimization

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

![tag](https://img.shields.io/github/v/release/pysal/spopt?include_prereleases&sort=semver)

[![Continuous Integration](https://github.com/pysal/spopt/actions/workflows/testing.yml/badge.svg)](https://github.com/pysal/spopt/actions/workflows/testing.yml)

[![codecov](https://codecov.io/gh/pysal/spopt/branch/main/graph/badge.svg)](https://codecov.io/gh/pysal/spopt)

[![Documentation](https://img.shields.io/static/v1.svg?label=docs&message=current&color=9cf)](http://pysal.org/spopt/)

[![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause)

[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)

[![status](https://joss.theoj.org/papers/1413cf2c0cf3c561386949f2e1208563/status.svg)](https://joss.theoj.org/papers/1413cf2c0cf3c561386949f2e1208563)

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4444156.svg)](https://doi.org/10.5281/zenodo.4444156)

[![Discord](https://img.shields.io/badge/Discord-join%20chat-7289da?style=flat&logo=discord&logoColor=cccccc&link=https://discord.gg/BxFTEPFFZn)](https://discord.gg/BxFTEPFFZn)

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)](http://pysal.org), it is under active development for the inclusion of newly proposed models and methods for regionalization, facility location, and transportation-oriented solutions. 

### Regionalization

```python

import spopt, libpysal, geopandas, numpy

mexico = geopandas.read_file(libpysal.examples.get_path("mexicojoin.shp"))

mexico["count"] = 1

attrs = [f"PCGDP{year}" for year in range(1950, 2010, 10)]

w = libpysal.weights.Queen.from_dataframe(mexico)

mexico["count"], threshold_name, threshold, top_n = 1, "count", 4, 2

numpy.random.seed(123456)

model = spopt.region.MaxPHeuristic(mexico, w, attrs, threshold_name, threshold, top_n)

model.solve()

mexico["maxp_new"] = model.labels_

mexico.plot(column="maxp_new", categorical=True, figsize=(12,8), ec="w");

```







### Locate

```python

from spopt.locate import MCLP

from spopt.locate.util import simulated_geo_points

import numpy, geopandas, pulp, spaghetti

solver = pulp.PULP_CBC_CMD(msg=False, warmStart=True)

lattice = spaghetti.regular_lattice((0, 0, 10, 10), 9, exterior=True)

ntw = spaghetti.Network(in_data=lattice)

street = spaghetti.element_as_gdf(ntw, arcs=True)

street_buffered = geopandas.GeoDataFrame(

    geopandas.GeoSeries(street["geometry"].buffer(0.5).unary_union),

    crs=street.crs,

    columns=["geometry"],

)

client_points = simulated_geo_points(street_buffered, needed=100, seed=5)

ntw.snapobservations(client_points, "clients", attribute=True)

clients_snapped = spaghetti.element_as_gdf(

    ntw, pp_name="clients", snapped=True

)

facility_points = simulated_geo_points(street_buffered, needed=10, seed=6)

ntw.snapobservations(facility_points, "facilities", attribute=True)

facilities_snapped = spaghetti.element_as_gdf(

    ntw, pp_name="facilities", snapped=True

)

cost_matrix = ntw.allneighbordistances(

    sourcepattern=ntw.pointpatterns["clients"],

    destpattern=ntw.pointpatterns["facilities"],

)

numpy.random.seed(0)

ai = numpy.random.randint(1, 12, 100)

mclp_from_cost_matrix = MCLP.from_cost_matrix(cost_matrix, ai, 4, p_facilities=4)

mclp_from_cost_matrix = mclp_from_cost_matrix.solve(solver)

```

*see [notebook](https://github.com/pysal/spopt/blob/main/notebooks/mclp.ipynb) for plotting code*







## Examples

More examples can be found in the [Tutorials](https://pysal.org/spopt/tutorials.html) section of the documentation.

- [Max-p-regions problem](https://pysal.org/spopt/notebooks/maxp.html)

- [Skater](https://pysal.org/spopt/notebooks/skater.html)

- [Region K means](https://pysal.org/spopt/notebooks/reg-k-means.html)

- [Facility Location Real World Problem](https://pysal.org/spopt/notebooks/facloc-real-world.html)

All examples can be run interactively by launching this repository as a [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/pysal/spopt/main).

## Requirements

- [scipy](http://scipy.github.io/devdocs/)

- [numpy](https://numpy.org/devdocs/)

- [pandas](https://pandas.pydata.org/docs/)

- [networkx](https://networkx.org/)

- [libpysal](https://pysal.org/libpysal/)

- [scikit-learn](https://scikit-learn.org/stable/)

- [geopandas](https://geopandas.org/)

- [pulp](https://coin-or.github.io/pulp/)

- [shapely](https://shapely.readthedocs.io/en/stable/)

- [spaghetti](https://github.com/pysal/spaghetti)

## Installation

spopt is available on the [Python Package Index](https://pypi.org/). Therefore, you can either install directly with pip from the command line:

```

$ pip install -U spopt

```

or download the source distribution (.tar.gz) and decompress it to your selected destination. Open a command shell and navigate to the decompressed folder. Type:

```

$ pip install .

```

You may also install the latest stable spopt via conda-forge channel by running:

```

$ conda install --channel conda-forge spopt

```

## Related packages

* Region

* Locate

  * [`allagash`](https://github.com/apulverizer/allagash)

  * [`maximum-coverage-location`](https://github.com/cyang-kth/maximum-coverage-location)

  * [`OR_classic_problem`](https://github.com/khamechian1987/OR_classic_problem)

  * [`p-center`](https://github.com/antoniomedrano/p-center)

  * [`pyspatialopt`](https://github.com/apulverizer/pyspatialopt)

## Contribute

PySAL-spopt is under active development and contributors are welcome.

If you have any suggestions, feature requests, or bug reports, please open new [issues](https://github.com/pysal/spopt/issues) on GitHub. To submit patches, please review [PySAL's documentation for developers](https://pysal.org/docs/devs/), the PySAL [development guidelines](https://github.com/pysal/pysal/wiki), the `spopt` [contributing guidelines](https://github.com/pysal/spopt/blob/main/.github/CONTRIBUTING.md) before  opening a [pull request](https://github.com/pysal/spopt/pulls). Once your changes get merged, you’ll automatically be added to the [Contributors List](https://github.com/pysal/spopt/graphs/contributors).

## Support

If you are having trouble, please [create an issue](https://github.com/pysal/spopt/issues), [start a discussion](https://github.com/pysal/spopt/discussions), or talk to us in [PySAL's Discord channel](https://discord.gg/BxFTEPFFZn).

## Code of Conduct

As a PySAL-federated project, `spopt` follows the [Code of Conduct](https://github.com/pysal/governance/blob/main/conduct/code_of_conduct.rst) under the [PySAL governance model](https://github.com/pysal/governance).

## License

The project is licensed under the [BSD 3-Clause license](https://github.com/pysal/spopt/blob/main/LICENSE.txt).

## Citation

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

```

@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:

[](https://www.nsf.gov/index.jsp) National Science Foundation Award #1831615: [RIDIR: Scalable Geospatial Analytics for Social Science Research](https://www.nsf.gov/awardsearch/showAward?AWD_ID=1831615)