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https://github.com/gegen07/spopt
Spatial Optimization
https://github.com/gegen07/spopt
Last synced: 3 months ago
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Spatial Optimization
- Host: GitHub
- URL: https://github.com/gegen07/spopt
- Owner: gegen07
- License: bsd-3-clause
- Fork: true (pysal/spopt)
- Created: 2021-05-19T01:27:24.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-04-11T13:00:57.000Z (7 months ago)
- Last Synced: 2024-05-15T04:34:22.454Z (6 months ago)
- Language: Python
- Homepage: https://pysal.org/spopt/
- Size: 92.9 MB
- Stars: 5
- Watchers: 4
- Forks: 1
- Open Issues: 8
-
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
[](https://github.com/pysal/spopt/actions/workflows/testing.yml)
[](https://codecov.io/gh/pysal/spopt)
[](http://pysal.org/spopt/)
[](https://opensource.org/licenses/BSD-3-Clause)
[](https://github.com/psf/black)
[](https://joss.theoj.org/papers/1413cf2c0cf3c561386949f2e1208563)
[](https://doi.org/10.5281/zenodo.4444156)
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 [](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)
* [`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 the [gitter room](https://gitter.im/pysal/spopt).
## 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)