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.. documentation master file
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`spaghetti`
===========
**SPA**\ tial **G**\ rap\ **H**\ s: n\ **ET**\ works, **T**\ opology, & **I**\ nference
---------------------------------------------------------------------------------------
`Spaghetti` is an open-source Python library for the analysis of network-based
spatial data. Originating from the `network` module in `PySAL (Python Spatial
Analysis Library) <http://pysal.org>`_, it is under active development for the
inclusion of newly proposed methods for building graph-theoretic networks and
the analysis of network events. An installation guide, API reference,
and usage tutorials are provided here through the links above.
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<div class="col-md-6 col-xs-15">
<a
href="https://pysal.org/spaghetti/notebooks/network-segmentation.html"
class="thumbnail"
>
<img
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<div class="caption text-center">
<h6>Network Representations</h6>
</div>
</a>
</div>
<div class="col-sm-6 col-xs-15">
<a
href="https://pysal.org/spaghetti/notebooks/network-spatial-dependence.html"
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<img
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<div class="caption text-center">
<h6>Network Spatial Dependence</h6>
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</a>
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</div>
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</div>
History
-------
`Spaghetti` was
created and has evolved in line with the Python Spatial Analysis Library ecosystem for
the specific purpose of utilizing the functionality of spatial weights in
`libpysal <https://pysal.org/libpysal/>`_ for generating network segment contiguity objects.
The PySAL project was started in the mid-2000s when installation was difficult to maintain.
Due to the non-triviality of relying on dependencies to secondary packages, a conscious
decision was made to limit dependencies and build native PySAL data structures in cases
where at all possible. Therefore, the original `pysal.network` submodule was developed to
address the need for integrating support for network data structures with PySAL weights
data structures, with the target audience being spatial data scientists and anyone
interested in investigating network-centric phenomena within PySAL. Owing to the
co-development of network functionality found within `spaghetti` and the evolution of
the wider PySAL ecosystem, today, the package provides specialized network functionality
that easily integrates with the rest of PySAL. This allows users of `spaghetti`’s network
functionality to access spatial analysis functionality that complements network analysis,
such as spatial statistical tools with `esda` and integration with core components of
`libpysal`: `libpysal.weights` (mentioned above),
`libpysal.cg` (computational geometry and data structures),
`libpysal.io` (input-output), and `libpysal.examples` (built-in example data).
Development
-----------
Development of `spaghetti` is hosted on GitHub_.
Support
-------
All questions, comments, & discussions should happen in a public forum, where possible.
Please start a `discussion <https://github.com/pysal/spaghetti/discussions>`_ for questions, talk to us in `PySAL's Discord channel <https://discord.gg/BxFTEPFFZn>`_, or open an `issue <https://github.com/pysal/spaghetti/issues>`_ if there appears to be a
bug. Private messages and emails will not be answered in a substantive manner.
Citing `spaghetti`
------------------
If you use PySAL-spaghetti in a scientific publication, we would appreciate using the following BibTeX citations::
@article{Gaboardi2021,
doi = {10.21105/joss.02826},
url = {https://doi.org/10.21105/joss.02826},
year = {2021},
publisher = {The Open Journal},
volume = {6},
number = {62},
pages = {2826},
author = {James D. Gaboardi and Sergio Rey and Stefanie Lumnitz},
title = {spaghetti: spatial network analysis in PySAL},
journal = {Journal of Open Source Software}
}
@misc{Gaboardi2018,
author = {Gaboardi, James D. and Laura, Jay and Rey, Sergio and
Wolf, Levi John and Folch, David C. and Kang, Wei and
Stephens, Philip and Schmidt, Charles},
month = {oct},
year = {2018},
title = {pysal/spaghetti},
url = {https://github.com/pysal/spaghetti},
doi = {10.5281/zenodo.1343650},
keywords = {graph-theory,network-analysis,python,spatial-networks,topology}
}
Citing Work
-----------
* **Lovelace, R**. `Open source tools for geographic analysis in transport planning`. Journal of Geographical Systems (2021): 1-32. https://doi.org/10.1007/s10109-020-00342-2.
* **Rey, Sergio J., et al**. `The PySAL Ecosystem: Philosophy and Implementation`. Geographical Analysis (2022): 467-487. https://doi.org/10.1111/gean.12276.
* **Barboza-Salerno, Gia E., and Jacquelyn CA Meshelemiah**. `Gun Violence on Walkable Routes to and from School: Recommendations for Policy and Practice`. Journal of Urban Health (2023): 1-16. https://doi.org/10.1007/s11524-023-00802-2
* **Barboza, Gia, and Jacquelyn Meshelemiah**. `Danger, Students Beware, School Ahead! Gun Violence Exposure Near Schools in Compton, California`. (2023). https://doi.org/10.21203/rs.3.rs-2976516/v1
Funding
-------
This project is/was partially funded through:
.. figure:: _static/images/ardc_logo.png
:target: https://atlantardc.wordpress.com
:width: 150
:align: left
The Atlanta Research Data Center: `A Polygon-Based Approach to Spatial Network Allocation <https://atlantardc.files.wordpress.com/2018/05/ardc-newsletter_2018_2.pdf>`_
.. figure:: _static/images/nsf_logo.png
:target: https://www.nsf.gov/index.jsp
:width: 100
:align: left
National Science Foundation Award #1825768: `National Historical Geographic Information System <https://www.nsf.gov/awardsearch/showAward?AWD_ID=1825768&HistoricalAwards=false>`_
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.. toctree::
:hidden:
:maxdepth: 3
:caption: Contents:
Installation <installation>
Tutorials <tutorials>
API <api>
References <references>
.. _PySAL: https://github.com/pysal/pysal
.. _GitHub: https://github.com/pysal/spaghetti
|
