spaghetti¶

SPAtial GrapHs: nETworks, Topology, & Inference¶

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), 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.

Network Representations

Network Spatial Dependence

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 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).

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