PySAL

PySAL
Original authorsSergio J. Rey and Luc Anselin
DeveloperPySAL community
ReleaseJuly 2010; 16 years ago (2010-07)
Written inPython
Operating systemCross-platform
TypeSpatial analysis; statistical software
LicenseBSD 3-Clause License
Websitepysal.org

PySAL (Python Spatial Analysis Library) is an open-source Python library and ecosystem for spatial data science. It provides tools for geocomputation, spatial analysis, spatial statistics, spatial econometrics, and geovisualization.[1] The project is distributed as a metapackage and as a set of smaller packages that provide tools for spatial weights, exploratory spatial data analysis, spatial regression, regionalization, point-pattern analysis, and mapping.[1][2]

History

PySAL began in 2005 as a collaboration between Sergio J. Rey and Luc Anselin.[3] Its initial design drew on spatial analysis functionality developed for GeoDa and Space-Time Analysis of Regional Systems (STARS).[4] The library was first formally released in July 2010 as a single Python package.[1]

The project was restructured as a metapackage in 2018. The first 2.x release, PySAL 2.0.0, followed in January 2019 and dropped support for Python 2 in favor of Python 3.[3][1] The reorganization separated the metapackage from packages with more specific responsibilities and made it possible to install individual components independently.[5]

Features

PySAL's packages are grouped into four parts:[2]

  • lib contains core data structures and methods, including libpysal for spatial weights, computational geometry, graph construction, and example datasets.
  • explore contains exploratory and descriptive methods, including esda for exploratory spatial data analysis, giddy for spatial dynamics, pointpats for point-pattern analysis, and packages for inequality, segregation, urban morphology, and spatial networks.
  • model contains statistical and econometric methods, including mgwr for multiscale geographically weighted regression and spreg for spatial regression and econometrics.
  • viz contains map classification and statistical visualization tools, including mapclassify and splot.

The library supports the construction and analysis of spatial weights matrices, measures of spatial autocorrelation such as Moran's I, exploratory spatial and spatiotemporal data analysis, spatial econometrics, regionalization, and geovisualization.[6][7] PySAL is released under the BSD 3-Clause License.[8]

Applications

PySAL's spatial econometrics implementations have been compared with implementations for MATLAB, Stata, and R.[9] A 2012 study incorporated PySAL and other open-source packages into a web-based environment for exploratory spatiotemporal data analysis.[10] The textbook GIS Algorithms introduces PySAL through examples of spatial weights and Moran's I.[11] University courses on geographic data science have used PySAL for mapping and spatial-weights exercises.[12]

PySAL has been used for Local Moran's I and network computations in traffic collision research, [13] for local indicators of spatial association in epidemiological studies,[14] and for point-pattern analysis in microbiome research.[15]

See also

References

  1. ^ a b c d Kang, Wei (2020). "PySAL and Spatial Statistics Libraries". Geographic Information Science & Technology Body of Knowledge. University Consortium for Geographic Information Science. doi:10.22224/gistbok/2020.3.1. Retrieved 13 July 2026.
  2. ^ a b "PySAL: Python Spatial Analysis Library". PySAL. Retrieved 13 July 2026.
  3. ^ a b "About Us". PySAL. Retrieved 13 July 2026.
  4. ^ Rey, Sergio J.; Anselin, Luc (2007). "PySAL: A Python Library of Spatial Analytical Methods". The Review of Regional Studies. 37 (1): 5–27. doi:10.52324/001c.8285.
  5. ^ Rey, Sergio J.; Anselin, Luc; Amaral, Pedro; Arribas-Bel, Dani; Cortes, Renan Xavier; Gaboardi, James David; Kang, Wei; et al. (2022). "The PySAL Ecosystem: Philosophy and Implementation". Geographical Analysis. 54 (3): 467–487. Bibcode:2022GeoAn..54..467R. doi:10.1111/gean.12276.
  6. ^ Rey, Sergio J.; Anselin, Luc (2010). "PySAL: A Python Library of Spatial Analytical Methods". In Fischer, Manfred M.; Getis, Arthur (eds.). Handbook of Applied Spatial Analysis: Software Tools, Methods and Applications. Springer. pp. 175–193. doi:10.1007/978-3-642-03647-7_11. ISBN 978-3-642-03647-7.
  7. ^ Rey, Sergio J.; Anselin, Luc; Li, Xun; Pahle, Robert; Laura, Jason; Li, Wenwen; Koschinsky, Julia (2015). "Open Geospatial Analytics with PySAL". ISPRS International Journal of Geo-Information. 4 (2): 815–836. Bibcode:2015IJGI....4..815R. doi:10.3390/ijgi4020815. hdl:2286/R.I.35714.
  8. ^ "PySAL". GitHub. PySAL. Retrieved 13 July 2026.
  9. ^ Bivand, Roger; Piras, Gianfranco (2015). "Comparing Implementations of Estimation Methods for Spatial Econometrics". Journal of Statistical Software. 63 (18): 1–36. doi:10.18637/jss.v063.i18.
  10. ^ She, B.; Zhu, X.; Xiao, W. (2012). "Building an Integrated Web-Based Environment for Exploratory Spatiotemporal Data Analysis". ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. I-4: 169–174. Bibcode:2012ISPAn..I4..169S. doi:10.5194/isprsannals-I-4-169-2012.
  11. ^ Xiao, Ningchuan (2016). "Appendix B: GDAL/OGR and PySAL". GIS Algorithms: Theory and Applications for Geographic Information Science & Technology. SAGE Publications. pp. 291–302. ISBN 978-1-4462-7433-0. Retrieved 13 July 2026.
  12. ^ Arribas-Bel, Dani (2019). "A course on Geographic Data Science". Journal of Open Source Education. 2 (16): 42. doi:10.21105/jose.00042.
  13. ^ Fan, Yaxin; Zhu, Xinyan; She, Bing; Guo, Wei; Guo, Tao (2018). "Network-constrained spatio-temporal clustering analysis of traffic collisions in Jianghan District of Wuhan, China". PLOS ONE. 13 (4) e0195093. Bibcode:2018PLoSO..1395093F. doi:10.1371/journal.pone.0195093. PMC 5909624. PMID 29672551.
  14. ^ van Eijk, Anna Maria; Sutton, Patrick L.; et al. (2019). "The burden of submicroscopic and asymptomatic malaria in India revealed from epidemiology studies at three varied transmission sites in India". Scientific Reports. 9 (1) 17095. Bibcode:2019NatSR...917095V. doi:10.1038/s41598-019-53386-w. PMC 6863831. PMID 31745160.
  15. ^ Grodner, Benjamin; Shi, Ying; et al. (2024). "Spatial mapping of mobile genetic elements and their bacterial hosts in complex microbiomes". Nature Microbiology. 9 (9): 2262–2277. doi:10.1038/s41564-024-01735-5. PMC 11371653. PMID 38918467.

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