PySAL grew out of a collaborative effort between Luc Anselin’s group previously located at the University of Illinois, Champaign-Urbana, and Serge Rey who was at San Diego State University. It was born out of a recognition that the respective projects at the two institutions, PySpace (now GeoDaSpace) and STARS - Space Time Analysis of Regional Systems, could benefit from a shared analytical core, since this would limit code duplication and free up additional developer time to focus on enhancements of the respective applications.
This recognition also came at a time when Python was starting to make major inroads in geographic information systems as represented by projects such as the Python Cartographic Library, Shapely and ESRI’s adoption of Python as a scripting language, among others. At the same time there was a dearth of Python modules for spatial statistics, spatial econometrics, location modeling and other areas of spatial analysis, and the role for PySAL was then expanded beyond its support of STARS and GeoDaSpace to provide a library of core spatial analytical functions that could support the next generation of spatial analysis applications.
In 2008 the home for PySAL moved to the GeoDa Center for Geospatial Analysis and Computation at Arizona State University.
It is important to underscore what PySAL is, and is not, designed to do. First and foremost, PySAL is a library in the fullest sense of the word. Developers looking for a suite of spatial analytical methods that they can incorporate into application development should feel at home using PySAL. Spatial analysts who may be carrying out research projects requiring customized scripting, extensive simulation analysis, or those seeking to advance the state of the art in spatial analysis should also find PySAL to be a useful foundation for their work.
End users looking for a user friendly graphical user interface for spatial analysis should not turn to PySAL directly. Instead, we would direct them to projects like STARS and the GeoDaX suite of software products which wrap PySAL functionality in GUIs. At the same time, we expect that with developments such as the Python based plug-in architectures for QGIS, GRASS, and the toolbox extensions for ArcGIS, that end user access to PySAL functionality will be widening in the near future.
- Rey, Sergio J. and Luc Anselin. (2010) PySAL: A Python Library of Spatial Analytical Methods. In M. Fischer and A. Getis (eds.) Handbook of Applied Spatial Analysis: Software Tools, Methods and Applications. Springer, Berlin. .
- Rey, Sergio J. and Luc Anselin. (2009) PySAL: A Python Library for Spatial Analysis and Geocomputation. (Movie) Python for Scientific Computing. Caltech, Pasadena, CA August 2009.
- Rey, Sergio J. (2009). Show Me the Code: Spatial Analysis and Open Source. Journal of Geographical Systems 11: 191-2007.
- Rey, S.J., Anselin, L., & M. Hwang. (2008). Dynamic Manipulation of Spatial Weights Using Web Services. GeoDa Center Working Paper 2008-12.