Parallel optimal choropleth map classification in PySAL

Sergio J. Rey, Luc Anselin, Robert Pahle, Xing Kang, Philip Stephens

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


In this article, we report on our experiences with refactoring a spatial analysis library to support parallelization. Python Spatial Analysis Library (PySAL) is a library of spatial analytical functions written in the open-source language, Python. As part of a larger scale effort toward developing cyberinfrastructure of GIScience, we examine the particular case of choropleth map classification through alternative parallel implementations of the Fisher-Jenks optimal classification method using a multi-core, single desktop environment. The implementations rely on three different parallel Python libraries, PyOpenCL, Parallel Python, (PP) and Multiprocessing. Our results point to the dominance of the CPU-based Parallel Python and Multiprocessing implementations over the Graphical Processing Unit (GPU)-based PyOpenCL approach.

Original languageEnglish (US)
Pages (from-to)1023-1039
Number of pages17
JournalInternational Journal of Geographical Information Science
Issue number5
StatePublished - May 2013


  • PySAL
  • parallelization
  • spatial analysis

ASJC Scopus subject areas

  • Information Systems
  • Geography, Planning and Development
  • Library and Information Sciences


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