Improved parallel optimal choropleth map classification

Jason Laura, Sergio J. Rey

Research output: Chapter in Book/Report/Conference proceedingChapter

3 Scopus citations

Abstract

In this chapter we introduce an improved parallel optimal choropleth map classification algorithm to support spatial analysis. This work contributes to the development of a Distributed Geospatial CyberInfrastructure and offers an implementation of the Fisher-Jenks optimal classification method suitable for multi-core desktop environments. We provide a description of both a single-core vectorized implementation and a parallelized implementation. Our results show that single core vectorization alone provides computational speedups compared to previous parallel implementations and that a combined, parallel and vectorized, implementation offers significant speed improvements.

Original languageEnglish (US)
Title of host publicationModern accelerator technologies for geographic information science
PublisherSpringer US
Pages197-212
Number of pages16
Volume9781461487456
ISBN (Print)9781461487456, 1461487447, 9781461487449
DOIs
StatePublished - Aug 1 2013

Keywords

  • Parallelization •
  • PySAL
  • Spatial analysis •
  • Vectorization •

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Laura, J., & Rey, S. J. (2013). Improved parallel optimal choropleth map classification. In Modern accelerator technologies for geographic information science (Vol. 9781461487456, pp. 197-212). Springer US. https://doi.org/10.1007/978-1-4614-8745-6-15