An evaluation of sampling and full enumeration strategies for Fisher Jenks classification in big data settings

Sergio J. Rey, Philip Stephens, Jason Laura

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

Large data contexts present a number of challenges to optimal choropleth map classifiers. Application of optimal classifiers to a sample of the attribute space is one proposed solution. The properties of alternative sampling-based classification methods are examined through a series of Monte Carlo simulations. The impacts of spatial autocorrelation, number of desired classes, and form of sampling are shown to have significant impacts on the accuracy of map classifications. Tradeoffs between improved speed of the sampling approaches and loss of accuracy are also considered. The results suggest the possibility of guiding the choice of classification scheme as a function of the properties of large data sets.

Original languageEnglish (US)
Pages (from-to)796-810
Number of pages15
JournalTransactions in GIS
Volume21
Issue number4
DOIs
StatePublished - Aug 2017

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

Fingerprint Dive into the research topics of 'An evaluation of sampling and full enumeration strategies for Fisher Jenks classification in big data settings'. Together they form a unique fingerprint.

  • Cite this