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

Transactions in GIS
By: , and 

Links

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.

Additional publication details

Publication type Article
Publication Subtype Journal Article
Title An evaluation of sampling and full enumeration strategies for Fisher Jenks classification in big data settings
Series title Transactions in GIS
DOI 10.1111/tgis.12236
Volume 21
Issue 4
Year Published 2017
Language English
Publisher Wiley
Contributing office(s) Astrogeology Science Center
Description 15 p.
First page 796
Last page 810