An Alternative Classification Scheme for Uncertain Attribute Mapping

Ran Wei, Anthony Grubesic

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

The reality of uncertain data cannot be ignored. Anytime that spatial data are used to assist planning, decision making, or policy generation, it is likely that error or uncertainty in the data will propagate through processing protocols and analytic techniques, potentially leading to biased or incorrect decision making. The ability to directly account for uncertainty in spatial analysis efforts is critically important. This article focuses on addressing data uncertainty in one of the most important and widely used exploratory spatial data analysis (ESDA) techniques—choropleth mapping—and proposes an alternative map classification method for uncertain spatial data. The classification approach maximizes within-class homogeneity under data uncertainty while explicitly integrating spatial characteristics to reduce visual map complexity and to facilitate pattern perception. The method is demonstrated by mapping the 2009 to 2013 American Community Survey estimates of median household income in Salt Lake County, Utah, at the census tract level.

Original languageEnglish (US)
Pages (from-to)1-12
Number of pages12
JournalProfessional Geographer
DOIs
StateAccepted/In press - Mar 31 2017

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Earth-Surface Processes

Fingerprint Dive into the research topics of 'An Alternative Classification Scheme for Uncertain Attribute Mapping'. Together they form a unique fingerprint.

  • Cite this