Geovisualizing attribute uncertainty of interval and ratio variables: A framework and an implementation for vector data

Hyeongmo Koo, Yongwan Chun, Daniel A. Griffith

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Geovisualization of attribute uncertainty helps users to recognize underlying processes of spatial data. However, it still lacks an availability of uncertainty visualization tools in a standard GIS environment. This paper proposes a framework for attribute uncertainty visualization by extending bivariate mapping techniques. Specifically, this framework utilizes two cartographic techniques, choropleth mapping and proportional symbol mapping based on the types of attributes. This framework is implemented as an extension of ArcGIS in which three types of visualization tools are available: overlaid symbols on a choropleth map, coloring properties to a proportional symbol map, and composite symbols.

Original languageEnglish
Pages (from-to)89-96
Number of pages8
JournalJournal of Visual Languages and Computing
Volume44
DOIs
StatePublished - Feb 2018

Keywords

  • Bivariate mapping
  • GIS
  • Geovisualization
  • Uncertainty

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