Geovisualizing attribute uncertainty of interval and ratio variables: A framework and an implementation for vector data. (February 2018)

• Record Type: Journal Article
• Authors: Koo, Hyeongmo; Chun, Yongwan; Griffith, Daniel A.
• Is Part Of: Journal of visual languages & computing. Volume 44(2018)
• Page Start: 89
• Page End: 96
• Publication Date: 2018-02
• Subjects: Uncertainty -- Geovisualization -- Bivariate mapping -- GIS; Visual programming languages (Computer science) -- Periodicals; Visual programming (Computer science) -- Periodicals; Programming languages (Electronic computers) -- Semantics -- Periodicals; Langages de programmation visuelle -- Périodiques; Programmation visuelle -- Périodiques; Langages de programmation -- Sémantique -- Périodiques; Programming languages (Electronic computers) -- Semantics; Visual programming (Computer science); Visual programming languages (Computer science); Periodicals; Electronic journals; 005
• DOI: 10.1016/j.jvlc.2017.11.007
• Languages: English
• ISSNs: 1045-926X

Abstract

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.