Geovisualizing attribute uncertainty of interval and ratio variables: a framework and an implementation for vector...

Area of Science:

• Geographic Information Science
• Cartography
• Data Visualization

Background:

• Effective geovisualization of attribute uncertainty is crucial for understanding spatial data processes.
• Standard Geographic Information System (GIS) environments currently lack sufficient uncertainty visualization tools.

Purpose of the Study:

• To propose a novel framework for visualizing attribute uncertainty.
• To extend existing bivariate mapping techniques for enhanced geovisualization.

Main Methods:

• The framework extends bivariate mapping techniques.
• It integrates choropleth mapping and proportional symbol mapping based on attribute types.
• Implementation as an ArcGIS extension provides three visualization tools: overlaid symbols, proportional symbol coloring, and composite symbols.

Main Results:

• The developed framework offers practical tools for attribute uncertainty visualization within GIS.
• The integration of different cartographic techniques addresses varying attribute types.
• Users gain improved recognition of underlying spatial data processes through enhanced visualization.

Conclusions:

• The proposed framework effectively addresses the gap in GIS uncertainty visualization tools.
• Extending bivariate mapping provides a flexible and powerful approach to geovisualizing attribute uncertainty.
• This contributes to more accurate interpretation and analysis of spatial data.