Koo Hyeongmo, Chun Yongwan, Griffith Daniel A
School of Economic, Political and Policy Sciences, The University of Texas at Dallas, 800 West Campbell Road, Richardson, Texas 75080-3021, USA.
Associate Professor, School of Economic, Political and Policy Sciences, The University of Texas at Dallas, 800 West Campbell Road, Richardson, Texas 75080-3021, USA.
J Vis Lang Comput. 2018 Feb;44:89-96. doi: 10.1016/j.jvlc.2017.11.007. Epub 2017 Dec 5.
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.
属性不确定性的地理可视化有助于用户识别空间数据的潜在过程。然而，在标准的地理信息系统（GIS）环境中，仍然缺乏不确定性可视化工具。本文通过扩展双变量映射技术，提出了一个属性不确定性可视化的框架。具体而言，该框架基于属性类型，利用两种制图技术：分级统计图法和比例符号法。此框架作为ArcGIS的扩展来实现，其中有三种可视化工具可用：分级统计图上的叠加符号、比例符号图的着色属性以及复合符号。
