区间和比率变量属性不确定性的地理可视化:矢量数据的框架与实现

Hyeongmo Koo , Yongwan Chun , Daniel A. Griffith
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引用次数: 8

摘要

属性不确定性的地理可视化有助于用户识别空间数据的底层过程。然而,在标准的GIS环境中,仍然缺乏不确定性可视化工具的可用性。本文提出了一种扩展二元映射技术的属性不确定性可视化框架。具体而言,该框架利用了两种制图技术,即基于属性类型的线面映射和比例符号映射。这个框架是作为ArcGIS的扩展实现的,其中有三种类型的可视化工具可用:覆盖在choropleth地图上的符号,比例符号地图的着色属性和复合符号。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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.

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来源期刊
Journal of Visual Languages and Computing
Journal of Visual Languages and Computing 工程技术-计算机:软件工程
CiteScore
1.62
自引率
0.00%
发文量
0
审稿时长
26.8 weeks
期刊介绍: The Journal of Visual Languages and Computing is a forum for researchers, practitioners, and developers to exchange ideas and results for the advancement of visual languages and its implication to the art of computing. The journal publishes research papers, state-of-the-art surveys, and review articles in all aspects of visual languages.
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