数据从采集到使用的过程不可避免地会引入不确定性.与数据错误和数据矛盾不同的是,不确定性存在于数据处理的各个环节,甚至各个环节会导致新的不确定性.作为数据不可或缺的组成部分,可视化不确定性有助于用户更加准确地分析和理解数据并做出正确的决策.文中简略地介绍了不确定性的基本来源;从不确定性的可视表达形式角度出发,梳理并总结了当前主流的不确定性可视化方法,包括图标法、可视变量编码法、几何体表达法和动画表达法;并探讨了需要进一步探索的研究方向.
It is inevitable to bring about uncertainty during the process of data acquisition, derivation, and visualization. Different from data error and data conflict, uncertainty resides in and propagates along each stage of the visualization pipeline. Uncertainty is an important ingredient of data. Visualizing uncertainty can help the analysts gain insight into the data to make better decisions. In this paper, first we introduce the main sources of uncertainty then we summarize the state-of-the-art uncertainty visualization approaches into four categories, glyph, visual variable encoding, geometry, and animation at last we outline some directions requiring further studies.