在视觉分析中设计模糊性:从风险评估和预测中吸取教训
IEEE transactions on visualization and computer graphics
|October 23, 2023
概括
视觉分析工具需要在风险评估中接受模糊性. 新的设计可以通过鼓励反思和替代解释来更好地支持意义的创造.
科学领域:
- 信息可视化 信息可视化
- 人与计算机的交互
- 风险分析 风险分析
背景情况:
- 在风险评估和预测中,模糊性是常见的.
- 有限的研究存在于设计视觉分析工具的模两可.
研究的目的:
- 调查视觉分析设计和数据表如何支持雪崩预测中的感知.
- 确定视觉分析工具的设计含义,以适应模两可.
主要方法:
- 使用感知过程的概念框架进行定性研究.
- 将新的视觉分析设计与现有工具 (数据表) 的比较.
主要成果:
- 两种系统都产生了类似的分析结果.
- 观察到对模两可的理解和分析行动的差异.
- 确定了"凝结"作为一个关键的,尚未探索的分析行动.
结论:
- 传统的可视化设计指南需要修订.
- 数据接口应该鼓励反思和替代解释.
- 视觉分析工具必须支持风险评估的模两可的性质.
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