Public Understanding of Visual Representations of Uncertainty in Temperature Forecasts
Public Understanding of Visual Representations of Uncertainty in Temperature Forecasts
复制标题
公众对温度预测不确定性的视觉表示的理解
DOI：
10.1177/1555343415591275
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发表时间：
2015
影响因子：
2
通讯作者：
Jan B. F. van Erp
中科院分区：
文献类型：
--
作者：
S. Tak;A. Toet;Jan B. F. van Erp
多日天气预报通常包括不确定性的图形表示。然而，概率事件的视觉表示经常被公众误解。尽管现在正在使用各种不确定性可视化，但决定其成功部署的参数仍然未知。同时，正确理解可能的天气预报结果将使公众能够做出更好的决策，并增加他们对这些预测的信任。我们研究了具有不确定性的温度预测可视化的视觉形式和宽度对温度可能超过给定值的概率估计的影响。结果表明，人们应用了与正态分布非常相似的不确定性分布的内部模型，这证实了之前的发现。此外，可视化类型似乎会影响所应用的内部模型，特别是所描绘的不确定性范围之外的值的概率估计。此外，我们发现感知的不确定性不一定线性映射到视觉特征，因为根据不确定性范围的宽度，对与该范围的相同相对位置的判断有所不同。最后，不确定性分布的内部模型与参与者的计算能力有关。我们为不确定性可视化的制造者或设计者提供了一些启示。
Multiday weather forecasts often include graphical representations of uncertainty. However, visual representations of probabilistic events are often misinterpreted by the general public. Although various uncertainty visualizations are now in use, the parameters that determine their successful deployment are still unknown. At the same time, a correct understanding of possible weather forecast outcomes will enable the public to make better decisions and will increase their trust in these predictions. We investigated the effects of the visual form and width of temperature forecast visualizations with uncertainty on estimates of the probability that the temperature could exceed a given value. The results suggest that people apply an internal model of the uncertainty distribution that closely resembles a normal distribution, which confirms previous findings. Also, the visualization type appears to affect the applied internal model, in particular the probability estimates of values outside the depicted uncertainty range. Furthermore, we find that perceived uncertainty does not necessarily map linearly to visual features, as identical relative positions to the range are being judged differently depending on the width of the uncertainty range. Finally, the internal model of the uncertainty distribution is related to participants’ numeracy. We include some implications for makers or designers of uncertainty visualizations.
影响因子：
3.2
作者：
F. Pappenberger;E. Stephens;J. Thielen;P. Salamon;D. Demeritt;Schalk Jan Andel;F. Wetterhall;L. Alfieri
通讯作者：
F. Pappenberger;E. Stephens;J. Thielen;P. Salamon;D. Demeritt;Schalk Jan Andel;F. Wetterhall;L. Alfieri