Under the mantra: ‘Make use of colorblind friendly graphs’

IF 1.5 3区 环境科学与生态学 Q4 ENVIRONMENTAL SCIENCES Environmetrics Pub Date : 2024-08-20 DOI:10.1002/env.2877
Duccio Rocchini, Ludovico Chieffallo, Elisa Thouverai, Rossella D'Introno, Francesca Dagostin, Emma Donini, Giles Foody, Simon Garnier, Guilherme G. Mazzochini, Vitezslav Moudry, Bob Rudis, Petra Simova, Michele Torresani, Jakub Nowosad
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Abstract

Colorblindness is a genetic condition that affects a person's ability to accurately perceive colors. Several papers still exist making use of rainbow colors palette to show output. In such cases, for colorblind people such graphs are meaningless. In this paper, we propose good practices and coding solutions developed in the R Free and Open Source Software to (i) simulate colorblindness, (ii) develop colorblind friendly color palettes and (iii) provide the tools for converting a noncolorblind friendly graph into a new image with improved colors.

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以 "使用色盲友好图表 "为口号
色盲是一种遗传病,会影响人准确感知颜色的能力。目前仍有一些论文使用彩虹色调色板来显示输出结果。在这种情况下,对于色盲者来说,这些图表毫无意义。在本文中,我们提出了在 R 免费开源软件中开发的良好实践和编码解决方案,以便:(i) 模拟色盲;(ii) 开发色盲友好型调色板;(iii) 提供工具,将非色盲友好型图形转换为具有改进色彩的新图像。
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来源期刊
Environmetrics
Environmetrics 环境科学-环境科学
CiteScore
2.90
自引率
17.60%
发文量
67
审稿时长
18-36 weeks
期刊介绍: Environmetrics, the official journal of The International Environmetrics Society (TIES), an Association of the International Statistical Institute, is devoted to the dissemination of high-quality quantitative research in the environmental sciences. The journal welcomes pertinent and innovative submissions from quantitative disciplines developing new statistical and mathematical techniques, methods, and theories that solve modern environmental problems. Articles must proffer substantive, new statistical or mathematical advances to answer important scientific questions in the environmental sciences, or must develop novel or enhanced statistical methodology with clear applications to environmental science. New methods should be illustrated with recent environmental data.
期刊最新文献
Issue Information A hierarchical constrained density regression model for predicting cluster‐level dose‐response Under the mantra: ‘Make use of colorblind friendly graphs’ A flexible and interpretable spatial covariance model for data on graphs How to find the best sampling design: A new measure of spatial balance
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