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Scientific maps should reach everyone: The cblindplot R package to let colour blind people visualise spatial patterns

Rocchini, Duccio; Nowosad, Jakub; D’Introno, Rossella; Chieffallo, Ludovico; Bacaro, Giovanni; Gatti, Roberto Cazzolla; Foody, Giles M; Furrer, Reinhard; Gábor, Lukáš; Malavasi, Marco; Marcantonio, Matteo; Marchetto, Elisa; Moudrý, Vítězslav; Ricotta, Carlo; Šímová, Petra; Torresani, Michele; Thouverai, Elisa (2023). Scientific maps should reach everyone: The cblindplot R package to let colour blind people visualise spatial patterns. Ecological Informatics, 76:102045.

Abstract

Maps represent powerful tools to show the spatial variation of a variable in a straightforward manner. A crucial aspect in map rendering for its interpretation by users is the gamut of colours used for displaying data. One part of this problem is linked to the proportion of the human population that is colour blind and, therefore, highly sensitive to colour palette selection. The aim of this paper is to present the cblindplot R package and its founding function - cblind.plot() - which enables colour blind people to just enter an image in a coding workflow, simply set their colour blind deficiency type, and immediately get as output a colour blind friendly plot. We will first describe in detail colour blind problems, and then show a step by step example of the function being proposed. While examples exist to provide colour blind people with proper colour palettes, in such cases (i) the workflow include a separate import of the image and the application of a set of colour ramp palettes and (ii) albeit being well documented, there are many steps to be done before plotting an image with a colour blind friendly ramp palette. The function described in this paper, on the contrary, allows to (i) automatically call the image inside the function without any initial import step and (ii) explicitly refer to the colour blind deficiency type being experienced, to further automatically apply the proper colour ramp palette.

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Mathematics
07 Faculty of Science > Institute for Computational Science
Dewey Decimal Classification:510 Mathematics
Scopus Subject Areas:Life Sciences > Ecology, Evolution, Behavior and Systematics
Physical Sciences > Ecology
Physical Sciences > Modeling and Simulation
Physical Sciences > Ecological Modeling
Physical Sciences > Computer Science Applications
Physical Sciences > Computational Theory and Mathematics
Physical Sciences > Applied Mathematics
Uncontrolled Keywords:Applied Mathematics, Computational Theory and Mathematics, Computer Science Applications, Ecological Modeling, Modeling and Simulation, Ecology, Ecology, Evolution, Behavior and Systematics Colour blindness, Computational ecology, Ecological informatics, Mapping, R, Scientific communication
Language:English
Date:1 September 2023
Deposited On:16 Apr 2023 15:51
Last Modified:29 Dec 2024 02:37
Publisher:Elsevier
ISSN:1574-9541
Additional Information:Data availability : The code and the data shown in this paper are available under GitHub at: https://github.com/ducciorocchini/cblindplot. Appendix A. Supplementary data Acknowledgments: We thank the handling Editor and an anonymous Reviewer for suggestions on a previous version of this manuscript. We are grateful to Prof. Gabor Lövei and Dr. Giovanna Pezzi for a critical assessment of a previous draft of this manuscript. We thank Barbara Guida for having worked on the graphical sketch of Fig. 1. This study has received funding from the project SHOWCASE (SHOWCASing synergies between agriculture, biodiversity and ecosystems services to help farmers capitalising on native biodiversity) within the European Union’s Horizon 2020 Researcher and Innovation Programme under grant agreement No. 862480. DR, MM, VM and PS were partially funded by the Horizon Europe project Earthbridge. DR was also funded by: (i) a research project implemented under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.4 - Call for tender No. 3138 of 16 December 2021, rectified by Decree n.3175 of 18 December 2021 of Italian Ministry of University and Research funded by the European Union – NextGenerationEU. Project code CN\_00000033, Concession Decree No. 1034 of 17 June 2022 adopted by the Italian Ministry of University and Research, CUP J33C22001190001, Project title “National Biodiversity Future Center - NBFC”; (ii) the Horizon Europe project B3. This work was supported by Friuli Venezia Giulia Region Operative Program, Europen Social Fund – 2014/2020 Program, Specific Action n. 53/16: Integrative professional training courses within degree programs. RF is supported by the Swiss National Science Foundation SNSF-175529.GMF is supported by the School of Geography, University of Nottingham.MT is supported by the Autonomous Province of Bolzano. This paper is dedicated to the memory of Prof. Peter Alan Burrough (formerly at Oxford University) and Prof. Peter Fisher (formerly at Leicester University), who significantly changed the Geographical Information Science. Supplementary material: https://ars.els-cdn.com/content/image/1-s2.0-S1574954123000742-mmc1.zip Authors’ contribution statement: D.R., J.N. and E.T.contributed to the development of the algorithms and the coding of the colorblind package. D.R., J.N., R.D.I. and L.C. contributed to the conceptual development of the theoretical background of this paper. All authors contributed to the writing of the manuscript.
OA Status:Hybrid
Publisher DOI:https://doi.org/10.1016/j.ecoinf.2023.102045
Project Information:
  • Funder: European Union
  • Grant ID:
  • Project Title:
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  • Content: Published Version
  • Language: English
  • Licence: Creative Commons: Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)

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