Bibliography
Allaire, J., and Dervieux, C. (2024), quarto: R interface to quarto markdown publishing
system. https://doi.org/10.32614/CRAN.package.quarto.
Anscombe, F. J. (1973), “Graphs
in statistical analysis,” The American Statistician,
Taylor & Francis, 27, 17–21.
Arnold, J. B. (2024), ggthemes: Extra
themes, scales and geoms for ’ggplot2’. https://doi.org/10.32614/CRAN.package.ggthemes.
Bartonicek, A., Urbanek, S., and Murrell, P. (2025), “No more, no
less than sum of its parts: Groups, monoids, and the algebra of
graphics, statistics, and interaction,” Journal of
Computational and Graphical Statistics, 34, 1063–1074. https://doi.org/10.1080/10618600.2024.2429708.
Bates, D., Mächler, M., Bolker, B., and Walker, S. (2015),
“Fitting linear mixed-effects models using lme4,” Journal of Statistical
Software, 67, 1–48. https://doi.org/10.18637/jss.v067.i01.
Bedward, M., Eppstein, D., and Menzel, P. (2024), packcircles: Circle packing. https://doi.org/10.32614/CRAN.package.packcircles.
Begg, S. H., Welsh, M. B., and Bratvold, R. B. (2014),
“Uncertainty vs. Variability: What’s the difference and why is it
important?” in SPE hydrocarbon economics and evaluation
symposium. https://doi.org/10.2118/169850-MS.
Belsley, D. A., Kuh, E., and Welsch, R. E. (1980), Regression
diagnostics: Identifying influential data and sources of
collinearity, Wiley series in probability and statistics, New York:
Wiley. https://doi.org/10.1002/0471725153.
Benjamin, D. M., and Budescu, D. V. (2018), “The role of type and
source of uncertainty on the processing of climate models
projections,” Frontiers in Psychology, 9, 1–17. https://doi.org/10.3389/fpsyg.2018.00403.
Bivand, R., Pebesma, E., and Gomez-Rubio, V. (2013), Applied spatial data analysis with
R, second edition, Springer, NY.
Bivand, R., and Rundel, C. (2023), rgeos: Interface to geometry engine - open source
(’GEOS’).
Blenkinsop, S., Fisher, P., Bastin, L., and Wood, J. (2000),
“Evaluating the perception of uncertainty in alternative
visualization strategies,” Cartographica, 37, 1–13. https://doi.org/10.3138/3645-4v22-0m23-3t52.
Boger, T., Most, S. B., and Franconeri, S. L. (2021), “Jurassic
mark: Inattentional blindness for a datasaurus reveals that
visualizations are explored, not seen,” in 2021 IEEE
visualization conference (VIS), IEEE, pp. 71–75.
Bokulich, A., and Parker, W. (2021), “Data models, representation
and adequacy-for-purpose,” European Journal for Philosophy of
Science, Springer, 11, 31.
Bornkamp, B. (2018), “Calculating quantiles of noisy distribution
functions using local linear regressions,” Computational
Statistics, Springer, 33, 487–501. https://doi.org/10.1007/s00180-017-0736-0.
Bostrom, A., Anselin, L., and Farris, J. (2008), “Visualizing
seismic risk and uncertainty: A review of related
research,” in Annals of the New
York Academy of Sciences,
Blackwell Publishing Inc., pp. 29–40. https://doi.org/10.1196/annals.1399.005.
Boukhelifa, N., Bezerianos, A., Isenberg, T., and Fekete, J. D. (2012),
“Evaluating sketchiness as a visual variable for the depiction of
qualitative uncertainty,” IEEE Transactions on Visualization
and Computer Graphics, IEEE, 18, 2769–2778. https://doi.org/10.1109/TVCG.2012.220.
Boukhelifa, N., Perrin, M.-E., Huron, S., and Eagan, J. (2017),
“How data workers cope with uncertainty: A task characterisation
study,” in Proceedings of the 2017 CHI conference on human
factors in computing systems, CHI ’17, New York, NY, USA:
Association for Computing Machinery, pp. 3645–3656. https://doi.org/10.1145/3025453.3025738.
Brennen, A., and Tuerk, S. (2018), “An instrument for evaluating
uncertainty visualization techniques,” Conference on Human
Factors in Computing Systems - Proceedings, 2018-April, 1–6. https://doi.org/10.1145/3170427.3188649.
Buja, A., Cook, D., Hofmann, H., Lawrence, M., Lee, E. K., Swayne, D.
F., and Wickham, H. (2009), “Statistical inference for exploratory
data analysis and model diagnostics,” Philosophical
Transactions of the Royal Society A: Mathematical, Physical and
Engineering Sciences, Royal Society, 367, 4361–4383. https://doi.org/10.1098/rsta.2009.0120.
Bullock, D. S., Boerngen, M., Tao, H., Maxwell, B., Luck, J. D.,
Shiratsuchi, L., Puntel, L., and Martin, N. F. (2019), “The
data-intensive farm management project: Changing agronomic research
through on-farm precision experimentation,” Agronomy
Journal, 111, 2736–2746. https://doi.org/10.2134/agronj2019.03.0165.
Casella, G., and Berger, R. (2024), Statistical inference,
Chapman; Hall/CRC.
Chakraborty, S., Kiefer, P., and Raubal, M. (2024), “The influence
of uncertainty visualization on cognitive load in a safety- and
time-critical decision-making task,” International Journal of
Geographical Information Science, 38, 1583–1610. https://doi.org/10.1080/13658816.2024.2348747.
Chatfield, C. (1985), “The initial examination of data,”
Journal of the Royal Statistical Society. Series A (General),
[Royal Statistical Society, Oxford University Press], 148, 214–253. https://doi.org/10.2307/2981969.
Cheong, L., Bleisch, S., Kealy, A., Tolhurst, K., Wilkening, T., and
Duckham, M. (2016), “Evaluating the impact of visualization of
wildfire hazard upon decision-making under uncertainty,”
International Journal of Geographical Information Science,
Taylor & Francis, 30, 1377–1404. https://doi.org/10.1080/13658816.2015.1131829.
Clarke, E., Sherrill-Mix, S., and Dawson, C. (2025), ggbeeswarm: Categorical scatter (violin point)
plots. https://doi.org/10.32614/CRAN.package.ggbeeswarm.
Cleveland, W. S., and McGill, R. (1984), “Graphical perception:
Theory, experimentation, and application to the development of graphical
methods,” Journal of the American Statistical
Association, 79, 531–554. https://doi.org/10.1080/01621459.1984.10478080.
Cliff, A. D., and Ord, J. K. (1981), Spatial processes: Models &
applications, London: Pion.
Cook, D., Lee, E.-K., and Majumder, M. (2016), “Data visualization
and statistical graphics in big data analysis,” Annual Review
of Statistics and Its Application, 3, 133–159. https://doi.org/10.1146/annurev-statistics-041715-033420.
Cook, D., Reid, N., and Tanaka, E. (2021), “The foundation is
available for thinking about data visualization inferentially,”
Harvard Data Science Review. https://doi.org/10.1162/99608f92.8453435d.
Correll, M., and Gieicher, M. (2015), “Implicit uncertainty
visualization: Aligning perception and statistics,” in
Workshop on visualization for decision making under uncertainty.
Https://api. Semanticscholar. Org/CorpusID.
Correll, M., and Gleicher, M. (2014), “Error bars considered
harmful: Exploring alternate encodings for mean and error,”
IEEE Transactions on Visualization and Computer Graphics, IEEE,
20, 2142–2151. https://doi.org/10.1109/TVCG.2014.2346298.
Correll, M., and Heer, J. (2016), “Surprise! Bayesian weighting
for de-biasing thematic maps,” IEEE transactions on
visualization and computer graphics, IEEE, 23, 651–660.
Correll, M., Moritz, D., and Heer, J. (2018), “Value-suppressing
uncertainty palettes,” Conference on Human Factors in
Computing Systems - Proceedings, 2018-April, 1–11. https://doi.org/10.1145/3173574.3174216.
Crameri, F. (2018), “Geodynamic diagnostics, scientific
visualisation and StagLab 3.0,” Geoscientific
Model Development, 11, 2541–2562. https://doi.org/10.5194/gmd-11-2541-2018.
Crameri, F., Shephard, G. E., and Heron, P. J. (2020), “The misuse
of colour in science communication,” Nature
Communications, 11, 5444. https://doi.org/10.1038/s41467-020-19160-7.
Csárdi, G. (2025), cli: Helpers for
developing command line interfaces. https://doi.org/10.32614/CRAN.package.cli.
Dupin, C. (1826), “Carte figurative de
l’instruction populaire de la france,” Bruxelles: s.n.
Eddelbuettel, D. (2025), digest: Create
compact hash digests of r objects. https://doi.org/10.32614/CRAN.package.digest.
Firke, S. (2024), janitor: Simple tools
for examining and cleaning dirty data. https://doi.org/10.32614/CRAN.package.janitor.
Fischhoff, B., and Davis, A. L. (2014), “Communicating scientific
uncertainty,” Proceedings of the National Academy of Sciences
of the United States of America, 111, 13664–13671. https://doi.org/10.1073/pnas.1317504111.
Fox, J., and Weisberg, S. (2019), An R companion to
applied regression, Thousand Oaks CA: Sage.
Franconeri, S. L. (2021), “Three perceptual tools for seeing and
understanding visualized data,” Current Directions in
Psychological Science, 30, 367–375. https://doi.org/10.1177/09637214211009512.
Frerebeau, N. (2025), khroma: Colour
schemes for scientific data visualization, Pessac, France:
Université Bordeaux Montaigne. https://doi.org/10.5281/zenodo.1472077.
Gentleman, R. C., Carey, V. J., Bates, D. M., and others (2004),
“Bioconductor: Open software development for computational biology
and bioinformatics,” Genome Biology, 5, R80. https://doi.org/10.1186/gb-2004-5-10-r80.
Gobira, M., Freire, V., Tinoco, C., Avelino, G. S., Carricondo, P.,
Dias, A., and Negreiros, M. A. (2025), “Assessing the accuracy of
a digital color vision test,” Archivos de la Sociedad
Española de Oftalmología (English Edition), 100, 781–787. https://doi.org/10.1016/j.oftale.2025.09.008.
Gohel, D., and Skintzos, P. (2024), flextable: Functions for tabular reporting.
https://doi.org/10.32614/CRAN.package.flextable.
Goldstein, D. G., and Rothschild, D. (2014), “Lay understanding of
probability distributions,” Judgment and Decision
Making, 9, 1–14.
Griethe, H., and Schumann, H. (2006), “The visualization of
uncertain data: Methods and problems,” in SimVis, pp.
143–156.
Gschwandtner, T., Bögl, M., Federico, P., and Miksch, S. (2016),
“Visual encodings of temporal uncertainty: A comparative user
study,” IEEE Transactions on Visualization and Computer
Graphics, IEEE Computer Society, 22, 539–548. https://doi.org/10.1109/TVCG.2015.2467752.
Guo, Z., Kale, A., Kay, M., and Hullman, J. (2025),
“VMC: A grammar for visualizing statistical model
checks,” IEEE Transactions on Visualization and Computer
Graphics, IEEE Educational Activities Department, 31, 798–808. https://doi.org/10.1109/TVCG.2024.3456402.
Gustafson, A., and Rice, R. E. (2019), “The effects of uncertainty
frames in three science communication topics,” Science
Communication, 41, 679–706. https://doi.org/10.1177/1075547019870811.
Hadjimichael, A., Schlumberger, J., and Haasnoot, M. (2024), “Data
visualisation for decision making under deep uncertainty: Current
challenges and opportunities,” Environmental Research
Letters, 19, 111011. https://doi.org/10.1088/1748-9326/ad858b.
Haupt, I. A. (1930), “Tests for color-blindness: A survey of the
literature with bibliography to 1928,” The Journal of General
Psychology, 3, 222–267. https://doi.org/10.1080/00221309.1930.9918203.
Heer, J., and Bostock, M. (2010), “Crowdsourcing graphical
perception: Using mechanical turk to assess visualization
design,” in Proceedings of the SIGCHI conference on human
factors in computing systems, CHI ’10, New York, NY, USA:
Association for Computing Machinery, pp. 203–212. https://doi.org/10.1145/1753326.1753357.
Henderson, and Velleman (1981), “Building multiple regression
models interactively,” Biometrics, 37, 391–411.
Henry, L., and Wickham, H. (2026b), rlang: Functions for base types and core r and
’tidyverse’ features. https://doi.org/10.32614/CRAN.package.rlang.
Henry, L., and Wickham, H. (2026a), lifecycle: Manage the life cycle of your package
functions. https://doi.org/10.32614/CRAN.package.lifecycle.
Hester, J., and Bryan, J. (2024), glue:
Interpreted string literals. https://doi.org/10.32614/CRAN.package.glue.
Hofmann, H., Follett, L., Majumder, M., and Cook, D. (2012),
“Graphical tests for power comparison of competing
designs,” IEEE Transactions on Visualization and Computer
Graphics, 18, 2441–2448. https://doi.org/10.1109/TVCG.2012.230.
Hofmann, H., Hare, E., and GGobi Foundation (2026), gglogo: Geom for logo sequence plots.
Huebner, M., Vach, W., and le Cessie, S. (2016), “A systematic
approach to initial data analysis is good research practice,”
The Journal of Thoracic and Cardiovascular Surgery, 151, 25–27.
https://doi.org/10.1016/j.jtcvs.2015.09.085.
Hullman, J. (2016), “Why evaluating uncertainty visualization is
error prone,” ACM International Conference Proceeding
Series, 24-October, 143–151. https://doi.org/10.1145/2993901.2993919.
Hullman, J. (2020), “Why authors don’t visualize
uncertainty,” IEEE Transactions on Visualization and Computer
Graphics, IEEE Computer Society, 26, 130–139. https://doi.org/10.1109/TVCG.2019.2934287.
Hullman, J., and Gelman, A. (2021), “Designing for interactive
exploratory data analysis requires theories of graphical
inference,” Harvard Data Science Review, 1–70. https://doi.org/10.1162/99608f92.3ab8a587.
Hullman, J., Kay, M., Kim, Y. S., and Shrestha, S. (2018),
“Imagining replications: Graphical prediction discrete
visualizations improve recall estimation of effect uncertainty,”
IEEE Transactions on Visualization and Computer Graphics, IEEE,
24, 446–456. https://doi.org/10.1109/TVCG.2017.2743898.
Hullman, J., Qiao, X., Correll, M., Kale, A., and Kay, M. (2019),
“In pursuit of error: A survey of uncertainty visualization
evaluation,” IEEE Transactions on Visualization and Computer
Graphics, IEEE Computer Society, 25, 903–913. https://doi.org/10.1109/TVCG.2018.2864889.
Hullman, J., Resnick, P., and Adar, E. (2015), “Hypothetical
outcome plots outperform error bars and violin plots for inferences
about reliability of variable ordering,” PLoS ONE,
Public Library of Science, 10. https://doi.org/10.1371/journal.pone.0142444.
Hyndman, R. J., and Athanasopoulos, G. (2021), Forecasting: Principles and practice,
3rd edition, Melbourne, Australia: OTexts.
Ibrekk, H., and Morgan, M. G. (1987), “Graphical communication of
uncertain quantities to nontechnical people,” Risk
Analysis, 7, 519–529. https://doi.org/10.1111/j.1539-6924.1987.tb00488.x.
Ihaka, R. (2003), “Colour for
presentation graphics,” Proceedings of the 3rd
International Workshop on Distributed Statistical Computing.
Jung, P. H., Thill, J.-C., and Issel, M. (2019), “Spatial
autocorrelation and data uncertainty in the American
Community Survey: A critique,”
International Journal of Geographical Information Science, 33,
1155–1175. https://doi.org/10.1080/13658816.2018.1554811.
Kale, A., Kay, M., and Hullman, J. (2021), “Visual reasoning
strategies for effect size judgments and decisions,” IEEE
Transactions on Visualization and Computer Graphics, 27, 272–282.
https://doi.org/10.1109/TVCG.2020.3030335.
Kale, A., Nguyen, F., Kay, M., and Hullman, J. (2018),
“Hypothetical outcome plots help untrained observers judge trends
in ambiguous data,” IEEE Transactions on Visualization and
Computer Graphics, IEEE, 25, 892–902.
Kay, M. (2019), “How much value should an uncertainty palette
suppress if an uncertainty palette should suppress value?
Statistical and perceptual perspectives,” OSF
Preprints. https://doi.org/10.31219/osf.io/6xcnw.
Kay, M. (2023), “ggdist:
Visualizations of distributions and uncertainty in the grammar of
graphics,” IEEE Transactions on Visualization and Computer
Graphics, IEEE, 30, 414–424.
Khizer, M. A., Ijaz, U., Khan, T. A., Khan, S., Liaqat, T., Jamal, A.,
Zahid, I., Shah, H. G., and Zahid, M. A. (2022), “Smartphone color
vision testing as an alternative to the conventional
Ishihara booklet,” Cureus, 14, e30747. https://doi.org/10.7759/cureus.30747.
Kim, Y. S., Walls, L. A., Krafft, P., and Hullman, J. (2019), “A
Bayesian cognition approach to improve data
visualization,” Conference on Human Factors in Computing
Systems - Proceedings, 1–14. https://doi.org/10.1145/3290605.3300912.
Kinkeldey, C., MacEachren, A. M., and Schiewe, J. (2014), “How to
assess visual communication of uncertainty? A systematic
review of geospatial uncertainty visualisation user studies,”
Cartographic Journal, 51, 372–386. https://doi.org/10.1179/1743277414Y.0000000099.
Koo, H., Wong, D. W., and Chun, Y. (2019), “Measuring global
spatial autocorrelation with data reliability information,”
The Professional Geographer : the Journal of the Association of
American Geographers, 71, 551–565. https://doi.org/10.1080/00330124.2018.1559652.
Koonchanok, R., Tawde, G. Y., Narayanasamy, G. R., Walimbe, S., and
Reda, K. (2023), “Visual belief elicitation reduces the incidence
of false discovery,” in Proceedings of the 2023 CHI
conference on human factors in computing systems, pp. 1–17.
Kuhnert, P. M., Pagendam, D. E., Bartley, R., Gladish, D. W., Lewis, S.
E., and Bainbridge, Z. T. (2018), “Making management decisions in
the face of uncertainty: A case study using the Burdekin
catchment in the Great Barrier Reef,” Marine and
Freshwater Research, 69, 1187–1200. https://doi.org/10.1071/MF17237.
Kyveryga, P. M. (2019), “On-farm research: Experimental
approaches, analytical frameworks, case studies, and impact,”
Agronomy Journal, 111, 2633–2635. https://doi.org/10.2134/agronj2019.11.0001.
Lee, C., Yang, T., Inchoco, G. D., Jones, G. M., and Satyanarayan, A.
(2021), “Viral visualizations: How coronavirus skeptics use
orthodox data practices to promote unorthodox science online,” in
Proceedings of the 2021 CHI conference on human factors in computing
systems, pp. 1–18.
Lenth, R. V. (2025), emmeans: Estimated
marginal means, aka least-squares means. https://doi.org/10.32614/CRAN.package.emmeans.
Li, W., Cook, D., Tanaka, E., and VanderPlas, S. (2024), “A plot
is worth a thousand tests: Assessing residual diagnostics with the
lineup protocol,” Journal of Computational and Graphical
Statistics, 1–19. https://doi.org/10.1080/10618600.2024.2344612.
Lim, N. J., Brandt, S. A., and Seipel, S. (2016), “Visualisation
and evaluation of flood uncertainties based on ensemble
modelling,” International Journal of Geographical Information
Science, Taylor & Francis, 30, 240–262.
Locke, S., and D’Agostino McGowan, L. (2018), datasauRus: Datasets from the
datasaurus dozen.
Lucchesi, L. R., and Wikle, C. K. (2017), “Visualizing uncertainty
in areal data with bivariate choropleth maps, map pixelation and glyph
rotation,” Stat, Wiley-Blackwell Publishing Ltd, 6,
292–302. https://doi.org/10.1002/sta4.150.
Lucchesi, L., and Kuhnert, P. (2020), Vizumap:
Visualizing uncertainty in spatial data.
Lucchesi, L., Kuhnert, P., and Wikle, C. (2021),
“Vizumap: An R package for visualising
uncertainty in spatial data,” Journal of Open Source
Software, 6, 2409. https://doi.org/10.21105/joss.02409.
Luce, R. D., and Edwards, W. (1958), “The derivation of subjective
scales from just noticeable differences.” Psychological
review, American Psychological Association, 65, 222.
MacEachren, A. M. (1992), “Visualizing uncertain
information,” Cartographic Perspectives, 10–19. https://doi.org/10.14714/CP13.1000.
MacEachren, A. M., Robinson, A., Hopper, S., Gardner, S., Murray, R.,
Gahegan, M., and Hetzler, E. (2005), “Visualizing geospatial
information uncertainty: What we know and what we need to
know,” Cartography and Geographic Information Science,
32, 139–160. https://doi.org/10.1559/1523040054738936.
Maceachren, A. M., Roth, R. E., O’Brien, J., Li, B., Swingley, D., and
Gahegan, M. (2012), “Visual semiotics & uncertainty
visualization: An empirical study,” IEEE Transactions on
Visualization and Computer Graphics, IEEE, 18, 2496–2505. https://doi.org/10.1109/TVCG.2012.279.
Majumder, M., Hofmann, H., and Cook, D. (2013), “Validation of
visual statistical inference, applied to linear models,”
Journal of the American Statistical Association, 108, 942–956.
https://doi.org/10.1080/01621459.2013.808157.
Mann, H. B., and Wald, A. (1943), “On stochastic limit and order
relationships,” The Annals of Mathematical Statistics,
14, 217–226. https://doi.org/10.1214/aoms/1177731415.
Manski, C. F. (2020), “The lure of incredible certitude,”
Economics and Philosophy, 36, 216–245. https://doi.org/10.1017/S0266267119000105.
Mason, H., Cook, D., Goodwin, S., Tanaka, E., and VanderPlas, S.
(2026a), “The noisy
work of uncertainty visualisation research.”
Mason, H., Cook, D., Goodwin, S., and VanderPlas, S. (2026b), ggdibbler: Add uncertainty to data
visualisations. https://doi.org/10.32614/CRAN.package.ggdibbler.
Matejka, J., and Fitzmaurice, G. (2017), “Same stats, different
graphs: Generating datasets with varied appearance and identical
statistics through simulated annealing,” in Proceedings of
the 2017 CHI conference on human factors in computing systems, CHI
’17, New York, NY, USA: Association for Computing Machinery, pp.
1290–1294. https://doi.org/10.1145/3025453.3025912.
McNutt, A., Kindlmann, G., and Correll, M. (2020), “Surfacing
visualization mirages,” in Proceedings of the 2020
CHI Conference on Human
Factors in Computing
Systems, Honolulu HI USA: ACM, pp. 1–16. https://doi.org/10.1145/3313831.3376420.
Meng, X. L. (2014), “A trio of inference problems that could win
you a nobel prize in statistics (if you help fund it),” Past,
Present, and Future of Statistical Science, 537–562. https://doi.org/10.1201/b16720-52.
Meyer, M. A., Broome, F. R., and Jr., R. H. S. (1975), “Color
statistical mapping by the U.S. Bureau of the
Census,” The American Cartographer, Taylor
& Francis, 2, 101–117. https://doi.org/10.1559/152304075784313250.
Moritz, D., Fisher, D., Ding, B., and Wang, C. (2017), “Trust, but
verify: Optimistic visualizations of approximate queries for exploring
big data,” in Proceedings of the 2017 CHI conference on human
factors in computing systems, pp. 2904–2915.
Müller, K., and Wickham, H. (2025), tibble: Simple data frames. https://doi.org/10.32614/CRAN.package.tibble.
Ndlovu, A., Shrestha, H., and Harrison, L. T. (2023), “Taken by
surprise? Evaluating how Bayesian surprise &
suppression influences peoples’ takeaways in map visualizations,”
in 2023 IEEE visualization and visual analytics (VIS), IEEE,
pp. 136–140.
Neuwirth, E. (2022), RColorBrewer:
ColorBrewer palettes.
O’Hara-Wild, M., Kay, M., Hayes, A., and Hyndman, R. (2024), distributional: Vectorised probability
distributions. https://doi.org/10.32614/CRAN.package.distributional.
O’Neill, O. (2018), “Linking trust to trustworthiness,”
International Journal of Philosophical Studies, Routledge, 26,
293–300. https://doi.org/10.1080/09672559.2018.1454637.
Olston, C., and Mackinlay, J. D. (2002), “Visualizing data with
bounded uncertainty,” Proceedings - IEEE Symposium on
Information Visualization, INFO VIS, IEEE, 2002-Janua, 37–40. https://doi.org/10.1109/INFVIS.2002.1173145.
Otsuka, J. (2023), Thinking about atatistics: The philosophical
foundations, New York: Routledge, p. 204. https://doi.org/10.4324/9781003319061.
Padilla, L., Hosseinpour, H., Fygenson, R., Howell, J., Chunara, R., and
Bertini, E. (2022a), “Impact of COVID-19 forecast
visualizations on pandemic risk perceptions,” Scientific
Reports 2022 12:1, Nature Publishing Group, 12, 1–14. https://doi.org/10.1038/s41598-022-05353-1.
Padilla, L., Kay, M., and Hullman, J. (2022b), “Uncertainty
visualization,” in Computational Statistics in
Data Science, eds. W. W. Piegorsch, R. A.
Levine, H. H. Zhang, and T. C. M. Lee, Hoboken, NJ: John Wiley &
Sons, pp. 405–426.
Padilla, L., Powell, M., Kay, M., and Hullman, J. (2021),
“Uncertain about uncertainty: How qualitative expressions of
forecaster confidence impact decision-making with uncertainty
visualizations,” Frontiers in Psychology, Frontiers
Media S.A., 11. https://doi.org/10.3389/fpsyg.2020.579267.
Padilla, L., Ruginski, I., and Creem-Regehr, S. (2017), “Effects
of ensemble and summary displays on interpretations of geospatial
uncertainty data,” Cognitive Research: Principles and
Implications, Springer, 2. https://doi.org/10.1186/s41235-017-0076-1.
Pang, A. T., Wittenbrink, C. M., and Lodha, S. K. (1997),
“Approaches to uncertainty visualization,” Visual
Computer, 13, 370–390. https://doi.org/10.1007/s003710050111.
Pebesma, E. (2018), “Simple Features for
R: Standardized support for spatial vector data,” The
R Journal, 10, 439–446. https://doi.org/10.32614/RJ-2018-009.
Pebesma, E., and Bivand, R. (2023), Spatial data science: With
applications in R, Chapman; Hall/CRC. https://doi.org/10.1201/9780429459016.
Pedersen, T. L. (2024), tidygraph: A
tidy API for graph manipulation. https://doi.org/10.32614/CRAN.package.tidygraph.
Pedersen, T. L. (2025b), patchwork: The
composer of plots. https://doi.org/10.32614/CRAN.package.patchwork.
Pedersen, T. L. (2025a), ggraph: An
implementation of grammar of graphics for graphs and networks. https://doi.org/10.32614/CRAN.package.ggraph.
Peña-Araya, V., Fontaine, C. M., Wei, X., Delpech, G., and Bezerianos,
A. (2025), “Uncertainty in science is malleable. Advocating for
user-agency in defining uncertainty in visualizations: A case study in
geology,” in Proceedings of the 2025 CHI conference on human
factors in computing systems, pp. 1–18.
Pham, B., Streit, A., and Brown, R. (2009), “Visualization of
information uncertainty: Progress and challenges,” in
Advanced information and knowledge processing, Springer-Verlag
London Ltd, pp. 19–48. https://doi.org/10.1007/978-1-84800-269-2_2.
Plutino, A., Armellin, L., Mazzoni, A., Marcucci, R., and Rizzi, A.
(2023), “Aging variations in Ishihara test
plates,” Color Research & Application, 48, 721–734.
https://doi.org/10.1002/col.22877.
Potter, K., Kniss, J., Riesenfeld, R., and Johnson, C. R. (2010),
“Visualizing summary statistics and uncertainty,”
Computer Graphics Forum, 29, 823–832. https://doi.org/10.1111/j.1467-8659.2009.01677.x.
Reda, K., and Szafir, D. A. (2021a), “Rainbows revisited: Modeling
effective colormap design for graphical inference,” IEEE
Transactions on Visualization and Computer Graphics, 27, 1032–1042.
https://doi.org/10.1109/TVCG.2020.3030439.
Reda, K., and Szafir, D. A. (2021b), “Rainbows revisited: Modeling
effective colormap design for graphical inference,” IEEE
transactions on visualization and computer graphics, 27, 1032–1042.
https://doi.org/10.1109/TVCG.2020.3030439.
Robinson, D., Hayes, A., Couch, S., and Hvitfeldt, E. (2026), broom: Convert statistical objects into tidy
tibbles. https://doi.org/10.32614/CRAN.package.broom.
Robinson, E. A., Howard, R., and VanderPlas, S. (2023),
“‘You draw it’: Implementation of visually fitted
trends with r2d3,” Journal of
Data Science, School of Statistics, Renmin University of China, 21,
281–294.
Roy Chowdhury, N., Cook, D., Hofmann, H., Majumder, M., Lee, E.-K., and
Toth, A. L. (2015), “Using visual statistical inference to better
understand random class separations in high dimension, low sample size
data,” Computational Statistics, 30, 293–316. https://doi.org/10.1007/s00180-014-0534-x.
Sanyal, J., Zhang, S., Bhattacharya, G., Amburn, P., and Moorhead, R. J.
(2009), “A user study to compare four uncertainty visualization
methods for 1D and 2D datasets,”
IEEE Transactions on Visualization and Computer Graphics, IEEE,
15, 1209–1218. https://doi.org/10.1109/TVCG.2009.114.
Sarma, A., Guo, S., Hoffswell, J., Rossi, R., Du, F., Koh, E., and Kay,
M. (2023), “Evaluating the use of uncertainty visualisations for
imputations of data missing at random in scatterplots,” IEEE
Transactions on Visualization and Computer Graphics, 29, 602–612.
https://doi.org/10.1109/TVCG.2022.3209348.
Sarma, A., Pu, X., Cui, Y., Correll, M., Brown, E. T., and Kay, M.
(2024), “Odds and insights: Decision quality in exploratory data
analysis under uncertainty,” in Proceedings of the CHI
conference on human factors in computing systems, CHI ’24, New
York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3613904.3641995.
Satyanarayan, A., Moritz, D., Wongsuphasawat, K., and Heer, J. (2016),
“Vega-lite: A grammar of interactive graphics,” IEEE
Transactions on Visualization and Computer Graphics, IEEE, 23,
341–350.
Savelli, S., and Joslyn, S. (2013), “The advantages of predictive
interval forecasts for non-expert users and the impact of
visualizations,” Applied Cognitive Psychology, Wiley
Online Library, 27, 527–541.
Savvides, R., Henelius, A., Oikarinen, E., and Puolamäki, K. (2019),
“Significance of patterns in data visualisations,” in
Proceedings of the 25th ACM SIGKDD
International Conference on
Knowledge Discovery & Data
Mining, Anchorage AK USA: Association for Computing
Machinery, pp. 1509–1517. https://doi.org/10.1145/3292500.3330994.
Simons, D. J., and Levin, D. T. (1997), “Change blindness,”
Trends in Cognitive Sciences, 1, 261–267. https://doi.org/10.1016/S1364-6613(97)01080-2.
Smart, S., and Szafir, D. A. (2019), “Measuring the separability
of shape, size, and color in scatterplots,” Conference on
Human Factors in Computing Systems - Proceedings, 1–14. https://doi.org/10.1145/3290605.3300899.
Smemoe, C. M. (2004), Floodplain risk analysis using flood
probability and annual exceedance probability maps, Brigham Young
University.
Spiegelhalter, D. (2017), “Risk and uncertainty
communication,” Annual Review of Statistics and Its
Application, 4, 31–60. https://doi.org/10.1146/annurev-statistics-010814-020148.
Stauffer, R., Mayr, G. J., Dabernig, M., and Zeileis, A. (2009),
“Somewhere over the rainbow: How to make effective use of colors
in meteorological visualizations,” Bulletin of the American
Meteorological Society, 96, 203–216. https://doi.org/10.1175/BAMS-D-13-00155.1.
Strochak, S., Ueyama, K., and Williams, A. (2024), urbnmapr: State and county shapefiles in sf and tibble
format.
Sumner, M. (2021), ozmaps: Australia
maps. https://doi.org/10.32614/CRAN.package.ozmaps.
Swihart, B. J., Caffo, B., James, B. D., Strand, M., Schwartz, B. S.,
and Punjabi, N. M. (2010), “Lasagna plots: A saucy alternative to
spaghetti plots,” Epidemiology, LWW, 21, 621–625.
Tamura, S., Okamoto, Y., Nakagawa, S., Sakamoto, T., Ando, M., and
Shigeri, Y. (2017), “Light wavelengths of LEDs to
improve the color discrimination in Ishihara test and
Farnsworth Panel D-15 test for
deutans,” Color Research & Application, 42, 424–430.
https://doi.org/10.1002/col.22106.
Thomson, J., Hetzler, E., MacEachren, A., Gahegan, M., and Pavel, M.
(2005), “A typology for visualizing uncertainty,”
Visualization and Data Analysis 2005, 5669, 146. https://doi.org/10.1117/12.587254.
Tierney, N. (2020), “Ishihara,”
https://github.com/njtierney/ishihara.
Tierney, N., and Cook, D. (2023), “Expanding tidy data principles
to facilitate missing data exploration, visualization and assessment of
imputations,” Journal of Statistical Software, 105,
1–31. https://doi.org/10.18637/jss.v105.i07.
Tukey, J. W., and others (1977), Exploratory data analysis,
Springer.
UNESCO (n.d.). “International
Standard Classification of
Education - ISCED
Institute for Statistics
(UIS).”
Vanderplas, S., Cook, D., and Hofmann, H. (2020), “Testing
statistical charts: What makes a good graph?” Annual Review
of Statistics and Its Application, Annual Reviews, 7, 61–88.
VanderPlas, S., and Hofmann, H. (2015), “Signs of the sine
illusion—why we need to care,” Journal of Computational and
Graphical Statistics, Taylor & Francis, 24, 1170–1190.
VanderPlas, S., and Hofmann, H. (2017), “Clusters beat trend!?
Testing feature hierarchy in statistical graphics,”
Journal of Computational and Graphical Statistics, Taylor &
Francis, 26, 231–242.
VanderPlas, S., Röttger, C., Cook, D., and Hofmann, H. (2021),
“Statistical significance calculations for scenarios in visual
inference,” Stat, Wiley Online Library, 10, e337.
Venables, W. N., and Ripley, B. D. (2002), Modern applied statistics
with S, New York: Springer.
Waldhör, T. (1996), “The spatial autocorrelation coefficient
Moran’s I under heteroscedasticity,”
Stat Med, 15, 887–892. https://doi.org/10.1002/(sici)1097-0258(19960415)15:7/9<887::aid-sim257>3.0.co;2-e.
Walker, W. E., Harremoes, P., Rotmans, J., Van Der Sluijs, J. P., Van
Asselt, M. B. A., Janssen, P., and Krayer Von Krauss, M. P. (2003),
“Defining
uncertainty,” Integrated Assessment, 4, 5–17.
Waller, L. A. (2024), “Maps: A statistical view,”
Annual Review of Statistics and its Application, Annual
Reviews, 11, 75–96. https://doi.org/10.1146/annurev-statistics-032921-040851.
Wallsten, T. S., Budescu, D. V., Erev, I., and Diederich, A. (1997),
“Evaluating and combining subjective probability
estimates,” Journal of Behavioral Decision Making, 10,
243–268. https://doi.org/10.1002/(sici)1099-0771(199709)10:3<243::aid-bdm268>3.0.co;2-m.
Wickham, H. (2010), “A layered grammar of graphics,”
Journal of Computational and Graphical Statistics, 19, 3–28. https://doi.org/10.1198/jcgs.2009.07098.
Wickham, H. (2019), Advanced R, Chapman; Hall/CRC.
https://doi.org/10.1201/9781351201315.
Wickham, H. (2023), conflicted: An
alternative conflict resolution strategy. https://doi.org/10.32614/CRAN.package.conflicted.
Wickham, H., Averick, M., Bryan, J., Chang, W., D’Agostino McGowan, L.,
François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., Kuhn, M.,
Pedersen, T. L., Miller, E., Bache, S. M., Müller, K., Ooms, J.,
Robinson, D., Seidel, D. P., Spinu, V., Takahashi, K., Vaughan, D.,
Wilke, C., Woo, K., and Yutani, H. (2019), “Welcome to the
tidyverse,” Journal of Open Source Software, 4, 1686. https://doi.org/10.21105/joss.01686.
Wickham, H., Cook, D., Hofmann, H., and Buja, A. (2010),
“Graphical inference for Infovis,” IEEE
Transactions on Visualization and Computer Graphics, 16, 973–979.
https://doi.org/10.1109/TVCG.2010.161.
Wickham, H., François, R., Henry, L., Müller, K., and Vaughan, D.
(2023), dplyr: A grammar of data
manipulation. https://doi.org/10.32614/CRAN.package.dplyr.
Wickham, H., and Hofmann, H. (2011), “Product plots,”
IEEE Transactions on Visualization and Computer Graphics, 17,
2223–2230. https://doi.org/10.1109/TVCG.2011.227.
Wickham, H., Lawrence, M., Cook, D., Buja, A., Hofmann, H., and Swayne,
D. F. (2009), “The plumbing of interactive graphics,”
Computational Statistics, Springer, 24, 207–215.
Wickham, H., Pedersen, T. L., and Seidel, D. (2025a), scales: Scale functions for visualization. https://doi.org/10.32614/CRAN.package.scales.
Wickham, H., Vaughan, D., and Girlich, M. (2025b), tidyr: Tidy messy data. https://doi.org/10.32614/CRAN.package.tidyr.
Wilkinson, L. (2005), The grammar of graphics, Berlin,
Heidelberg: Springer-Verlag. https://doi.org/10.1007/0-387-28695-0.
Wu, Y., Guo, Z., Mamakos, M., Hartline, J., and Hullman, J. (2023),
“The rational agent
benchmark for data visualization.”
Xiao, J. (2021), “Spatial aggregation entropy: A heterogeneity and
uncertainty metric of spatial aggregation,” Annals of the
American Association of Geographers, 111, 1236–1252. https://doi.org/10.1080/24694452.2020.1807309.
Yang, F., Cai, M., Mortenson, C., Fakhari, H., Lokmanoglu, A. D.,
Hullman, J., Franconeri, S., Diakopoulos, N., Nisbet, E. C., and Kay, M.
(2023), “Swaying the public? Impacts of election
forecast visualizations on emotion, trust, and intention in the 2022
US midterms,” IEEE Transactions on Visualization
and Computer Graphics, IEEE, 30, 23–33.
Yu, G. (2025), shadowtext: Shadow text
grob and layer. https://doi.org/10.32614/CRAN.package.shadowtext.
Zhang, M., and Lin, D. K. J. (2022), “Visualization for interval
data,” Journal of Computational and Graphical
Statistics, 31, 960–975. https://doi.org/10.1080/10618600.2022.2066678.
Zhao, J., Wang, Y., Mancenido, M. V., Chiou, E. K., and Maciejewski, R.
(2023), “Evaluating the impact of uncertainty visualization on
model reliance,” IEEE Transactions on Visualization and
Computer Graphics, IEEE, 30, 4093–4107. https://doi.org/10.1109/TVCG.2023.3251950.
Zhu, H. (2024), kableExtra: Construct complex table with ’kable’ and
pipe syntax. https://doi.org/10.32614/CRAN.package.kableExtra.