References

BBC Visual and Data Journalism Team. 2019. “BBC Visual and Data Journalism Cookbook for R Graphics.” https://github.com/bbc/rcookbook.
Beecham, R. 2020. “Using Position, Angle and Thickness to Expose the Shifting Geographies of the 2019 UK General Election.” Environment and Planning A: Economy and Space 52 (5): 833–36. https://doi.org/10.1177/0308518X20909392.
———. 2024. gridmappr: An R Package for Creating Small Multiple Gridmap Layouts.” In GISRUK 2024. Leeds, UK: Zenodo. https://doi.org/10.5281/zenodo.10926863.
Beecham, R., J. Dykes, L. Hama, and N. Lomax. 2021. “On the Use of ‘Glyphmaps’ for Analysing the Scale and Temporal Spread of COVID-19 Reported Cases.” ISPRS International Journal of Geo-Information 10 (4). https://doi.org/10.3390/ijgi10040213.
Beecham, R., J. Dykes, W. Meulemans, A. Slingsby, C. Turkay, and J. Wood. 2017. “Map Line-Ups: Effects of Spatial Structure on Graphical Inference.” IEEE Transactions on Visualization & Computer Graphics 23 (1): 391–400. https://doi.org/10.1109/TVCG.2016.2598862.
Beecham, R., J. Dykes, C. Rooney, and W. Wong. 2021. Design Exposition Discussion Documents for Rich Design Discourse in Applied Visualization.” IEEE Transactions on Visualization and Computer Graphics 27 (8): 3451–62. https://doi.org/10.1109/TVCG.2020.2979433.
Beecham, R., and R. Lovelace. 2023. “A Framework for Inserting Visually-Supported Inferences into Geographical Analysis Workflow: Application to Road Safety Research” 55: 345–66. https://doi.org/10.1111/gean.12338.
Beecham, R., and A. Slingsby. 2019. “Characterising Labour Market Self-Containment in London with Geographically Arranged Small Multiples.” Environment and Planning A: Economy and Space 51 (6): 1217–24. https://doi.org/10.1177/0308518x19850580.
Beecham, R., A. Slingsby, and C. Brunsdon. 2018. “Locally-Varying Explanations Behind the United Kingdom’s Vote to Leave the European Union.” Journal of Spatial Information Science 16: 117–36. https://doi.org/10.5311/josis.2018.16.377.
Beecham, R., N. Williams, and L. Comber. 2020. “Regionally-Structured Explanations Behind Area-Level Populism: An Update to Recent Ecological Analyses.” PLOS One 15 (3): e0229974. https://doi.org/10.1371/journal.pone.0229974.
Beecham, R., and J. Wood. 2014. “Exploring Gendered Cycling Behaviours Within a Large-Scale Behavioural Data-Set.” Transportation Planning and Technology 37 (1): 83–97. https://doi.org/10.1080/03081060.2013.844903.
Beecham, R., Y. Yang, C. Tait, and R. Lovelace. 2023. “Connected Bikeability in London: Which Localities Are Better Connected by Bike and Does This Matter?” Environment and Planning B: Urban Analytics and City Science 50 (8): 2103–17. https://doi.org/10.1177/23998083231165122.
Bhatia, A., and H. Reich. 2020. Covidtrends. https://github.com/aatishb/covidtrends.
Boukhelifa, N., A. Bezerianos, T. Isenberg, and J. Fekete. 2012. “Evaluating Sketchiness as a Visual Variable for the Depiction of Qualitative Uncertainty.” IEEE Transactions on Visualization and Computer Graphics 18 (12): 2769–78. https://doi.org/10.1109/TVCG.2012.220.
Brewer, C., and A. Campbell. 1998. “Beyond Graduated Circles: Varied Point Symbols for Representing Quantitative Data on Maps.” Cartographic Perspectives, no. 29: 6–25. https://doi.org/10.14714/CP29.672.
Brunsdon, C., and M. Charlton. 2011. “An Assessment of the Effectiveness of Multiple Hypothesis Testing for Geographical Anomaly Detection.” Environment and Planning B: Planning and Design 38 (2): 216–30. https://doi.org/10.1068/b36093.
Brunsdon, C., and A. Comber. 2021. “Opening Practice: Supporting Reproducibility and Critical Spatial Data Science.” Journal of Geographical Systems 23: 477–96. https://doi.org/10.1007/s10109-020-00334-2.
Brunsdon, C., M. Fortheringham, and M. Charlton. 2002. “Geographically Weighted Summary Statistics: A Framework for Localised Exploratory Data Analysis.” Computers, Environment and Urban Systems 26: 501–24. https://doi.org/10.1016/S0198-9715(01)00009-6.
Buja, A., D. Cook, H. Hofmann, M. Lawrence, E-K Lee, D. Swayne, and H. Wickham. 2009. “Statistical Inference for Exploratory Data Analysis and Model Diagnostics.” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 367 (1906): 4361–83. https://doi.org/10.1098/rsta.2009.0120.
Burn-Murdoch, J. 2020. “BELIV 2020 Keynote: John Burn-Murdoch.” In 2020 IEEE Workshop on Evaluation and Beyond - Methodological Approaches to Visualization (BELIV), 9–9. https://doi.org/10.1109/BELIV51497.2020.00007.
———. 2021. “Vaccines Are Working: Charts That Show the Covid Endgame.” The Financial Times. https://www.ft.com/content/d71729a3-72e8-490c-bd7e-757027f9b226.
———. 2023. “Making Charts That Make an Impact.” Invited talk, Data Visualization Society’s Outlier Conference. https://www.youtube.com/watch?v=tIbaQUo6H9g&ab_channel=DataVisualizationSociety.
Butler, D., and S. Van Beek. 1990. “Why Not Swing? Measuring Electoral Change.” Political Science & Politics 23 (2): 178–84. https://doi.org/10.2307/420065.
Charlotte Muth, L. 2018. “Your Friendly Guide to Colors in Data Visualisation: An Overview of Color Tools.” Datawrapper. https://www.datawrapper.de/blog/colorguide.
Cleveland, W., and R. McGill. 1984. “Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods.” Journal of the American Statistical Association 79 (387): 531–54. https://doi.org/10.2307/2288400.
Comber, A., C. Brunsdon, M. Charlton, G. Dong, R. Harris, B. Lu, Y. Lü, et al. 2023. “A Route Map for Successful Applications of Geographically Weighted Regression.” Geographical Analysis 55 (1): 155–78. https://doi.org/10.1111/gean.12316.
Correll, M., and M. Gleicher. 2014. “Error Bars Considered Harmful: Exploring Alternate Encodings for Mean and Error.” IEEE Transactions on Visualization and Computer Graphics 20 (12): 2142–51. https://doi.org/10.1109/TVCG.2014.2346298.
Donoho, D. 2017. “50 Years of Data Science.” Journal of Computational and Graphical Statistics 26 (6): 745–66. https://doi.org/10.1080/10618600.2017.1384734.
Dykes, J., and C. Brunsdon. 2007. “Geographically Weighted Visualization: Interactive Graphics for Scale-Varying Exploratory Analysis.” IEEE Transactions on Visualization and Computer Graphics 13 (6): 1161–68. https://doi.org/10.1109/tvcg.2007.70558.
Financial Times. 2020. “Coronavirus Trajectory Tracker Explained.” https://www.ft.com/video/9a72a9d4-8db1-4615-8333-4b73ae3ddff8.
———. 2021. ‘It’s not a bad flu season’ - Covid myths debunked with data.” https://www-ft-com.proxy-ub.rug.nl/video/0cd6f9f9-664e-40f9-bad4-dde59d7c746c?playlist-name=latest&playlist-offset=223.
Franconeri, S. L., L. M. Padilla, P. Shah, J. M. Zacks, and J. Hullman. 2021. “The Science of Visual Data Communication: What Works.” Psychological Science in the Public Interest 22 (3): 110–61. https://doi.org/10.1177/15291006211051956 .
Friendly, M. 1992. “Mosaic Displays for Loglinear Models.” In ASA, Proceedings of the Statistical Graphics Section, 61–68.
Gamio, L., and D. Keating. 2016. “How Trump Redrew the Electoral Map, from Sea to Shining Sea.” The Washington Post. https://www.washingtonpost.com/graphics/politics/2016-election/election-results-from-coast-to-coast/.
Gelman, A. 2004. “Exploratory Data Analysis for Complex Models.” Journal of Computational and Graphical Statistics 13 (4): 755–79. https://doi.org/10.1198/106186004X11435.
Gelman, A., and J. Hill. 2006. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge, UK: Cambridge University Press. https://doi.org/10.1017/CBO9780511790942.
Gelman, A., J. Hill, and A. Vehtari. 2020. Regression and Other Stories. Analytical Methods for Social Research. Cambridge University Press. https://doi.org/10.1017/9781139161879.
Gelman, A., J. Hill, and M. Yajima. 2012. “Why We (Usually) Don’t Have to Worry about Multiple Comparisons.” Journal of Research on Educational Effectiveness 5 (2): 189–211. https://doi.org/10.1080/19345747.2011.618213.
Gleicher, M., D. Albers, R. Walker, I. Jusufi, C. Hansen, and J. Roberts. 2011. “Visual Comparison for Information Visualization.” Information Visualization 10 (4): 289–309. https://doi.org/10.1177/1473871611416549.
Gross, J. 2016. “How to Better Communicate Election Forecasts — in One Simple Chart.” The Washington Post. https://www.washingtonpost.com/news/monkey-cage/wp/2016/11/29/how-to-better-communicate-election-forecasts-in-one-simple-chart/.
Hanretty, C. 2017. “Areal Interpolation and the UK’s Referendum on EU Membership.” Journal of Elections, Public Opinion and Parties 37 (4): 466–83. https://doi.org/10.1080/17457289.2017.1287081.
Haroz, S., R. Kosara, and S. L. Franconeri. 2016. “The Connected Scatterplot for Presenting Paired Time Series.” IEEE Transactions on Visualization and Computer Graphics 22 (9): 2174–86. https://doi.org/10.1109/TVCG.2015.2502587.
Harrower, M., and C. A. Brewer. 2003. “ColorBrewer.org: An Online Tool for Selecting Colour Schemes for Maps.” The Cartographic Journal 40 (1): 27–37. https://doi.org/10.1179/000870403235002042.
Healy, K. 2019. Data Visualization: A Practical Introduction. Princeton, NJ: Princeton University Press. https://socviz.co.
———. 2020. covdata: COVID-19 Case and Mortality Time Series. http://kjhealy.github.io/covdata.
Heer, J., and M. Bostock. 2010. “Crowdsourcing Graphical Perception: Using Mechanical Turk to Assess Visualization Design.” In ACM Human Factors in Computing Systems, 203–12. https://doi.org/10.1145/1753326.1753357.
Henry Riche, N., C. Hurter, N. Diakopoulos, and S. Carpendale, eds. 2018. Data-Driven Storytelling. Abingdon, UK: CRC Press. https://doi.org/10.1201/9781315281575.
Hullman, J., and A. Gelman. 2021. “Designing for Interactive Exploratory Data Analysis Requires Theories of Graphical Inference.” Harvard Data Science Review 3 (3).
Hullman, J., P. Resnick, and E. Adar. 2015. “Hypothetical Outcome Plots Outperform Error Bars and Violin Plots for Inferences About Reliability of Variable Ordering.” PLOS One 10 (11). https://doi.org/10.1371/journal.pone.0142444.
Ismay, C., and A. Kim. 2020. Statistical Inference via Data Science: A ModernDive into R and the Tidyverse. New York, NY: CRC Press. https://doi.org/10.1201/9780367409913.
Jeppson, H., and H. Hofmann. 2023. “Generalized Mosaic Plots in the ggplot2 Framework.” The R Journal 14 (4): 50–78. https://doi.org/10.32614/RJ-2023-013.
Kale, A., F. Nguyen, M. Kay, and J. Hullman. 2019. “Hypothetical Outcome Plots Help Untrained Observers Judge Trends in Ambiguous Data.” IEEE Transactions on Visualization and Computer Graphics 25 (1): 892–902. https://doi.org/10.1109/TVCG.2018.2864909.
Kay, M. 2021. “Uncertainty Visualization as a Moral Imperative.” Invited talk, BostonCHI meeting. https://www.youtube.com/watch?v=mfQ3QVyw4N0&ab_channel=BostonCHI.
———. 2024. ggdist: Visualizations of Distributions and Uncertainty in the Grammar of Graphics.” IEEE Transactions on Visualization and Computer Graphics 30 (1): 414–24. https://doi.org/10.1109/TVCG.2023.3327195.
Kinkeldey, C., A. MacEachren, and J. Schiewe. 2014. “How to Assess Visual Communication of Uncertainty? A Systematic Review of Geospatial Uncertainty Visualisation User Studies.” The Cartographic Journal 51 (4): 372–86. https://doi.org/10.1179/1743277414Y.0000000099.
Kosara, R. 2023. “Lesson 4: Presentation, Uncertainty, ISOTYPE.” ObservableHQ Notebook; ObservableHQ. https://observablehq.com/@observablehq/lesson-4-presentation-uncertainty-isotype?collection=@observablehq/advanced-data-vis-course.
Kuhn, M., and J. Silge. 2023. Tidy Modelling with R. Sebastopol, CA: O’Reilly.
Lawlor, O., and H. Robertson. 2021. Masters series: Maarten Lambrechts’ connected scatter plot.” https://www.washingtonpost.com/news/monkey-cage/wp/2016/11/29/how-to-better-communicate-election-forecasts-in-one-simple-chart/?noredirect=on.
Lovelace, R., M. Morgan, L. Hama, M. Padgham, D. Ranzolin, and A. Sparks. 2019. “Stats 19: A Package for Working with Open Road Crash Data.” The Journal of Open Source Software 4 (33): 1181. https://doi.org/10.21105/joss.01181.
Lovelace, R., J. Nowosad, and J. Muenchow. 2019. Geocomputation with R. London, UK: CRC Press.
Loy, A., H. Hofmann, and D. Cook. 2017. “Model Choice and Diagnostics for Linear Mixed-Effects Models Using Statistics on Street Corners.” Journal of Computational and Graphical Statistics 26 (3): 478–92. https://doi.org/10.1080/10618600.2017.1330207.
McGill, Tukey, R., and W. A. Larsen. 1978. “Variations of Box Plots.” The American Statistician 32: 12–16. https://doi.org/10.2307/2683468.
Munzner, T. 2014. Visualization Analysis and Design. AK Peters Visualization Series. Boca Raton, FL: CRC Press.
NHC. 2023. National Hurricane Center and Central Pacific Hurricane Center.” https://www.nhc.noaa.gov/.
Noble, S., D. McLennan, M. Noble, E. Plunkett, N. Gutacker, M. Silk, and G. Wright. 2019. “The English Indices of Deprivation 2019.” Ministry of Housing, Communities & Local Government. https://www.gov.uk/government/statistics/english-indices-of-deprivation-2019.
Open Science Collaboration. 2015. “Estimating the Reproducibility of Psychological Science.” Science 349 (6251): aac4716. https://doi.org/10.1126/science.aac4716.
Padilla, L., M. Kay, and J. Hullman. 2021. “Uncertainty Visualization.” In Wiley StatsRef: Statistics Reference Online, edited by B. Everitt N. Balakrishnan T. Colton and J. L. Teugels. Wiley. https://doi.org/10.1002/9781118445112.stat08296.
Pebesma, E. 2018. Simple Features for R: Standardized Support for Spatial Vector Data.” The R Journal 10 (1): 439–46. https://doi.org/10.32614/RJ-2018-009.
Roth, R. 2021. “Cartographic Design as Visual Storytelling: Synthesis and Review of Map-Based Narratives, Genres, and Tropes.” The Cartographic Journal 58 (1): 83–114. https://doi.org/10.1080/00087041.2019.1633103.
Scherer, C. 2023. “Designing Data Visualizations to Successfully Tell a Story.” Workshop at Posit::conf(2023), Chicago, IL. https://posit-conf-2023.github.io/dataviz-storytelling/.
Silver, N. 2016. “Why FiveThirtyEight Gave Trump a Better Chance Than Almost Anyone Else.” FiveThirtyEight. https://fivethirtyeight.com/features/why-fivethirtyeight-gave-trump-a-better-chance-than-almost-anyone-else.
Stevens, S. 1946. “On the Theory of Scales of Measurement.” Science 103 (2684): 677–80. https://doi.org/10.1126/science.103.2684.677.
The New York Times. 2021. Coronavirus (Covid-19) Data in the United States. https://github.com/nytimes/covid-19-data.
The Turing Way Community. 2025. “The Turing Way: A Handbook for Reproducible, Ethical and Collaborative Research.” Zenodo. https://doi.org/10.5281/zenodo.15213042.
Thebault, R., and A. Hauslohner. 2020. “Covid-19’s Deadly New Surge Is in Rural America as States Re-Open - The Washington Post.” The Washington Post. https://www.washingtonpost.com/nation/2020/05/24/coronavirus-rural-america-outbreaks/?arc404=true.
Tortosa, E. V., R. Lovelace, E. Heinen, and R. P. Mann. 2021. “Socioeconomic Inequalities in Cycling Safety: An Analysis of Cycling Injury Risk by Residential Deprivation Level in England.” Journal of Transport & Health 23: 101291. https://doi.org/10.1016/j.jth.2021.101291.
Tufte, E. 1983. The Visual Display of Quantitative Information. Cheshire, CT: Graphics Press.
Tukey, J. W. 1962. The Future of Data Analysis.” The Annals of Mathematical Statistics 33 (1): 1–67. https://doi.org/10.1214/aoms/1177704711.
Tukey, John W. 1977. Exploratory Data Analysis. Reading, MA: Addison-Wesley.
Uberoi, E., C. Baker, and R. Cracknell. 2020. “General Election 2019: Full Results and Analysis.” House of Commons Library. https://commonslibrary.parliament.uk/research-briefings/cbp-8749/.
Van Goethem, A., A. Reimer, B. Speckmann, and J. Wood. 2014. “Stenomaps: Shorthand for Shapes.” IEEE Transactions on Visualization and Computer Graphics 20 (12): 2053–62. https://doi.org/10.1109/TVCG.2014.2346274.
Visvalingam, M. 1981. “The Signed Chi-Score Measure for the Classification and Mapping of Polychotomous Data.” The Cartographic Journal 18 (1): 32–43. https://doi.org/10.1179/caj.1981.18.1.32.
White, T. 2017. “Symbolization and the Visual Variables.” In The Geographic Information Science & Technology Body of Knowledge, edited by John P. Wilson.
Wickham, H. 2010. “A Layered Grammar of Graphics.” Journal of Computational and Graphical Statistics 19 (1): 3–28. https://doi.org/10.1198/jcgs.2009.07098.
———. 2014. “Tidy Data.” Journal of Statistical Software 59 (10): 1–23. https://doi.org/10.18637/jss.v059.i10.
Wickham, H., M. Çetinkaya-Rundel, and G. Grolemund. 2023. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. Second. Sebastopol, CA: O’Reilly Media.
Wickham, H., D. Cook, H. Hofmann, and A. Buja. 2010. “Graphical Inference for Infovis.” IEEE Transactions on Visualization and Computer Graphics 16 (6): 973–79. https://doi.org/10.1109/TVCG.2010.161.
Wickham, H., and G. Grolemund. 2017. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. Sebastopol, CA: O’Reilly Media.
Wickham, H., D. Navarro, and T. Lin Pedersen. 2023. ggplot2: Elegant Graphics for Data Analysis. 3rd ed. New York, NY: Springer.
Wilkinson, L. 1999. The Grammar of Graphics. New York, NY: Springer.
Wolf, L. J., L. Anselin, D. Arribas-Bel, and L. Rivers Mobley. 2021. “On Spatial and Platial Dependence: Examining Shrinkage in Spatially Dependent Multilevel Models.” Annals of the American Association of Geographers 111 (6): 1679–91. https://doi.org/10.1080/24694452.2020.1841602.
Wood, J., J. Dykes, and A. Slingsby. 2010. “Visualisation of Origins, Destinations and Flows with OD Maps.” The Cartographic Journal 47 (2): 117–29. https://doi.org/10.1179/000870410X12658023467367.
Wood, J., P. Isenberg, T. Isenberg, J. Dykes, N. Boukhelifa, and A. Slingsby. 2012. “Sketchy Rendering for Information Visualization.” IEEE Transactions on Visualization and Computer Graphics 18 (12): 2749–58. https://doi.org/10.1109/TVCG.2012.262.
Wood, J., A. Kachkaev, and J. Dykes. 2018. “Design Exposition with Literate Visualization.” IEEE Transactions on Visualization and Computer Graphics 25 (1): 759–68. https://doi.org/10.1109/TVCG.2018.2864836.
Wood, J., A. Slingsby, and J. Dykes. 2011. “Visualizing the Dynamics of London’s Bicycle-Hire Scheme.” Cartographica: The International Journal for Geographic Information and Geovisualization, no. 4: 239–51. https://doi.org/10.1179/000870410X12658023467367.
Yang, F., M. Cau, C. Mortenson, H. Fakhari, A. D. Lokmanoglu, J. Hullman, S. Franconeri, N. Diakopoulos, E. C. Nisbet, and M. Kay. 2024. “Swaying the Public? Impacts of Election Forecast Visualizations on Emotion, Trust, and Intention in the 2022 U.S. Midterms.” IEEE Transactions on Visualization and Computer Graphics 30 (1): 23–33. https://doi.org/10.1109/TVCG.2023.3327356.
Yang, Y., R. Beecham, A. Heppenstall, A. Turner, and A. Comber. 2022. “Understanding the Impacts of Public Transit Disruptions on Bikeshare Schemes and Cycling Behaviours Using Spatiotemporal and Graph-Based Analysis: A Case Study of Four London Tube Strikes.” Journal of Transport Geography 98: 103255. https://doi.org/10.1016/j.jtrangeo.2021.103255.