References by topic

Author

Camille Seaberry

Modified

February 14, 2024

This is most of what’s in my Zotero bibliography for this class arranged by topic.

Main texts

Foundational books and chapters

Cairo, A. (2019). How charts lie: Getting smarter about visual information (First edition). W. W. Norton & Company.
Wickham, H., Çetinkaya-Rundel, M., & Grolemund, G. (2023). R for data science: Import, tidy, transform, visualize, and model data (2nd edition). O’Reilly. https://r4ds.hadley.nz/
Wilke, C. (2019). Fundamentals of data visualization: A primer on making informative and compelling figures (First edition). O’Reilly. https://clauswilke.com/dataviz/
Yau, N. (2013). Data points: Visualization that means something. John Wiley & Sons, Inc.

General references

References, frameworks, and grammar of graphics

BBC. (2010). Hans Rosling’s 200 Countries, 200 Years, 4 Minutes - The Joy of Stats - BBC Four. https://www.youtube.com/watch?v=jbkSRLYSojo
Chang, W. (2018). R graphics cookbook: Practical recipes for visualizing data (Second edition). O’Reilly. https://r-graphics.org/
Cogley, B., & Setlur, V. (2022). Functional Aesthetics for Data Visualization. John Wiley and Sons.
D’Ignazio, C. (2015). What would feminist data visualization look like? https://civic.mit.edu/feminist-data-visualization.html
D’Ignazio, C., & Klein, L. F. (2020). Data feminism. The MIT Press. https://data-feminism.mitpress.mit.edu/
Du Bois, W. E. B., Battle-Baptiste, W., & Rusert, B. (2018). W.E.B Du Bois’s data portraits: Visualizing Black America (First edition). The W.E.B. Du Bois Center At the University of Massachusetts Amherst ; Princeton Architectural Press.
Gilmore, R. W. (2023). Abolition Geography.
Kirk, A. (2016). Data visualisation: A handbook for data driven design. SAGE.
Kosara, R. (2019). The DataSaurus, Anscombe’s Quartet, and why summary statistics need to be taken with a grain of salt. https://www.youtube.com/watch?v=RbHCeANCbW0
Munzner, T. (2014). Visualization Analysis and Design. A K Peters/CRC Press. https://doi.org/10.1201/b17511
Ribecca, S. (2024). The Data Visualisation Catalogue. https://datavizcatalogue.com/
Schwabish, J. (n.d.-a). Catherine D’Ignazio and Lauren Klein (142). Retrieved January 30, 2024, from https://policyviz.com/podcast/episode-142-catherine-dignazio-and-lauren-klein/
Schwabish, J. (n.d.-b). Sarah Williams (191). Retrieved January 18, 2024, from https://policyviz.com/podcast/episode-191-sarah-williams/
Schwabish, J. (2021). Better Data Visualizations : A Guide for Scholars, Researchers, and Wonks. Columbia University Press.
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
Wickham, H., Çetinkaya-Rundel, M., & Grolemund, G. (2023). R for data science: Import, tidy, transform, visualize, and model data (2nd edition). O’Reilly. https://r4ds.hadley.nz/
Wilke, C. (2019). Fundamentals of data visualization: A primer on making informative and compelling figures (First edition). O’Reilly. https://clauswilke.com/dataviz/

Aesthetics & styling

Color, visual perception, annotations, text, and styleguides

Aisch, G. (2019). Chroma.js palette helper. https://gka.github.io/palettes
Bertini, E., & Stefaner, M. (n.d.). Color with Karen Schloss (119). Retrieved February 7, 2024, from https://datastori.es/119-color-with-karen-schloss/
Brewer, C. A. (2006). Basic Mapping Principles for Visualizing Cancer Data Using Geographic Information Systems (GIS). American Journal of Preventive Medicine, 30(2), S25–S36. https://doi.org/10.1016/j.amepre.2005.09.007
Datawrapper. (2021). What to consider when choosing colors for data visualization. https://academy.datawrapper.de/article/140-what-to-consider-when-choosing-colors-for-data-visualization
France, T. (2020). Choosing Fonts for Your Data Visualization, Nightingale. In Nightingale. https://nightingaledvs.com/choosing-fonts-for-your-data-visualization/
Gramazio, C. C., Laidlaw, D. H., & Schloss, K. B. (2017). Colorgorical: Creating discriminable and preferable color palettes for information visualization. IEEE Transactions on Visualization and Computer Graphics. https://doi.org/10.1109/TVCG.2016.2598918
Heer, J., & Bostock, M. (2010). Crowdsourcing graphical perception: Using mechanical turk to assess visualization design. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 203–212. https://doi.org/10.1145/1753326.1753357
Kebonye, N. M., Agyeman, P. C., Seletlo, Z., & Eze, P. N. (2023). On exploring bivariate and trivariate maps as visualization tools for spatial associations in digital soil mapping: A focus on soil properties. Precision Agriculture, 24(2), 511–532. https://doi.org/10.1007/s11119-022-09955-7
Kim, D. H., Setlur, V., & Agrawala, M. (2021). Towards Understanding How Readers Integrate Charts and Captions: A Case Study with Line Charts. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, 1–11. https://doi.org/10.1145/3411764.3445443
Kirk, A. (2015). Make grey your best friend. In Visualising Data. https://visualisingdata.com/2015/01/make-grey-best-friend/
Liu, Y., & Heer, J. (2018). Somewhere Over the Rainbow: An Empirical Assessment of Quantitative Colormaps. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 1–12. https://doi.org/10.1145/3173574.3174172
Muth, L. C. (2022). What to consider when using text in data visualizations. In Datawrapper. https://blog.datawrapper.de/text-in-data-visualizations/
Muth, L. C. (2024a). How to choose a color palette for choropleth maps. In Datawrapper. https://blog.datawrapper.de/how-to-choose-a-color-palette-for-choropleth-maps/
Muth, L. C. (2024b). How to choose an interpolation for your color scale - Datawrapper Blog. In Datawrapper. https://blog.datawrapper.de/interpolation-for-color-scales-and-maps/
Muth, L. C. (2024c). Which fonts to use for your charts and tables. In Datawrapper. https://blog.datawrapper.de/fonts-for-data-visualization/
Setlur, V., & Stone, M. C. (2016). A Linguistic Approach to Categorical Color Assignment for Data Visualization. IEEE Transactions on Visualization and Computer Graphics, 22(1), 698–707. https://doi.org/10.1109/TVCG.2015.2467471
Skau, D., & Kosara, R. (2016). Arcs, Angles, or Areas: Individual Data Encodings in Pie and Donut Charts. Computer Graphics Forum, 35(3), 121–130. https://doi.org/10.1111/cgf.12888
Urban Institute. (2023). Urban Institute Data Visualization style guide. http://urbaninstitute.github.io/graphics-styleguide/
Viz Palette. (2019). https://projects.susielu.com/viz-palette
World Health Organization. (2023). WHO Data Design Language v.0.9.2. https://apps.who.int/gho/data/design-language/

Understanding data

Decision-making, uncertainty, missing data, and logical fallacies

Aisch, G. (2016). Why we used jittery gauges in our live election forecast. In vis4.net. https://vis4.net/blog/jittery-gauges-election-forecast
Bertini, E., & Stefaner, M. (n.d.-a). Cognitive Bias and Visualization with Evanthia Dimara (116). Retrieved February 14, 2024, from https://datastori.es/116-cognitive-bias-and-visualization-with-evanthia-dimara/
Bertini, E., & Stefaner, M. (n.d.-b). Visualizing Uncertainty with Jessica Hullman and Matthew Kay (134). Retrieved January 30, 2024, from https://datastori.es/134-visualizing-uncertainty-with-jessica-hullman-and-matthew-kay/
Correll, M., & Gleicher, M. (2014). Error Bars Considered Harmful: Exploring Alternate Encodings for Mean and Error. IEEE Transactions on Visualization and Computer Graphics, 20(12), 2142–2151. https://doi.org/10.1109/TVCG.2014.2346298
Correll, M., Moritz, D., & Heer, J. (2018). Value-Suppressing Uncertainty Palettes. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 1–11. https://doi.org/10.1145/3173574.3174216
Cox, C., Amin, K., Kates, J., & Published, J. M. (2022). Why Do Vaccinated People Represent Most COVID-19 Deaths Right Now? In KFF. https://www.kff.org/policy-watch/why-do-vaccinated-people-represent-most-covid-19-deaths-right-now/
Etter, E. (2023). Data Visualization: A Subjective Lens on Reality. In Nightingale. https://nightingaledvs.com/data-visualization-a-subjective-lens-on-reality/
Hamel, L., Kirzinger, A., Muñana, C., & Published, M. B. (2020). KFF COVID-19 Vaccine Monitor: December 2020. In KFF. https://www.kff.org/coronavirus-covid-19/report/kff-covid-19-vaccine-monitor-december-2020/
Hamel, L., Lopes, L., & Published, M. B. (2021). KFF COVID-19 Vaccine Monitor: What Do We Know About Those Who Want to “Wait and See” Before Getting a COVID-19 Vaccine? In KFF. https://www.kff.org/coronavirus-covid-19/poll-finding/kff-covid-19-vaccine-monitor-wait-and-see/
Kay, M. (2024). Mjskay/ggdist. https://github.com/mjskay/ggdist
Kay, M., Kola, T., Hullman, J. R., & Munson, S. A. (2016). When (ish) is My Bus?: User-centered Visualizations of Uncertainty in Everyday, Mobile Predictive Systems. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, 5092–5103. https://doi.org/10.1145/2858036.2858558
Kirk, A. (n.d.). Alvin Chang (S2 E6). Retrieved February 2, 2024, from https://visualisingdata.com/2020/12/explore-explain-s2-e6-alvin-chang/
Kirk, A. (2016). Gauging election reaction. In Visualising Data. https://visualisingdata.com/2016/11/gauging-election-reaction/
Krackov, A., & Marikos, S. (2021). Asterisk Nation: One Tribe’s Challenge to Find Data About its Population. In Nightingale. https://nightingaledvs.com/asterisk-nation-one-tribes-challenge-to-find-data-about-its-population/
Lee, C., Yang, T., Inchoco, G. D., Jones, G. M., & Satyanarayan, A. (2021). Viral Visualizations: How Coronavirus Skeptics Use Orthodox Data Practices to Promote Unorthodox Science Online. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, 1–18. https://doi.org/10.1145/3411764.3445211
mimimimimi. (2024). MimiOnuoha/missing-datasets. https://github.com/MimiOnuoha/missing-datasets
Nation, Z. (2024). Zonination/perceptions. https://github.com/zonination/perceptions
Nyame-Mensah, A. (2022). When Oversimplification Obscures. In Nightingale. https://nightingaledvs.com/when-oversimplification-obscures/
Pillai, D., Artiga, S., Hamel, L., Schumacher, S., Kirzinger, A., Rao, A., & Published, A. K. (2024). Understanding the Diversity in the Asian Immigrant Experience in the U.S.: The 2023 KFF/LA Times Survey of Immigrants. In KFF. https://www.kff.org/racial-equity-and-health-policy/poll-finding/understanding-the-diversity-in-the-asian-immigrant-experience/
Sadler, R. C. (2016). How ZIP codes nearly masked the lead problem in Flint. In The Conversation. http://theconversation.com/how-zip-codes-nearly-masked-the-lead-problem-in-flint-65626
Schwabish, J. (n.d.). Joe Sharpe and Mike Orwell (242). Retrieved February 13, 2024, from https://policyviz.com/podcast/episode-242-joe-sharpe-and-mike-orwell/
Silver, N. (2015). The Most Diverse Cities Are Often The Most Segregated. In FiveThirtyEight. https://fivethirtyeight.com/features/the-most-diverse-cities-are-often-the-most-segregated/
Simeoni, F. (2023). Querying the Quantification of Queer. In Nightingale. https://nightingaledvs.com/querying-the-quantification-of-the-queer/
Smith, N. (2023). How not to be fooled by viral charts. https://www.noahpinion.blog/p/how-not-to-be-fooled-by-viral-charts

Storytelling

Telling a story and making a point

Fratczak, M. (2023). Can Datavis Make Unpalatable Data More Enjoyable? In Nightingale. https://nightingaledvs.com/can-datavis-make-unpalatable-data-more-enjoyable/
Hullman, J., & Diakopoulos, N. (2011). Visualization Rhetoric: Framing Effects in Narrative Visualization. IEEE Transactions on Visualization and Computer Graphics, 17(12), 2231–2240. https://doi.org/10.1109/TVCG.2011.255
Seaberry, C. (2018). CT Data Story: Housing Segregation in Greater New Haven. DataHaven. https://ctdatahaven.org/reports/ct-data-story-housing-segregation-greater-new-haven

Social justice & ethics

Data viz for action in the real world

Alderman, D. H., & Inwood, J. F. J. (2024). Black communities are using mapping to document and restore a sense of place. In The Conversation. http://theconversation.com/black-communities-are-using-mapping-to-document-and-restore-a-sense-of-place-221299
BocoupLLC. (2017). A Data Point Walks Into a Bar: Designing Data For Empathy - Lisa Charlotte Rost. https://www.youtube.com/watch?v=8XgF-RmNwUc
“Dispersion & Disparity” Research Project Results. (2023). https://3iap.com/dispersion-disparity-equity-centered-data-visualization-research-project-Wi-58RCVQNSz6ypjoIoqOQ/
Elghany, S. (2023). How Ethical Data Visualization Tells the Human Story. In Nightingale. https://nightingaledvs.com/ethical-data-visualization-tells-the-human-story/
Hahn, J. (2023). "Data replicates the existing systems of power" says Pulitzer Prize-winner Mona Chalabi. Dezeen. https://www.dezeen.com/2023/11/16/mona-chalabi-pulitzer-prize-winner/
Holder, E. (2022). Unfair Comparisons: How Visualizing Social Inequality Can Make It Worse, Nightingale. In Nightingale. https://nightingaledvs.com/unfair-comparisons-how-visualizing-social-inequality-can-make-it-worse/
Justice Policy Institute, & Prison Policy Initiative. (2022). Where people in prison come from: The geography of mass incarceration in Maryland. https://www.prisonpolicy.org/origin/md/2020/report.html
Levy-Rubinett, I. (2020). With Great Visualization Comes Great Responsibility. In Nightingale. https://nightingaledvs.com/with-great-visualization-comes-great-responsibility/
Makulec, A. (2020). Ten Considerations Before you Create another Chart about COVID-19. In Nightingale. https://medium.com/nightingale/ten-considerations-before-you-create-another-chart-about-covid-19-27d3bd691be8
Ney, J. (2023). Mapping Inequality Can Drive Social Impact. In Nightingale. https://nightingaledvs.com/mapping-inequality-can-drive-social-impact/
Thomas, T., Drewery, M., Greif, M., Kennedy, I., Ramiller, A., Toomet, O., & Hernandez, J. (2020). Baltimore Eviction Map. Eviction Research Network, UC Berkeley. https://evictionresearch.net/maryland/report/baltimore.html
University of Richmond Digital Scholarship Lab. (n.d.). Mapping Inequality: Redlining in New Deal America. In American Panorama: An Atlas of United States History. Retrieved November 10, 2022, from https://dsl.richmond.edu/panorama/redlining/

Spatial data

Spatial is special

Ericson, M. (2011). When Maps Shouldn’t Be Maps. In ericson.net. https://www.ericson.net/content/2011/10/when-maps-shouldnt-be-maps/
Nussbaumer Knaflic, C. (n.d.). Maps with Kenneth Field (41). Retrieved March 27, 2024, from https://storytellingwithdata.libsyn.com/storytelling-with-data-41-maps-with-kenneth-field
Simmon, R. (2024). From Space to Story in Data Journalism, Nightingale. In Nightingale. https://nightingaledvs.com/from-space-to-story-in-data-journalism/
US Census Bureau. (2021). Appendix B: Measures of Residential Segregation. In Guidance for Housing Patterns Data Users. https://www.census.gov/topics/housing/housing-patterns/guidance/appendix-b.html
Wiseman, A. (2015). Bad Maps Are Everywhere These Days. Here’s How to Avoid Being Fooled. Bloomberg CityLab. https://www.bloomberg.com/news/articles/2015-06-25/how-to-avoid-being-fooled-by-bad-maps
Wong, D. (2024). The SAGE Handbook of Spatial Analysis. SAGE Publications, Ltd. https://doi.org/10.4135/9780857020130
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