Chapter 16 Further Reading

Foundational Visualization Theory

Tufte, Edward R. The Visual Display of Quantitative Information. Graphics Press, 2nd ed., 2001. The classic text on statistical graphics. Tufte's concept of "chartjunk" (visual elements that don't carry information) and the "data-ink ratio" (maximize ink devoted to actual data) are essential design principles. Chapter 2 on graphical excellence and Chapter 5 on chartjunk are most directly applicable to political data visualization.

Cairo, Alberto. The Functional Art: An Introduction to Information Graphics and Visualization. New Riders, 2012. A more accessible and practically oriented introduction to data visualization by a journalism professor and practitioner. Cairo's concept of the "visualization wheel" — trading off among density, dimensionality, and multifunctionality — provides a useful framework for visualization design decisions.

Wilkinson, Leland. The Grammar of Graphics. Springer, 2nd ed., 2005. The theoretical foundation for the ggplot2 (R) and plotnine (Python) visualization packages. Wilkinson's decomposition of graphics into aesthetic mappings, geometric objects, and statistical transformations provides the most rigorous framework for thinking about what visualizations do and why.

Political Data Visualization

Yau, Nathan. Visualize This: The FlowingData Guide to Design, Visualization, and Statistics. Wiley, 2011. A practical guide to data visualization with substantial coverage of geographic and social data, including election mapping. Yau runs the FlowingData blog, which regularly features innovative political data visualizations.

Fowler, James, Michael Heaney, and Oleg Nikolayev. "The Political Visualizations of Academic Datasets." PS: Political Science and Politics, 2017. An academic examination of how political scientists use data visualization and the common errors in academic political graphics.

Economist Data Team. Daily Chart archive (economist.com/graphic-detail). The Economist's data visualization team produces consistently excellent political and economic graphics. Their archive is an invaluable resource of real-world examples of well-designed political data visualizations. Particularly recommended: their election forecast graphics and cartogram-based electoral maps.

Python Tools

GeoPandas Documentation (geopandas.org). The official documentation includes tutorials on choropleth mapping, spatial joins, and coordinate reference systems. The "Mapping and Plotting Tools" section is essential reading for anyone building geographic political visualizations.

Plotly Python Documentation (plotly.com/python). Comprehensive reference for all Plotly visualization types. The choropleth maps section and the dropdown/button interactivity section are most relevant to this chapter's examples.

Matplotlib Documentation — Color Choices (matplotlib.org/stable/tutorials/colors). The official matplotlib documentation on colormaps includes the rationale for perceptually uniform colormaps and a comprehensive guide to available palettes. The section on choosing colormaps for different data types is particularly useful.

Hunter, John D. "Matplotlib: A 2D Graphics Environment." Computing in Science and Engineering 9(3), 2007: 90–95. The original matplotlib paper. Short and readable; provides context for the library's design philosophy that helps explain some of its quirks.

Electoral Mapping and Geographic Visualization

Rodden, Jonathan A. Why Cities Lose: The Deep Roots of the Urban-Rural Political Divide. Basic Books, 2019. Chapter 1 contains an excellent discussion of how geographic concentration of Democratic votes (in cities) interacts with single-member district systems to produce systematic representation biases. The book uses maps throughout and is one of the best examples of geographic visualization in service of political analysis.

Katz, Jonathan and Gary King. "A Statistical Model for Multiparty Electoral Data." American Political Science Review 93(1), 1999: 15–32. A methodological foundation for analyzing and visualizing electoral data across districts and election cycles. The paper introduces visualization methods for multiparty systems that extend to two-party analyses.

Monmonier, Mark. How to Lie with Maps. University of Chicago Press, 3rd ed., 2018. A readable and entertaining examination of how maps mislead — through projection choices, scale manipulation, data classification choices, and color distortion. Essential reading for understanding the manipulative potential of choropleth maps and how to guard against it.

Redistricting and Gerrymandering Visualization

Stephanopoulos, Nicholas O. and Eric M. McGhee. "Partisan Gerrymandering and the Efficiency Gap." University of Chicago Law Review 82(2), 2015: 831–900. The original efficiency gap paper that introduced one of the central metrics in redistricting litigation. The paper includes substantial discussion of how to visualize and interpret efficiency gap statistics across states and election cycles.

Chen, Jowei and Jonathan Rodden. "Unintentional Gerrymandering: Political Geography and Electoral Bias in Legislatures." Quarterly Journal of Political Science 8(3), 2013: 239–269. Shows that Democratic votes' concentration in cities can produce partisan bias in districting plans even without intentional manipulation — an important counterpoint to purely intentional accounts of gerrymandering. Uses geographic visualization extensively.

Uncertainty Visualization

Hullman, Jessica and Matthew Kay. "Why Authors Don't Visualize Uncertainty." IEEE Transactions on Visualization and Computer Graphics 25(1), 2019: 130–139. A research study examining why data journalists and analysts frequently omit uncertainty from visualizations, and what cognitive barriers make uncertainty visualization difficult. Directly relevant to election forecast graphics like the New York Times Needle.

Padilla, Lace, Matthew Kay, and Jessica Hullman. "Uncertainty Visualization." Psychological Science in the Public Interest 22(3), 2021: 77–112. A comprehensive review of research on how people understand (and misunderstand) visual representations of uncertainty. Includes specific guidance for designers of probability visualizations in high-stakes public contexts.