Data Visualisation

Data visualisation (or ‘dataviz’) is the creation and study of visual presentations of data, such as maps, pie charts and line graphs. Recent computing developments have created new tools and striking visual techniques, especially for use online, including the use of animations. These can be used not simply to illustrate finished arguments, but also to help explore data in new ways. But poor visualisations can be confusing and even misleading. The tools and techniques need to be used with care.

Digital Humanities projects like DP find dataviz particularly useful because we’re dealing with massive amounts of information that can’t easily be viewed or understood using traditional methods. We’ll use visualisation techniques in a number of ways at different stages of the project, but particularly within the Epistemologies research theme, where we will use a range of visualisations to explore both structured and unstructured datasets.


Deeper: Critique and Discussion

Practical: Tools

    Web-based tools and services, ideal for novices (and there are probably many more!)

  • Tableau Public – ‘tell stories with interactive data on the web’
  • Voyant Tools – a web-based reading and analysis environment for digital texts
  • Data Hero – ‘helps you easily create dynamic visualizations of the data that matters to you’
  • Palladio – ‘a web-based platform for the visualization of complex, multi-dimensional data’
  • Raw – ‘an open web app to create custom vector-based visualizations on top of the amazing D3.js library through a simple interface’
  • Silk – ‘lets you create visualizations, maps and overviews’
  • textexture – visualize any text as a network
  • Lexos – ‘enables you to “scrub” (clean) your text(s), cut a text(s) into various size chunks, manage chunks and chunk sets, and choose from a suite of analysis tools for investigating those texts’
  • Google Fusion Tables
  • CartoDB – to visualise and analyse geospatial data
  • Datawrapper – “An open source tool helping anyone to create simple, correct and embeddable charts in minutes”
    More advanced tools (may require programming knowledge)

  • D3.js – a JavaScript library for manipulating documents based on data
    • dimple aims to ‘open up the power and flexibility of d3’ with a more gentle learning curve
    • d3plus a similar extension aimed at making it easier to use the power of D3
  • JavaScript InfoVis Toolkit – tools for creating Interactive Data Visualizations for the Web
  • R – a free software environment for statistical computing and graphics
  • Processing – a programming language for design and data visualisation
  • Gephi – an interactive visualization and exploration platform for all kinds of networks, dynamic and hierarchical graphs
  • Variance – “build elegant bespoke data graphics for the web, using only HTML & CSS”
  • Pattern – a “web mining module” (Python) including visualisation tools
  • Seaborn – statistical data visualisation (Python)

Practical: Tutorials, Code Examples etc

Projects and Examples

Short Bibliography

  • Börner, Katy. “Plug-and-Play Macroscopes.” Commun. ACM 54, no. 3 (March 2011): 60–69. doi:10.1145/1897852.1897871.
  • Brown, Susan, et al. “Visualizing Varieties of Association in Orlando.” Journal of the Chicago Colloquium on Digital Humanities and Computer Science 1, no. 1 (July 17, 2009).
  • Drucker, Johanna. “Humanities Approaches to Graphical Display” 5, no. 1 (2011).
  • Hearst, Marti. “Chapter 11. Information Visualization for Text Analysis.” In Search User Interfaces. Cambridge University Press, 2009.
  • Lima, Manuel. Visual Complexity : Mapping Patterns of Information. New York: Princeton Architectural Press; Enfield, 2011.
  • Manovich, Lev. “What Is Visualization?paj:The Journal of the Initiative for Digital Humanities, Media, and Culture 2, no. 1 (December 12, 2010).
  • Moretti, Franco. Graphs, Maps, Trees : Abstract Models for Literary History. London: Verso, 2007.
  • Mueller, Martin. “Digital Shakespeare, or towards a Literary Informatics.” Shakespeare 4, no. 1 (2008): 300–317.
  • Sinclair, Stéfan, Stan Ruecker, and Milena Radzikowska. “Information Visualization for Humanities Scholars.” In Literary Studies in the Digital Age, edited by Kenneth M. Price and Ray Siemens. Modern Language Association of America, 2013.
  • Theibault, John, et al. “See What I Mean? Visual, Spatial, and Game-Based History.” In Writing History in the Digital Age, edited by Kristen Nawrotzki and Jack Dougherty.
  • Tufte, Edward. The Visual Display of Quantitative Information. 2nd ed. Cheshire, Conn.: Graphics Press, 2001.

More Resources and References