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.
- A Tour through the Visualization Zoo – useful overview of different types of visualisations (with plenty of illustrations)
- Visualising Data – Andy Kirk’s website, providing an excellent range of resources
- Mia Ridge’s list of Data Visualisation Resources for Scholarly Research – topics include design, critique, and histories of dataviz
- Tooling up for Digital Humanities: Data Visualization – online workshop resource from Stanford University
- Understanding Data Visualisations – introductory resource from the Seeing Data project
- Visualization as a Digital Humanities ________ ? – Presentation: What is visualisation in the digital humanities and how is it used?
- DataVis.ca – a range of resources, gallery, and history of dataviz
- InfoVis:Wiki – a community platform for recent developments and news on Information Visualization.
- HelpMeViz – for “anyone who is searching for feedback on their visualization designs”
Deeper: Critique and Discussion
- The Historian’s Macroscope
- Visualizations and Historical Arguments
- Diggable Data, Scalable Reading and new Humanities Scholarship
- 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)
- 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
- How to explore a network graph of electronic literature in Gephi
- D3.js tutorials from Scott Murray
- An Introduction to D3.js using HTML, Scales and Chili Peppers
- Geospatial Historian – a tutorial-based open access textbook designed to teach practical digital mapping and GIS skills
- bl.ocksplorer.org – a tool to find blocks and gists that contain uses of the d3.js API
Projects and Examples
- Kindred Britain
- Domestic Migration in the 1880 Census
- Visualizing English Print
- Victorian Books
- US Census Data Visualisation Gallery
- Oxford Migration Observatory – data and resources
- 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.