Building on the work from module 6 to understand data, this module explores how to interrogate, analyze, and create data visualizations for purposes of research and for argumentation.
Outcomes
- Increased understanding of different styles of visualizations and their value for research and presentation.
- Increased understanding of data required to build different types of visualization.
- Ability to create at least two types of visualizations using free online tools, Plotly and Palladio.
Readings
- Gibbs, Frederick W. “New Forms of History: Critiquing Data and Its Representations.” The American Historian, 2016. http://tah.oah.org/february-2016/new-forms-of-history-critiquing-data-and-its-representations/. ♦ Estimated Read Time = 9 minutes
- Graham, Shawn, Ian Milligan, and Scott Weingart. “Principles of Information Visualization.” In The Historian’s Macroscope: Big Digital History, Pre-Draft. London: Imperial College Press, 2015. http://www.themacroscope.org/?page_id=469. ♦ Estimated Read Time = 30 minutes
- Bailey, Jefferson and Lily Pregill. “Speak to the Eyes: The History and Practice of Information Visualization.” December 20, 2012. http://www.jeffersonbailey.com/speak-to-the-eyes-the-history-and-practice-of-information-visualization/ ♦ Estimated Read Time = 30 minutes
- Klein, Lauren F. “The Image of Absence: Archival Silence, Data Visualization, and James Hemings.” American Literature Volum 85, Number 4, December 2013. DOI 10.1215/00029831-2367310. http://americanliterature.dukejournals.org/content/85/4/661.abstract ♦ Estimated Read Time = 20 minutes
Discussion Questions
- Gibbs discusses the importance of data fluency, particularly to facilitate analytical reading of visualizations. Did, or does, your graduate program include training in reading and critiquing visual representations of information? How might you teach this skill to others, whether students or peers?
- Based on the readings, particularly Gibbs and Graham, Milligan, & Weingart, what types of visualizations would best work with data sets found in your field? Which would be the most inappropriate or unintelligible visualization?
- What omissions, such as Klein’s archival silences, might visualization of your data sets reveal?
- Do you see visualization more useful to your data and/or field as a tool for discovery early in the process or as a means of communicating findings and argument?
Resources:
What type of visualization is best to communicate your argument in data? Review this visualization: Agarwal, Amit. “Choose the Right Chart Type for Your Data.” Digital Inspiration. Accessed November 2, 2016. http://www.labnol.org/software/find-right-chart-type-for-your-data/6523/.
Explore the visualization tool Tableau by browsing the gallery of visualizations made with it: https://public.tableau.com/en-us/s/gallery. Tableau offers desktop applications which generate interactive visualizations from spreadsheets (including csv, xlsx, and GoogleSheets) and json files, and offers options for embedding these visualizations on the web. There is a free public version as well as versions for students and instructors at accredited institutions.
If you are planning to visualize data, review the following questions to ask about your visualization.
Activity: Plotly
Follow the tutorial on this page to learn the basics of creating bar charts and scatter plots with Plotly, a web-based tool for creating data-based visualizations.
Activity for Creating a Network Visualization:
Read and work through the in-depth tutorial by Marten Düring, “From Hermeneutics to Data to Networks: Data Extraction and Network Visualization of Historical Sources,” available in the Programming Historian.
According to Düring, “network visualisations helped me to discover hitherto forgotten yet highly important contact brokers, highlight the overall significance of Jewish refugees as contact brokers and generally to navigate through a total of some 5,000 acts of help which connected some 1,400 people between 1942 and 1945.”
The tutorial discusses how to create an appropriate data set to represent social networks, offers sample data, and then walks through the steps of visualizing those networks using Palladio.
Project Lens
Kieran Healy’s visualization “A Co-Citation Network for Philosophy”