3. Information Visualization

“Data visualization” covers all kinds of visualizations. This chapter serves as an introduction to visualizing data, with all of the benefits and drawbacks of turning information into graphics, both practically and intellectually. While mapping is a kind of visualization as well, the next chapter is specifically dedicated to geospatial data.

3.1 Readings

Drucker, DH101 Coursebook, Lessons 4B and 5A

Drucker, Johanna. “Graphesis.” paj:The Journal of the Initiative for Digital Humanities, Media, and Culture 2, no. 1 (December 10, 2010).

Drucker, Johanna. Graphesis: Visual Forms of Knowledge Production (Harvard, 2014).

Drucker, Johanna, “Humanities Approaches to Graphical Display,” DHQ, 2011, Vol.5, No. 1

Hardin, Maila, Daniel Horn, Ross Perez, and Lori Williams. Which Chart or Graph Is Right for You? Tableau Software, January 2012.

Mamber, Stephen. “Space­Time Mappings as Database Browsing Tools.” In Media Computing, edited by Chitra Dorai and Svetha Venkatesh, 39–55. The Springer International Series in Video Computing 4. Springer US, 2002.

Manovich, Lev. “Trending: The Promises and the Challenges of Big Social Data.” In Debates in the Digital Humanities, edited by Matthew K. Gold, University of Minnesota, 2012.

Marcus, Aaron, and Emilie West Gould. “Crosscurrents: Cultural Dimensions and Global Web User-Interface Design.” Interactions 7, no. 4 (2000): 32–46.

Whitelaw, Mitchell. “Art Against Information: Case Studies in Data Practice.” Fibreculture, no. 11 (2008).

Yau, Nathan. “Representing Data.” In Data Points: Visualization That Means Something, 92–133, 2013.

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3.2 Resources

Visualising Data: For more data visualization tools.

Pick the Right Visualization for Your Data: Basic visualization guidelines that can help you decide what type of visualization will work best for different types of data and how to show data relationships.

Lev Manovich on Cultural Analytics: A great resource for learning more about cultural analytics and information visualization. (Be sure to look at the projects section.)

Design for Information: A nice overview by Isabel Mireilles of the many different kinds of information graphics, with historical information and some very useful design principles.

Micki Kaufman’s workshop on cleaning and visualizing data: Teach yourself several visualization tools by following along with this set of workshop materials.

Data + Design: A Simple Guide to Preparing and Visualizing Information: A book crafted by more than 50 volunteers from 14 countries around the globe with the goal to make it easier for people to tell interactive stories with data.

Network Analysis

“Demystifying Networks,” by Scott Weingart: If you can’t tell a node from an edge, start here. A plain-language introduction to network analysis concepts and vocabulary, with lots of pointers to other resources. See also the course packet for Elijah Meeks’s Introduction to Network Analysis class at the Humanities Intensive Learning and Teaching workshop and Marten Düring’s “Should I Do Network Analysis?” flowchart.

Social Network Analysis Glossary: This glossary will help you understand the basic terms that are used in network analysis.

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3.3 Tutorials

Make a Pivot Table with Excel: A pivot table summarizes data values.

Google Fusion Tables: A great starting point for creating data visualizations, especially for simpler charts, like pie charts and bar charts. You can’t do a ton of customization, but you can produce results with just a few clicks. Here’s a basic tutorial.

Tableau: Has a lot of advanced features and documentation. If you want to install it on your own laptop, choose Tableau Public. This tool can be a little overwhelming at first, but Lynda.com has video tutorials; you might start with “Up and Running with Tableau.”

Visualize This and Data Points, by Nathan Yau: These books are a mix of step-by-step instructions and more general insights on dataviz.

Creating a Network Graph with Gephi: Gephi is a powerful tool for network analysis, but it can be intimidating. It has a lot of tools for statistical analysis of network data — most of which you won’t be using at this stage of your work. This tutorial will walk you through creating a basic network visualization.

Nodes Lists and Edge Lists: To build a network diagram, you need a list of nodes (the individual actors) and a list of edges (the relationships between the actors).

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3.4 Tools


Excel: You can get pretty far with Excel’s built-in charts and graphs. They are not very fancy, but they are quite functional.

RAW: An open web application to create custom vector-based visualizations on top of the amazing D3.js library through a simple interface. You can make some really cool graphs and charts with RAW, and it is easy to embed them in a website.

Palladio: A web-based platform for the visualization of complex, multi-dimensional data. As with ManyEyes, you can upload a dataset and visualize it in a number of different ways. But the kinds of visualizations you can do are somewhat more advanced: you can combine maps and timelines, filter your data, make bimodal network graphs (ManyEyes only lets you do 1-mode), create relationships among tables, and download your data model. This may be the tool to try if you find that ManyEyes can’t quite handle what you want. Palladio is still new, so the documentation isn’t as robust as one might like, but this intro video is very helpful. However, Palladio is designed for the exploration (not necessarily the presentation) of humanities data. So while you can produce some impressive visualizations, you cannot really embed them. You can take screenshots, though. Example.

D3.js: D3.js is a JavaScript library for manipulating documents based on data. D3 helps you bring data to life using HTML, SVG, and CSS.

Viewshare: A free platform for generating and customizing views (interactive maps, timelines, facets, tag clouds) that allow users to experience your digital collections. Developed at the Library of Congress, ViewShare is a tool that allows you to visualize a spreadsheet of data in many different ways, including as a map. Its standout feature, is its faceted browsing capability. You can drill down in your data, much the way you drill down from “shoes” to “women’s shoes” to “pumps” on Amazon.] Example.

Tableau: Open data and explore it with Tableau Desktop Public Edition. Drag and drop to create stunning visualizations with ease.

Plot.ly: A newer, web-based dataviz tool that has a lot of cool features, and it is easy to get started with it. Plot.ly is free for unlimited public charts. Charts can be easily embedded on a website from this platform.

Image Analysis Tools

Image analysis, particularly computerized image recognition, is still fairly crude, but a number of tools do allow you to work with images as data.

Image Plot, Image Montage, Image Slice, and Visual Sense: Created by the Lev Manovich-led Software Studies Initiative, these tools allow you to analyze many images at once. ImagePlot is free software tool that visualizes collections of images and video of any size. It is implemented as a macro which works with the open source image processing program ImageJ.. Image Plot arranges images on axes according to user-defined parameters, Image Montage arrays tiny thumbnails in a grid, Image Slice provides a “slice” view of an image collection, and Visual Sense is an environment for exploring images as plots, bar graphs, and arrays. The Software Studies Initiative has provided fairly robust documentation. See how Manovich and his team have used these tools in the Projects section of the Software Studies Initiative’s site.

Image Quilts: The brainchild of Edward Tufte, Image Quilts is a simple plugin for the Chrome browser that arrays all the images on a given webpage in a grid. Use it with, for example, a Google Image search for “happiness” to see some of the most popular representations of the concept in a neat grid. Great for teaching.

TinEye: Feed TinEye an image and it will scour the web for visually similar images.

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3.5 Projects

1. 10 Best Data Visualization Projects of the Year – 2010

2. Gallery of data visualization, Michael Friendly

3. Gallery of data visualization, Michael Friendly – Timelines

4. Public Secrets

5. Blood Sugar

6. Art as Data as Art

7. Mapping Gothic France

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