Tag Archives: data analysis

Visual Tools: Voyant, CartoDB, and Palladio

New web-based, open source technology has dramatically shifted the landscape of digital humanities. It has affected fields related to  digital humanities in two significant ways. For institutions and digital humanists a new quest to create, build, and host project sites has emerged. These digital projects allow users to interact and manipulate data in specific ways that yield almost infinite combinations. For users, these digital projects have laid the groundwork for moving research beyond the archive and to digest and draw conclusions based on datasets and information expressed through new macro-based visuals. The projects/programs reviewed here focus on textual analysis, geospatial mapping, and visual graphing based on large sets of metadata and archival information.

Strength/Weakness: The strength of Voyant is the range of text analysis provided: cirrus, networks, graphs, context, verbal patterns. This is also its weakness. At first glance it’s very impressive but when trying to set or manipulate certain features available to the user for the purposes of customization or multiple datasets, the program does not function well.
Similarity/Difference: Voyant is similar to CartoDB and Palladio in that they are all free open-source, web-based programs. Voyant and Palladio do not require usernames or passwords. Voyant is different from CartoDB because CartoDB does require a sign-up. Voyant is different from Palladio because Voyant has one main screen with several visual fields, while Palladio only focuses on one type of visual analysis at a time, i.e. maps or graphs.
Complement: Voyant provides sophisticated text analysis and CartoDB provides sophisticated geographical analysis. Paired together, they provide unbelievably rich yet simple ways to “see” data relationships. Palladio and Voyant complement one another because they allow users to layer and filter the same data to produce different types of word graphs, clouds, and networks.

Strength/Weakness: The strength of CartoDB is the visual clarity and graphic options for its maps. The program’s weakness is that it really only serves to create maps and not graphs or other visual organizers. As a side note, this could just as easily be a strength because it does one thing well.
Similarity/Difference: CartoDB is similar to Palladio in that it focuses on one type of visualization, which it does very well. It is different in that its foci are  maps=CartoDB and graphs=Palladio. CartoDB is similar to Voyant on a basic level; they both produce visual graphic representations of the relationships within a large set of data. They are different because Voyant attempts to do many things (but not geospatial mapping), while CartoDB focuses on geography and not text.
Complement: CartoDB and Voyant complement each other well for the same reasons that they differ (above). Voyant does what CartoDB does not and vice versa, so together they provide an even more comprehensive picture of patterns that can be draw from data. Palladio and CartoDB complement one another because each does a different thing well. I would be tempted to use these two rather than Voyant because they are both user friendly.

Strength/Weakness: The strength of Palladio is its relatively easy interface and the ability to drag and organize nodes and lines. The weakness of Palladio is the inability to save projects in formats other than svg or json, and that beyond the visual graphing network there is no additional information.
Similarity/Difference: It is similar to CartoDB in that it does have a map function, but Palladio is different because the most effective feature is visual network graphs. Palladio is similar to Voyant in that they both have word links and network features. They are different because Voyant is difficult to use (because of glitches not design), while Palladio is much easier to use.
Complement: Palladio complements Voyant by providing more options for word clouds and visual networks. Palladio provides a complement for CartoDB as they are both based on layering datasets manually with selecting different modes and filters.

As these open-source programs continued to “hone their skills” and “work out the kinks,”  they will no doubt provide continued and enhanced methods of data analysis that can be customized for and by individual interests.

Palladio Reflection


Palladio is  a new web-based platform for the visualization of complex, multi-dimensional data created and maintained by the Research Lab at Stanford University called Humanities + Design.  As a side note, it looks like the lab has just produced another free digital tool, Breve: http://breve.designhumanities.org/.

Stanford is making big strides in the field of digital humanities, but more importantly free and web-based, in other words it does not required downloaded software or paid subscriptions, membership, etc. In many ways, Palladio is the first step toward opening data visualization to “any researcher” by making it possible to upload data and visualize within the browser “without any barriers.” There is no need to create an account and they do not store the data.  Palladio also offers several video tutorials are available an a sample dataset to try out.

1) New users should begin on the homepage where there is an inviting and obvious “Start” button. The next page allows the input of data using a drop method rather than the typical file upload.
2) Once the original data loads — a primary table is generated that breaks down the information by category (as listed in original metadata). From here the user can edit and add layers by clicking on the categories and uploading additional data sets.
3) After all data has been entered, users can go to map or graph in the top left hand corner depending on the type of desired visualization.
4) Palladio is not primarily intended for use as a geo-spatial service but it does provide some mapping which allows users to see the geographical distribution of data.
5) Perhaps its most impressive function is as a graphing tool that can be manipulated to show any given combinations of relationships using options found in the settings. The most important categories to consider are “Source” and “Targets” as this creates the base nodes (circles) and the connective data web.
6) There are additional filter and what Palladio calls “Facets” that allow the user to further filter/organize information based on sub-categories found within the data as well as a timeline function, which for our activity was not a factor.
7) Finally, when the graph is complete and organized as the user would like, there is a quick and easy download option to SVG format. It would seem that a jpeg option would also make the platform more user-friendly.

Unfortunately, in its quest and success as an open-source program, it limits the user in saving and/or sharing visualizations. For example, you can download json or svg but there is no sharable link or embed option (that I can tell). An embed code to add interactive graphs to this blog entry for example would have been great. Still, Palladio and other web-based, open-source, user-friendly programs such as this are going to be a gamechangers not only for digital history or digital humanities but for academic research, publication, and pedagogy on secondary, undergraduate, and graduate levels.