by Palladio is a data-driven toolset for analyzing relationships across time through graphical interfaces based on humanistic inquiry. The program’s purpose utilizes visualization and analytical tools to help scholarly communities and networks to create a map or graph that connects different elements for their users to understand trends or connections across different periods of time, such as migration or transportation. The program was also made for a project by Stanford students who mapped out early modern communication networks graphically, aiming to connect the centers of Enlightenment thought. Similar to my Voyant and Kepler.gl articles, the same digital history class I took had me work with Palladio to understand its functionality, while also using the program firsthand to map out different interviews of former slaves in different areas across the United States.
To begin, several links provided by the class assignments loaded up different data spreadsheets that held interviews between the formerly enslaved individuals, similar to the spreadsheets I worked with in Kepler.gl, but with a few new additions. Once uploaded in the program, the user is immediately taken to a blank page with a series of tabs at the top. Each tab is divided into layers that can be edited to build up the map or graph the user needs for their project. The layer that popped up for me was specified for the interviews, consisting of some layers we mentioned in the Kepler.gl project (gender, age, etc.) as well as some new layers we utilized in Palladio (topics, type of slave, etc.).
For the assignment, the edits I made resulted in showing different bubble graphs that connected different layers onto common themes they shared. The first graph we utilized connected different interviews and interviewees to the location of the interview. However, many other graphs I created showed larger interconnectivity than the basic graph we created. For example, the IMF graph I created connected the interviewers to the gender of the individuals they interviewed. Another such example is in my TopAge graph, where the age of the interviewees connect with the different topics that were brought up during the interview. Each graph I mentioned were vastly different from the other, especially when it came to interactions and interconnectivity.
To conclude, Palladio is an excellent digital mapping program when trying to connect different datasets onto a graph or map. Despite some similarities to Kepler.gl, Palladio provides a distinct differentiation with how they display their program for its users. The interview assignment really highlighted the strengths of Palladio, although it can get a little hectic at times with the interconnectivity that the graph can assimilate. Overall, I highly recommend Palladio for digital historians who aim to map out any datasets that require interconnectivity of groups of any kind, such as regions, cultures or people.
