The network analysis project that I looked at is a visualization of pathways of runaway slaves during the middle of the 19th century, which use Palladio. This project is presented as an essay, with sections on their rationale, methodology, and conclusions. Screenshots of their network maps are presented as well. The data points that were used were collected from jailers’ notices. There were six data sets that were input into Palladio. Two sets of data were collected from each of these three states: Mississippi, Arkansas, and Texas. One set from each state was collected from all the jailers’ notices for the years 1840-1842. The other three sets were collected from a specific county jail (Bexar, Pulaski, and Jefferson) in each of the three states. The three county jails had roughly the same amount of notices from the late 1850s. The purpose of collecting this data was to visualize the general routess and/or patterns of routes that enslaved people in those countries were using to escape.
This project created a network of routes on a map in these states. Using this display makes it is easier to see the patterns in the slaves’ pathways in relation to cardinal directions in these states. The nodes represent the locations of of the jails where the runaway slaves were captured and the locations of where they were enslaved, as reported by the slaves. Even though there is not knowledge of the exact paths that the slaves took, the edges represent the general trajectory from their enslaved locations to the county jail. The network map uses the “point by point” option in Palladio to connect the slave owner’s location to the county jail. In the first three sets that are a collection of all the notices, there seems to be no easy patterns to decipher. The edges seem to go in several different directions. The network map does include another visual element that is not needed for the latter three sets. The “count by” feature that they employed has larger circles for frequency in location within the set. The latter the data set visualizations have many edges for one node because the data set is collected from one county jail per state. The “count by” feature is not used here because all the data is collected from one place, and therefore all the edges connect to that one node.