So, not only does Palladio do this cool thing with mapping data and showing the relationships it may have with a specific location, but it can also reveal relationships between the data itself. In particular, the datasets we used in class provided information about people who helped Jewish people in the midst of World War II. The dataset revealed information about what kind of help was given or received, who received or gave it, and details about the people themselves, such as their sex, whether they were Jewish or not, and the relationship they may have had with the people they were giving/receiving help to/from. Looking at the datasets themselves seemed a little confusing at first, as it was just a Google table with two pages of lists giving information on people and their relations.
Starting off with this data in Palladio, I looked at the relationships between the givers and receivers of help. After uploading the datasets and forming a link between both tables, I was able to produce this network graph, detailing the flow of help (that is, who was receiving help and who was giving it).Now, this is just a simple network graph, but seeing it immediately gives insight into the relationship between the people listed. I put “Giver” as the source and “Recipient” as the target, highlighting “Recipient” so that it would provide some contrast. The darker circles are the highlighted ones, and shows that some people who received help also gave help to others. When I highlighted “Givers” instead of “Recipients”, this revealed the very same, that the majority of the listed people were givers, as seen below:
Palladio has several filtering features, a facet filter that can show what forms of help was given or received (among others, such as date of first meeting, sex, NS race status, etc.) or choosing a timespan or timeline to see when the relationships may have taken place. These filters effectively narrow down the kind of data you’re looking at, and provides more details about what the graph is showing.
I looked at the facet filter, and under the “Dimensions” tab, I put “Form of Help”. After that, I chose a number representing one of the multiple forms of help that was given/received, and Palladio instantly mapped who may have been involved in this sort of network. Keeping the “Recipient” target nodes highlighted, I was able to see who had received Form of Help #3, and who had given it. As you can see from the graph below, the networks are much smaller and not as connected (as there had been when just graphing “Recipients” and “Givers”), Rita Neumann and Ralph Neumann did not have one person as a mediator for the 3rd form of help, and this particular network of help seemed to occur very separately.
Network visualizations, such as those that can be created with Palladio, are extremely useful when, as Scott Weingart says in his article “Demystifying Networks“, “network studies are made under the assumption that neither the stuff nor the relationships are the whole story on their own.” This means that networks are typically used to show the interdependency of certain factors (like the people and types of relationships in the dataset I used on Palladio), rather than giving the implication that they are independent, thus giving a special emphasis on the relationships between these factors. Weingart proceeds to say that there is a variety of relationships that can be shown in a network visualization, and that they can be indicated with different kinds of “edges” (links), whether they be curved or straight, the edges will indicate a specific relationship between the objects at each node. However, Palladio does not seem to utilize curved edges, and so, by choosing to view a specific relationship, it has to be done using a facet filter. Altogether, Weingart shows in his article just how much information can be portrayed in a network graph, and how it can be done.
On the other hand, in the other reading we had, “Using Metadata to Find Paul Revere” by Kieran Healy, networks can be formed without looking directly at the relationships that may have formed between specific people. Healy gives the example of looking at metadata that provided information on several US Founding Father’s memberships to different organizations, and from there, being able to see who may have been in the same organization, and thus whether they knew each other or not. Just by looking at the metadata and organizing it into a structure that indicated membership, Healy and his/her fellow researchers were able to find possible relationships and networks between these people. Additionally, Healy formed a chart of how many people each organization had in common, and thus was able to see what organizations may be linked to each other through its members. Healy’s research also indicated how much a visual network graph can reveal about relationships, not only between people, but between organizations or institutions. All of this was done without the relationships just being provided, they were formed by conclusions and contributed to an indication of greater relations occurring through the people.
All-in-all, network graphs are extremely useful in showing relationships and discovering new ones, as evidenced with Palladio, Weingart, and Healy. These network visualizations provide information about the relationships, such as how they were formed or what kind of relationship it was, but it can also lead to new revelations about what else may be interconnected through those people or objects.
apping feature, the IU archives number of each photo was mapped using geocoordinates. This map indicates where each photo was taken, using the IU archives number. To get to this map, I uploaded the Cushman .csv file into the Google app, clicked on the “Map of City and State” feature, and made sure the location was set to geocoordinates.
dataset. In the map I created on Palladio, I mapped the location by genre, rather than by the IU archive number. It looks pretty similar to the Google Fusion Tables’ map, but the Palladio map was much more fun to play with. Hovering the mouse over each dot on the map reveals the genre of the photo that was taken at that location, rather than the IU archives number. In order to get this map, it required a little more effort than the Google Fusion Tables, as the dataset itself had to be altered so the date could be correctly recorded on the mapping tool. But once the time zone was deleted from the dataset, the mapping feature wasn’t so complicated.