This is the network that I explored in my blog. It contains several social connections of the Grand Rapid Elites in the reform period. I mainly explored the women’s club, which contains information about the Grand Rapids Federation of Women’s Club’s leadership. In the dynamic network visualization, the center node is the women’s club and it is surrounded by nodes that represents different women back then. The edges means known affiliations between the club and women. The nodes are categorized into different groups, each group has a unique color. The color of the nodes is identical to its group color. Different colors represent different modularity class, and different sizes are determined by eigenvector centrality.
The project allows you to click on each node and learn the women’s name, modularity class, eigenvector centrality and her connections. Viewers are also be able to search names that they are interested, and filter by groups to see women that have the same modularity class. The visualization doesn’t have much interaction involved, since it is not a big dataset, but the amount of information it gives for each node already includes everything it could include. I think viewers can learn this network quite efficiently since there’s not much information involved for each data, and the center for this data is just the women’s club. Meanwhile, since the interaction is not fancy, it might decreases viewers’ engagement in learning the project.
The project was created based on the membership information “from a 1914 directory at the Grand Rapids Public Library’s Federation of Women’s Literary Club’s records” (Sarnacki). The tool that was used to clean the data is Open Refine and the visualization layout uses Yihan Hu Proportional. The project doesn’t contain any spatial analysis or text mining, as the data is directly given in the form of names, and no such analysis were necessary or required.
