Update visualizations.html

visualizations.html

@@ -57,7 +57,7 @@ <h3 style="text-align: center;">Visualizations</h3>
                         <li>Parse group dynamics and information distribution from a chaotic period of transition.</li>
                         <li>Democratize the pursuit of knowledge and expand the potential for research in this area of research.</li>
                     </ul>
-                 <p>This project is a marriage of early modern Scottish history and computational data science. It brings together methods of network science, prosopography, and traditional early modern political history surrounding communication. The visualizations show a story of connectivity over time.</p>
+                 <p>This project is a marriage of early modern Scottish history and computational data science. It brings together methods of network science, prosopography, and traditional early modern political history surrounding communication. The visualizations show a story of connectivity over time. We chose this particular corpus of letters because <i>Leven and Melville Papers</i> do a very good job at capturing the difficulties in the reconstruction and administration of Scottish governance during such a chaotic period. </p>
                 <h4>Methodology</h4>
                 <p>Phase one required turning qualitative data into quantitative data. We started by recording all of the pertinent data from the PDF version of the Leven and Melville Papers. We then turned this data into spreadsheet data, here we used categories: Id, Sender, Receiver, Location from, Location to, Latitude and Longitude, Type and Date. This allowed us to parse the networking data in the form of nodes and edges. The data was then cleaned using OpenRefine to split up latitude and longitude and keywords into different columns; we also made sure there were no blank tiles and duplicates. After creating a master spreadsheet information file, we then set about creating different sheets for different visualizations including people, places, keywords, nodes, and then edges (relationships). We were able to get all the data from the 599 letters contained in the digitized copy of the Leven and Melville papers into a csv file. Given the large dataset of Network Letters, it allowed for exploratory data analysis and investigation on different digital tools to identify the best representation of the relationships presented in the papers. One of the main tools we ended up using was the programming language Python; which contains a large number of libraries that extend the capabilities of the language, allowing for complex visualizations of the Network Data. One of the most prominent libraries used was networkx, which allowed the creation of network graphs along with the application of the Girvan-Newman algorithm to detect communities within the network. This algorithm works by repeatedly removing edges on the shortest path within the network. Additionally, the nodes are given corresponding colors to highlight their community, enabling an easier identification of groups in the network. The algorithm is important for understanding the network graph because a node with higher betweenness centrality would have more control over the network, due to the fact that more information will pass through that node. The implementation of these libraries and creation of visuals were carried out on Jupyter notebook, which is an open-source software for interactive computing. In addition, we experimented with tools such as Leaflet, Flourish, and Gephi for further analysis of the letters.</p>
                  <h4>Most Connected Nodes</h4>