My KIPP Blog – Week 10-11 – Knowledge Integration and Project Planning: Data, Inequality and Society (2022-2023)

In the course of Representing Data, the topic of our group project is visualizing the publishing data of the National Library. Libraries have always been a source of knowledge and culture, but accessing and understanding the vast amounts of data held by these institutions can be a daunting task, particularly for young audiences who may not be familiar with library catalogs or data analysis tools. So our project aims to make national library publishing data more accessible and engaging for younger audiences by using data visualization tools to present the data in intuitive and visually appealing ways.

By using a variety of visualization techniques, we were able to explore the themes, bias, and geographical distribution of the library’s publishing data, providing insight into the cultural and historical significance of the collection. Our analysis revealed several interesting patterns in the data. Word frequency analysis and word clouds showed the most common themes in the books catalogued by the library, and we attempted to identify potential biases in the collection by analyzing the frequency of certain keywords. The pie chart showed the distribution of languages in the Scottish National Library’s catalog, highlighting the diversity of languages represented. The time series frequency chart showed how the number of publications in the library’s collection has changed over time, providing insight into historical trends in publishing. The map of publishing cities showed the geographical distribution of the most prolific publishing cities in the library’s catalog, with London being the city with the highest number of publications. However, there are some limitations to our approach. For example, the location of publishers may not necessarily correspond to the location where the publications were created or distributed, so our map may not be a perfect representation of the data. Additionally, our map was limited to ten locations, and including more locations or using different visualization methods to display the entire dataset could be useful in future work.