As library collections grow over time, understanding collection growth patterns becomes very important for physical space management on our library shelves. In collaboration with faculty at Content & Digital Library Systems in Schaffer Library, I wanted to look at ways to provide stacks managers and collection development librarians actionable insight that would inform them on how to purchase strategically in consideration with the physical library space available. Since the books are organized using the Library of Congress (LC) classification system, I used Python to analyze LC classification data for Schaffer Library acquisitions from 2022 onwards. I parsed raw acquisition data from Alma, aggregated counts for each LC subclass, and created color-coordinated bar graphs for each LC class to visualize growth patterns. In addition to this visual insight, I defined a comprehensive growth index for each LC subclass based on a couple of different growth metrics. When presenting my work to the stacks manager and collection development librarian at Schaffer Library, they considered the visualizations to be very helpful in quickly gaining an overview of each LC class, and that ranking LC subclasses by a growth index was helpful in picking out the largest growth hotspots. I'm currently working on creating a robust interface for librarians to utilize to run these reports and gain clear intelligence. We hope that this project will solve a widely prevalent library problem: making the most out of existing library space in an era of continued collection growth.
Acknowledgements: I would like to thank all the faculty in Content & Digital Library Systems at Schaffer Library for their continual constructive feedback! I want to especially thank Corinne Chatnik for her close guidance in helping shape this project in the best way possible and offering me this opportunity.