Session 7: Data-Driven Space Planning: Using Python to Analyze Collection Growth Through LC Class.. скачать в хорошем качестве

Session 7: Data-Driven Space Planning: Using Python to Analyze Collection Growth Through LC Class..

by Corinne Chatnik and Mukundan Thanigaivelan (Union College) As library collections evolve, understanding spatial growth patterns becomes critical for effective space management and collection development. We will demonstrate how our library leveraged Python programming to analyze Library of Congress subclassification data, comparing acquisitions against weeded materials to predict physical space needs and identify collection imbalances. The workflow begins with data exports from Alma, our library management system, capturing both acquisitions and weeding records with their associated LC classifications. We then, using python and its libraries, normalize classification strings, aggregate additions and withdrawals by subclass, and calculate growth metrics. The resulting data provides our collection development librarian and stacks manager with clear, actionable intelligence. This project exemplifies creative technological solutions to future library challenges by transforming existing bibliographic monograph data into a predictive tool. Rather than requiring expensive software or complex infrastructure, our approach uses accessible open-source tools to solve a universal library problem: making the most of limited physical space in an era of continued collection growth and evolution.