A calculator to allocate library shelf space, as informed by historic subclass expansion, using Alma Analytics reports and space measurements
This project is designed to calculate and manage the space utilization of library shelves in a way that is informed by the expansion of each subclass, or part of a subclass, over a defined time period. It includes tools for measuring and analyzing the total, occupied, and free space on library shelves and for calculating free space necessary for expansion based on historic acquisition patterns.
- Input total and occupied shelf space to calculate free space for the an entire library, room or section, as well as for every single shelf, allocating different percentages of free space in each Library of Congress subclass or part of subclass based on projected growth.
- Data processing for shelf maps and subclass data using Pandas.
- Designed to run in a Colab environment.
To run this script, you need to have Python installed along with the following libraries:
- Pandas
You can install the required libraries using pip:
pip install pandas- Prepare the data input files (see Data Preparation).
- Ideally, import this repository into Google Colab as a new notebook.
- Create a folder structure in Colab with the path
content/files/inputs. - Upload the necessary data files (
shelf_map.xlsxandsubclass_data.xlsx) into theinputsfolder. - Run the script. The space allocations will be saved as an Excel file at content/files/outputs.
- Download the Excel file 'outputs'.
- Prepare the shelf map and save it as
shelf_map.xlsx. The file should contain columns for shelf ID and shelf length (cm). In our usage the data file shelf_map originates from a mapping of the banks of shelving in the relevant section of the library. - Prepare the subclass data and save it as
subclass_data.xlsx. The file should contain columns for subclass and growth as a proportion of all volumes from FY20-24, and a column "total occupied" with cm occupied by each subclass. In our usage the data file subclass_data originates in an Alma Analytics report on historical acquisitions patterns broken down by subclass (see the Analytics XML file), integrated with up-to-date measurements of the linear cm occupied by each subclass.
Sample Excel files are provided.
The accuracy of the space calculations depends on the accuracy of the lengths and numbers of shelves and the measurements of occupied space. Ensure that the data files are correctly formatted and contain accurate information.
Contributions are welcome! Please feel free to submit a Pull Request.
This project is licensed under the MIT License - see the LICENSE file for details.
- The script was automatically generated by ChatGPT, further developed in Colab and manually tweaked for accuracy and to reflect the conceptualisation of an acquisitions history-based space allocation policy by donnellop. The Readme file was created in conjunction with ChatGPT.