Treffer: Exploring Patron Behavior in an Academic Library: A Wi-Fi-Connection Data Analysis

This paper introduces a Patron Counting and Analysis (PCA) system that leverages Wi-Fi-connection data to monitor space utilization and analyze visitor patterns in academic libraries. The PCA system offers real-time crowding information to the public and a comprehensive visitor analysis dashboard for library administrators. The system's development was driven by the need for occupancy restrictions during the pandemic, ensuring a spacious environment for library visitors as well as balancing between efficient utilization and adhering to social distancing regulations. Traditional methods of patron behavior performance and library spatial analysis, such as manual head counting or card-swiping systems, often incur additional costs for labor, hardware installation, or software subscription. The PCA system, however, utilizes existing Wi-Fi-connection data, providing a cost-effective solution to represent patron demographics and spatial usage. Limitations may arise when patrons do not carry Wi-Fi-enabled devices or during periods of low Wi-Fi service functionality. Implemented in Node.js and integrated with Python Flask framework and related libraries, the PCA system was piloted at the King Library in Miami University, successfully demonstrating a high validity compared to manually collected data. It filters out noise and redundancy, visualizes the occupancy index meter in real time, and generates statistical reports by linking user IDs with demographic information. The PCA system's reliability was validated through manually head counting data collected at the King Library in Miami University, establishing it as a reliable tool for library space management and patron analysis.

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