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http://dx.doi.org/10.14400/JDC.2022.20.3.041

Analysis of public library book loan demand according to weather conditions using machine learning  

Oh, Min-Ki (Dept. of Library and Information Science, Kyungpook National University)
Kim, Keun-Wook (Big Data Center, Daegu Digital Industry Promotion Agency)
Shin, Se-Young (Dept. of Computer Science, Kyungpook National University)
Lee, Jin-Myeong (Dept. of Physics, Kyungpook National University)
Jang, Won-Jun (Big Data Center, Daegu Digital Industry Promotion Agency)
Publication Information
Journal of Digital Convergence / v.20, no.3, 2022 , pp. 41-52 More about this Journal
Abstract
Although domestic public libraries achieved quantitative growth based on the 1st and 2nd comprehensive library development plans, there were some qualitative shortcomings, and various studies have been conducted to improve them. Most of the preceding studies have limitations in that they are limited to social and economic factors and statistical analysis. Therefore, in this study, by applying the spatiotemporal concept to quantitatively calculate the decrease in public library loan demand due to rainfall and heatwave, by clustering areas with high demand for book loan due to weather changes and areas where it is not, factors inside and outside public libraries and After the combination, changes in public library loan demand according to weather changes were analyzed. As a result of the analysis, there was a difference in the decrease due to the weather for each public library, and it was found that there were some differences depending on the characteristics and spatial location of the public library. Also, when the temperature was over 35℃, the decrease in book loan demand increased significantly. As internal factors, the number of seats, the number of books, and area were derived. As external factors, the public library access ramp, cafe, reading room, floating population in their teens, and floating population of women in their 30s/40s were analyzed as important variables. The results of this analysis are judged to contribute to the establishment of policies to promote the use of public libraries in consideration of the weather in a specific season, and also suggested limitations of the study.
Keywords
Public library; Weather; Factor Analysis; Random Forest; Machine Learning;
Citations & Related Records
Times Cited By KSCI : 7  (Citation Analysis)
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