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Title: Big data analysis of public library operations and services by using the Chernoff face method
Authors: Kim, Y.S.
Cooke, Louise
Keywords: Public libraries
Chernoff face
Big data analysis
Performance evaluation
Data visualisation
Issue Date: 2017
Publisher: © Emerald
Citation: KIM, Y. and COOKE, L., 2017. Big data analysis of public library operations and services by using the Chernoff face method. Journal of Documentation, 73 (3), pp. 466-480.
Abstract: Purpose – The purpose of this paper is to conduct a big data analysis of public library operations and services of two cities in two countries by using the Chernoff face method. Design/methodology/approach – The study is designed to evaluate library services by analysing the Chernoff face. Big data on public libraries in London and Seoul were collected respectively from CIPFA and the Korean government’s website for drawing a Chernoff face. The association of variables and human facial features was decided by survey. Although limited in its capacity to handle a large number of variables (eight were analysed in this study) the Chernoff face method does readily allow for the comparison of a large number of instances of analysis. 58 Chernoff faces were drawn from the formatted data by using the R programming language. Findings – The study reveals that most of the local governments in London perform better than those of Seoul. This consequence is due to the fact that local governments in London operate more libraries, invest more budgets, allocate more staff and hold more collections than local governments in Seoul. This administration resulted in more use of libraries in London than Seoul. The study validates the benefit of using the Chernoff face method for big data analysis of library services. Practical implications – Chernoff face method for big data analysis offers a new evaluation technique for library services and provides insights that may not be as readily apparent and discernible using more traditional analytical methods. Originality/value – This study is the first to use the Chernoff face method for big data analysis of library services in library and information research.
Description: This article is © Emerald Group Publishing and permission has been granted for this version to appear here (please insert the web address here). Emerald does not grant permission for this article to be further copied/distributed or hosted elsewhere without the express permission from Emerald Group Publishing Limited.
Version: Accepted for publication
DOI: 10.1108/JD-08-2016-0098
URI: https://dspace.lboro.ac.uk/2134/23492
Publisher Link: http://dx.doi.org/10.1108/JD-08-2016-0098
ISSN: 1758-7379
Appears in Collections:Published Articles (Business School)

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