Paper Info
Title
Big data analysis of public library operations and services by using the Chernoff face method.
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 analyzing the Chernoff face. Big data on public libraries in London and Seoul were collected, respectively, from Chartered Institute of Public Finance and Accountancy 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 analyzed in this study) the Chernoff face method does readily allow for the comparison of a large number of instances of analysis. A total of 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 - The 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.
Year
DOI
Venue
2017
10.1108/JD-08-2016-0098
JOURNAL OF DOCUMENTATION
Keywords
Field
DocType
Performance,Evaluation,Libraries,Data,Visualization,Chernoff
Data science,World Wide Web,Data visualization,R Programming Language,Computer science,Chernoff face,Visualization,Originality,Public finance,Library science,Big data,Government
Journal
Volume
Issue
ISSN
73.0
3.0
0022-0418
Citations 
PageRank 
References 
0
0.34
1
Authors
2
Name
Order
Citations
PageRank
Seok Young Kim122.38
Louise Cooke2245.31