Comparative analysis of open government data topics and usability

• Published in: Quality & quantity Vol. 57; no. 6; pp. 5655 - 5671
• Main Author: Cho, Jane
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.12.2023; Springer; Springer Nature B.V
• Subjects: Central government; Clustering; Comparative analysis; Crime; Data; Factories; Health care; Health services; Higher education; Information; Local government; Methodology of the Social Sciences; Network analysis; Real estate; Regions; Small business; Social Sciences; Usability; Web portals; Open government data; Pathfinder network; Korean Government; Subject clusters; Usability
• ISSN: 0033-5177; 1573-7845
• DOI: 10.1007/s11135-023-01630-x

This study identifies differences in the content of open public data managed by the central government, local governments, public institutions, and the office of education in Korea through keyword network analysis. Pathfinder network analysis was performed by extracting keywords assigned to 1,200 data cases, open to the Korean Public Data Portals. Subject clusters were derived for each type of government and their utility was compared using download statistics. Eleven clusters were formed for public institutions with specialized information on national issues such as Health care and Real estate , while 15 clusters were formed for the central government with national administrative information, including Crime and Safety policing . Local governments and offices of education were assigned 16 and 11 topic clusters respectively, with data focusing on regional life such as Local factories and manufacturing, Resident registration , and Lifelong education. Usability was higher in public and central governments that deal with national-level specialized information than for regional-level information. It was also confirmed that subject clusters such as Health care, Real estate, and Crime showed high usability. Furthermore, there was a large gap in data utilization because of the existence of popular data that showed extremely high usage.