Scientometric analysis of geostatistics using multivariate methods

Feng Zhou; Huai-Cheng Guo; Yuh-Shan Ho; Chao-Zhong Wu

doi:10.1007/s11192-007-1798-5

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Scientometrics

Volume/Issue:: Volume 73: Issue 3

Scientometric analysis of geostatistics using multivariate methods

Authors:

Feng Zhou

Feng Zhou College of Environmental Sciences, Peking University, Beijing, P. R. China

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Huai-Cheng Guo

Huai-Cheng Guo

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Yuh-Shan Ho

Yuh-Shan Ho College of Environmental Sciences, Peking University, Beijing, P. R. China

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Chao-Zhong Wu

Chao-Zhong Wu Intelligent Transport System Research Center, Wuhan University of Technology, Wuhan, Hubei P. R. China

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Pages:: 265–279

Online Publication Date:: 04 Aug 2007

Publication Date:: 01 Dec 2007

Article Category:: Research Article

DOI:: https://doi.org/10.1007/s11192-007-1798-5

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Abstract

Multivariate methods were successfully employed in a comprehensive scientometric analysis of geostatistics research, and the publications data for this research came from the Science Citation Index and spanned the period from 1967 to 2005. Hierarchical cluster analysis (CA) was used in publication patterns based on different types of variables. A backward discriminant analysis (DA) with appropriate statistical tests was then conducted to confirm CA results and evaluate the variations of various patterns. For authorship pattern, the 50 most productive authors were classified by CA into 4 groups representing different levels, and DA produced 92.0% correct assignment with high reliability. The discriminant parameters were mean impact factor (MIF), annual citations per publication (ACPP), and the number of publications by the first author, for country/region pattern, CA divided the top 50 most productive countries/regions into 4 groups with 95.9% correct assignments, and the discriminant parameters were MIF, ACCP, and independent publication (IP); for institute pattern, 3 groups were identified from the top 50 most productive institutes with nearly 88.0% correct assignment, and the discriminant parameters were MIF, ACCP, IP, and international collaborative publication; last, for journal pattern, the top 50 most productive journals were classified into 3 groups with nearly 98.0% correct assignment, and its discriminant parameters were total citations, impact factor and ACCP. Moreover, we also analyzed general patterns for publication document type, language, subject category, and publication growth.

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Scientometrics

Print ISSN:: 0138-9130

Online ISSN:: 1588-2861

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Scientometrics
Language	English
Size	B5
Year of Foundation	1978
Volumes per Year	1
Issues per Year	12
Founder	Akadémiai Kiadó
Founder's Address	H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Publisher	Akadémiai Kiadó Springer Nature Switzerland AG
Publisher's Address	H-1117 Budapest, Hungary 1516 Budapest, PO Box 245. CH-6330 Cham, Switzerland Gewerbestrasse 11.
Responsible Publisher	Chief Executive Officer, Akadémiai Kiadó
ISSN	0138-9130 (Print)
ISSN	1588-2861 (Online)