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A geostatistical analysis of geostatistics

Author

Listed:
  • Tomislav Hengl

    (University of Amsterdam)

  • Budiman Minasny

    (The University of Sydney)

  • Michael Gould

    (Michael Gould Associates)

Abstract

The bibliometric indices of the scientific field of geostatistics were analyzed using statistical and spatial data analysis. The publications and their citation statistics were obtained from the Web of Science (4000 most relevant), Scopus (2000 most relevant) and Google Scholar (5389). The focus was on the analysis of the citation rate (CR), i.e. number of citations an author or a library item receives on average per year. This was the main criterion used to analyze global trends in geostatistics and to extract the Top 25 most-cited lists of the research articles and books in geostatistics. It was discovered that the average citation rate for geostatisticians has stabilized since 1999, while the authors’ n-index seems to have declined ever since. One reason for this may be because there are more and more young authors with a lower n-index. We also found that the number of publications an author publishes explains only 60% of the variation in the citation statistics and that this number progressively declines for an author with a lower number of publications. Once the geographic location is attached to a selection of articles, an isotropic Gaussian kernel smoother weighted by the CR can be used to map scientific excellence around the world. This revealed clusters of scientific excellence around locations such as Wageningen, London, Utrecht, Hampshire, UK, Norwich, Paris, Louvain, Barcelona, and Zürich (Europe); Stanford, Ann Arbor, Tucson, Corvallis, Seattle, Boulder, Montreal, Baltimore, Durham, Santa Barbara and Los Angeles (North America); and Canberra, Melbourne, Sydney, Santiago (Chile), Taipei, and Beijing (other continents). Further correlation with socio-economic variables showed that the spatial distribution of CRs in geostatistics is independent of the night light image (which represents economic activity) and population density. This study demonstrates that the commercial scientific indexing companies could enhance their service by assigning the geographical location to library items to allow spatial exploration and analysis of bibliometric indices.

Suggested Citation

  • Tomislav Hengl & Budiman Minasny & Michael Gould, 2009. "A geostatistical analysis of geostatistics," Scientometrics, Springer;Akadémiai Kiadó, vol. 80(2), pages 491-514, August.
    • Handle: RePEc:spr:scient:v:80:y:2009:i:2:d:10.1007_s11192-009-0073-3
    DOI: 10.1007/s11192-009-0073-3
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    References listed on IDEAS

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    Cited by:

    1. Copiello, Sergio, 2019. "Peer and neighborhood effects: Citation analysis using a spatial autoregressive model and pseudo-spatial data," Journal of Informetrics, Elsevier, vol. 13(1), pages 238-254.
    2. Csomós, György & Tóth, Géza, 2016. "Exploring the position of cities in global corporate research and development: A bibliometric analysis by two different geographical approaches," Journal of Informetrics, Elsevier, vol. 10(2), pages 516-532.
    3. Richard S.J. Tol, 2013. "Measuring catch-up growth in malnourished populations," Working Paper Series 6013, Department of Economics, University of Sussex Business School.
    4. Tol, Richard S.J., 2013. "Identifying excellent researchers: A new approach," Journal of Informetrics, Elsevier, vol. 7(4), pages 803-810.
    5. Javier X Leon & Gerard B M Heuvelink & Stuart R Phinn, 2014. "Incorporating DEM Uncertainty in Coastal Inundation Mapping," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-12, September.

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