Skip to main content
Log in

A geostatistical analysis of geostatistics

  • Published:
Scientometrics Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Bar-ilan J. (2008) Which h-index? – A comparison of WoS, Scopus and Google Scholar, Scientometrics. Scientometrics 74((2): 257–271

    Article  Google Scholar 

  • Batista P.D., Campiteli MG., Kinouchi O., Martinez A.S. (2006) Is it possible to compare researchers with different scientific interests?. Scientometrics 68((1): 179–189

    Article  Google Scholar 

  • Bauer K., Bakkalbasi N. (2005) An examination of citation counts in a new scholarly communication environment. D-Lib Magazine 11((9): 1–4

    Article  Google Scholar 

  • Bihui J. (2007) The AR-index: complementing the h-index. ISSI Newsletter 3((1): 1–6

    Google Scholar 

  • Cressie N. (1990) The origins of kriging. Mathematical Geology 22((3): 239–252

    Article  MATH  MathSciNet  Google Scholar 

  • Cressie N.A.C. (1993) Statistics for Spatial Data, revised edition. John Wiley & Sons, New York

    Google Scholar 

  • Diggle, P. J. (2003), Statistical Analysis of Spatial Point Patterns. A Hodder Arnold Publication.

  • Doll C.N.H., Muller J.P., Morley J.G. (2006) Mapping regional economic activity from night-time light satellite imagery. Ecological Economics 57((1): 75–92

    Article  Google Scholar 

  • Eastman J.R., Fulk M. (1993) Long sequence time series evaluation using standardized principal components. Photogrammetric Engineering and Remote Sensing 59((8): 1307–1312

    Google Scholar 

  • Gandin, L. S. (1963), Objective Analysis of Meteorological Fields. translated from Russian in 1965 by Israel Program for Scientific Translations, Jerusalem, Gidrometeorologicheskoe Izdatel’stvo (GIMIZ), Leningrad.

  • Giles J. (2005) Start your engines. Nature 438((1): 554–555

    Article  Google Scholar 

  • Harzing, A. W., Van der Wal, R. (2008), Google Scholar: the democratization of citation analysis? Ethics in Science and Environmental Politics, in press.

  • Isaaks E.H., Srivastava R.M. (1989) Applied Geostatistics. Oxford University Press, New York

    Google Scholar 

  • Jacsó P. (2005) Google Scholar: the pros and the cons. Online Information Review 29((2): 208–214

    Article  Google Scholar 

  • Journel A.G. (1986) Mining geostatistics. Mathematical Geology 18: 119–140

    Article  MathSciNet  Google Scholar 

  • Journel A.G., Huijbregts C.J. (1978) Mining Geostatistics. Academic Press, London

    Google Scholar 

  • Kousha K., Thelwall M. (2007) Google Scholar citations and Google Web/Url citations: A multidiscipline exploratory analysis. Journal of the American Society for Information Science and Technology 5((7): 1055–1065

    Article  Google Scholar 

  • Lasaponara R. (2006) On the use of principal component analysis (PCA) for evaluating interannual vegetation anomalies from SPOT/VEGETATION NDVI temporal series. Ecological Modelling 194((4): 429–434

    Article  Google Scholar 

  • Matheron, G. (1962), Traité de géostatistique appliquée, Mémoires du Bureau de Recherches Géologiques et Mini籥s, Vol 14. Editions Technip, Paris.

  • Meho L.I., Yang K. (2007) A new era in citation and bibliometric analyses: Web of Science, Scopus, and Google Scholar. Journal of the American Society for Information Science and Technology 58: 1–21

    Article  Google Scholar 

  • Minasny B., Hartemink A.E., Mcbratney A. (2007) Soil science and the h index. Scientometrics 73((3): 257–264

    Article  Google Scholar 

  • Noruzi A. (2005) Google Scholar: The new generation of citation indexes. LIBRI 55((4): 170–180

    Article  Google Scholar 

  • Piwowar, J. M., Millward, A. A. (2002), Multitemporal change analysis of multispectral imagery using principal components analysis. In: Geoscience and Remote Sensing Symposium IGARSS ’02, vol 3, IEEE International, pp. 1851–1853.

  • Roediger H.L. (2006) The h index in Science: A new measure of scholary contribution. The Academic Observer 19: 1–4

    Google Scholar 

  • Stein M.L. (1999) Interpolation of Spatial Data: Some Theory for Kriging. Series in Statistics, Springer, New York

    MATH  Google Scholar 

  • Webster R., Oliver M.A. (2007) Geostatistics for Environmental Scientists. Statistics in Practice Wiley, Chichester

    Book  MATH  Google Scholar 

  • Youden W.J. (1951) Statistical Methods for Chemists. John Wiley & Sons, New York

    Google Scholar 

  • Zhou F., Huai-cheng G., Yun-shan H., Chao-zhong W. (2007) Scientometric analysis of geostatistics using multivariate methods. Scientometrics 73: 265–279

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tomislav Hengl.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hengl, T., Minasny, B. & Gould, M. A geostatistical analysis of geostatistics. Scientometrics 80, 491–514 (2009). https://doi.org/10.1007/s11192-009-0073-3

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-009-0073-3

Keywords

Navigation