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A geostatistical analysis of geostatistics

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.

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References

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

  2. 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

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

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

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

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

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

  8. 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

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

  10. 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.

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

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

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

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

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

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

  17. 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

  18. 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

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

  20. 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

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

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

  23. 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.

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

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

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

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

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

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Correspondence to Tomislav Hengl.

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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

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Keywords

  • Kriging
  • Citation Rate
  • Water Resource Research
  • Geostatistical Analysis
  • Citation Statistic