The Use of Spatiotemporal Kriging in Academic Research: A Bibliometric Analysis
DOI:
https://doi.org/10.26512/rici.v19.n2.2026.59824Keywords:
Spatiotemporal kriging, Bibliometric analysis , Interpolation, Spatial statistics , Emission trading systemAbstract
Recent advances in capturing spatial data in different periods of time lead to new challenges in predicting missing information from neighboring data, and spatiotemporal kriging is rising as a powerful tool to face this challenge. This research aims to explore the main academic contributions to this statistical technique by performing a bibliometric analysis. From a sample of 953 articles (1990-2022) gathered from the Web of Science database, this study highlights the main contributors on this topic in terms of production and impact (authors, institutions, countries, journals and publishers), and also aims to bring to light the main scientific topics related to spatiotemporal kriging by explore the wording of authors’ keywords, titles and abstracts, and the categories in which the papers analyzed are included. These results lead to an integral view of the state of the art on spatiotemporal kriging, so as to serve as a storefront for researchers who need a comprehensive landscape for this statistical interpolation technique.
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ABU BAKAR, A.; MAHMOOD, N.Z.; YONEDA, M. Bibliometric insights into kriging research from 1980 to 2020: global trends and earth science connections. Earth Science Informatics, v. 19, n. 4, 2026. DOI: https://doi.org/10.1007/s12145-025-02053-y
AGHAEI CHADEGANI, Arezoo; SALEHI, Hadi; MD YUNUS, Melor M.; FARHADI, Hadi; FOOLADI, Masood; FARHADI, Maryam; ALE EBRAHIM, Nader. A comparison between two main academic literature collections: Web of Science and Scopus databases. Asian Social Science, v. 9, n. 5, p. 18-26, 2013. DOI: https://doi.org/10.5539/ass.v9n5p18.
AGYEMAN, Prince Chapman; AHADO, Samuel Kudjo; BORŮVKA, Luboš; BINEY, James Kobina Mensah; SARKODIE, Vincent Yaw Oppong; KEBONYE, Ndiye M.; KINGSLEY, John. Trend analysis of global usage of digital soil mapping models in the prediction of potentially toxic elements in soil/sediments: a bibliometric review. Environmental Geochemistry and Health, v. 43, n. 5, p. 1715-1739, 2021. DOI: https://doi.org/10.1007/s10653-020-00742-9.
ARIA, Massimo; CUCCURULLO, Corrado. Bibliometrix: an R-tool for comprehensive science mapping analysis. Journal of Informetrics, v. 11, n. 4, p. 959-975, 2017. DOI: https://doi.org/10.1016/j.joi.2017.08.007.
BANERJEE, Sudipto; GELFAND, Alan E.; FINLEY, Andrew O.; SANG, Huiyan. Gaussian Predictive Process Models for Large Spatial Data Sets. Journal of the Royal Statistical Society Series B: Statistical Methodology, v. 70, n. 4, p. 825-848, 2008. DOI: https://doi.org/10.1111/j.1467-9868.2008.00663.x.
BOGAERT, Patrick. Comparison of kriging techniques in a space-time context. Mathematical Geology, v. 28, p. 73–86, 1996. DOI: https://doi.org/10.1007/BF02273524.
CARRERA-HERNÁNDEZ, Jaime J.; GASKIN, Susan J. Spatio temporal analysis of daily precipitation and temperature in the Basin of Mexico. Journal of Hydrology, v. 336, n. 3-4, p. 231-249, 2017. DOI: https://doi.org/10.1016/j.jhydrol.2006.12.021.
CHILÈS, Jean-Paul; DELFINER, Pierre. Geostatistics: modeling spatial uncertainty. 2. ed. Hoboken: Wiley, 2012.
CHILÈS, Jean-Paul; DESASSIS, Nicolas. Fifty years of kriging. In: Handbook of Mathematical Geosciences. Cham: Springer International Publishing, p. 589-612, 2018. DOI: https://doi.org/10.1007/978-3-319-78999-6_29.
CHRISTAKOS, George. Modern Spatiotemporal Geostatistics. Oxford: Oxford University Press, 2012.
COSTA, Daniel Fonseca; CARVALHO, Francisval de Melo; MOREIRA, Bruno César de Melo; PRADO, José Willer do. Bibliometric analysis on the association between behavioral finance and decision making with cognitive biases such as overconfidence, anchoring effect and confirmation bias. Scientometrics, v. 111, n. 3, p. 1775-1799, 2017. DOI: https://doi.org/10.1007/s11192-017-2371-5.
CRESSIE, Noel A. C. Statistics for spatial data. Hoboken (NJ): John Wiley & Sons, 1993.
CRESSIE, Noel A. C.; HUANG, Hsin Cheng. Classes of Nonseparable, Spatio-Temporal Stationary Covariance Functions. Journal of the American Statistical Association, v. 94, n. 448, p.1330-1339, 1999. DOI: https://doi.org/10.1080/01621459.1999.10473885.
CRESSIE, Noel A. C.; SHI, Tao; KANG, Emily L. Fixed Rank Filtering for Spatio-Temporal Data. Journal of Computational and Graphical Statistics, v. 19, n. 3, p. 724-745, 2010. DOI: https://doi.org/10.1198/jcgs.2010.09051.
CRESSIE, Noel A. C.; WIKLE, Cristopher K. Statistics for Spatio-Temporal Data. Hoboken (NJ), John Wiley, 2011.
DE IACO, Sandra; MYERS, Donald E.; POSA, Donato. Space–time variograms and a functional form for total air pollution measurements. Computational Statistics & Data Analysis, v. 41, n. 2, p. 311-328, 2002. DOI: https://doi.org/10.1016/S0167-9473(02)00081-6.
DE IACO, Sandra; POSA, Donato. Predicting spatio-temporal random fields: Some computational aspects. Computers & Geosciences, v. 41, p. 12-24, 2012. DOI: https://doi.org/10.1016/j.cageo.2011.11.014.
DONTHU, Naveen; KUMAR, Satish; MUKHERJEE, Debmalya; PANDEY, Nitesh; LIM, Weng Marc. How to conduct a bibliometric analysis: an overview and guidelines. Journal of Business Research, v. 133, p. 285-296, 2021. DOI: https://doi.org/10.1016/j.jbusres.2021.04.070.
FERNÁNDEZ-CASAL, Rubén. Flexible spatio-temporal stationary variogram models. Statistics and Computing, v. 13, n. 2, p. 127-136, 2003. DOI: https://doi.org/10.1023/A:1023204525046.
FORTUNATO, Santo. Community detection in graphs. Physics Reports, v. 486, n. 3-5, p. 75-174, 2010. DOI: https://doi.org/10.1016/j.physrep.2009.11.002.
GERVINI, Daniel. Spatial kriging for replicated temporal point processes. Spatial Statistics, v. 51, p. 100681, 2022. DOI: https://doi.org/10.1016/j.spasta.2022.100681.
GNEITING, Tilmann. Nonseparable, Stationary Covariance Functions for Space-Time Data. Journal of the American Statistical Association, v. 97, n. 1, p. 590-600, 2002. DOI: https://doi.org/10.1198/016214502760047113.
GOOVAERTS, Pierre. Geostatistics for Natural Resource Evaluation. Oxford: Oxford University Press, 1997.
GUNDOGDU, Kemal Sulhi; GUNEY, Ibrahim. Spatial analyses of groundwater levels using universal kriging. Journal of Earth System Science, v. 116, n. 1, p. 49-55, 2007. DOI: https://doi.org/10.1007/s12040-007-0006-6.
HAAS, Timothy C. Local prediction of a spatio-temporal process with an application to wet sulfate deposition. Journal of the American Statistical Association, v. 90, p. 1189–1199, 1995. DOI: https://doi.org/10.1080/01621459.1995.10476625.
HE, Changpei; JI, Mingrui; GRIENEISEN, Michael L.; ZHAN, Yu. A review of datasets and methods for deriving spatiotemporal distributions of atmospheric CO2. Journal of Environmental Management, v. 322, p. 116101, 2022. DOI: https://doi.org/10.1016/j.jenvman.2022.116101.
HE, Qimin; ZHANG, Kefei; WU, Suqin; LIAN, Dajun; LI, Li; SHEN, Zhen; WAN, Moufeng; LI, Longjiang; WANG, Rui; FU, Erjiang; GAO, Biqing. An investigation of atmospheric temperature and pressure using an improved spatio-temporal kriging model for sensing GNSS-derived precipitable water vapor. Spatial Statistics, v. 51, p. 100664, 2022. DOI: https://doi.org/10.1016/j.spasta.2022.100664.
HENGL, Tomislav; HEUVELINK, Gerard B. M.; STEIN, Alfred. A generic framework for spatial prediction of soil variables based on regression-kriging. Geoderma, v. 120, n. 1-2, p. 75-93, 2004. DOI: https://doi.org/10.1016/j.geoderma.2003.08.018.
HENGL, Tomislav; HEUVELINK, Gerard B. M.; ROSSITER, David G. About regression-kriging: from equations to case studies. Computers and Geosciences, v. 33, n. 10, p. 1301-1315, 2007. DOI: https://doi.org/10.1016/j.cageo.2007.05.001.
HENGL, Tomislav; HEUVELINK, Gerard B. M.; TADIĆ, Melita Perčec; PEBESMA, Edzer J. Spatio-temporal prediction of daily temperatures using time-series of MODIS LST images. Theoretical and Applied Climatology, v. 107, n. 1-2, p. 265-277, 2012. DOI: https://doi.org/10.1007/s00704-011-0464-2.
HEUVELINK, Gerard B.M.; GRIFFITH, Daniel A. Space-time geostatistics for geography: A case study of radiation monitoring across parts of Germany. Geographical Analysis, v. 42, n. 2, p. 161-179, 2010. DOI: https://doi.org/10.1111/j.1538-4632.2010.00788.x.
HIRSCH, Jorge E. An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences, v. 102, n. 46, p. 16569-16572, 2005. DOI: https://doi.org/10.1073/pnas.0507655102.
HIRSCH, Jorge E.; BUELA-CASAL, Gualberto. The meaning of the h-index. International Journal of Clinical and Health Psychology, v. 14, n. 2, p. 161-164, 2014. DOI: https://doi.org/10.1016/S1697-2600(14)70050-X.
HUANG, Hsin-Cheng; CRESSIE, Noel A.C. Spatio-temporal prediction of snow water equivalent using the Kalman filter. Computational Statistics & Data Analysis, v. 22, n. 2, p. 159-175, 1996. DOI: https://doi.org/10.1016/0167-9473(95)00047-X.
JIANG, Wenxuan; SOUSA, Paulo S. A.; MOREIRA, Maria R. A.; AMARO, Graça Maciel. Lean direction in literature: a bibliometric approach. Production and Manufacturing Research, v. 9, n. 1, p. 241-263, 2021. DOI: https://doi.org/10.1080/21693277.2021.1978008.
JOURNEL, André G.; HUIJBREGTS, Charles J. Mining geostatistics. Cambridge: Cambridge University Press, 1979.
KILIBARDA, Milan; HENGL, Tomislav; HEUVELINK, Gerald B. M.; GRÄLER, Benedikt; PEBESMA, Edzer; TADIĆ, Melita Perčec; BAJAT, Bransilav. Spatio-temporal interpolation of daily temperatures for global land areas at 1 km resolution. Journal of Geophysical Research: Atmospheres, v. 119, p. 2294–2313, 2014. DOI: https://doi.org/10.1002/2013JD020803.
KLAVANS, Richard; BOYACK, Kevin W. Research portfolio analysis and topic prominence. Journal of Informetrics, v. 11, n. 4, p. 1159-1174, 2017. DOI: https://doi.org/10.1016/j.joi.2017.10.002.
KYRIAKIDIS, Phaedon C.; JOURNEL, André G. Geostatistical Space–Time Models: a Review. Mathematical Geology, v. 31, 651–684 (1999). DOI: https://doi.org/10.1023/A:1007528426688
KLEIJNEN, Jack P. C. Kriging metamodeling in simulation: a review. European Journal of Operational Research, v. 192, n. 3, p. 707-716, 2009. DOI: https://doi.org/10.1016/j.ejor.2007.10.013.
KRIGE, Daniel G. A statistical approaches to some basic mine valuation problems on the Witwatersrand. Journal of the Chemical, Metallurgical and Mining Society of South Africa, v. 52, p. 119-139, 1951. Disponível em: https://journals.co.za/doi/10.10520/AJA0038223X_4792.
LIM, Chae Young; WU, Wei Ying. Conditions on which cokriging does not better than kriging. Journal of Multivariate Analysis, v. 192, p. 105084, 2022. DOI: https://doi.org/10.1016/j.jmva.2022.105084.
LIU, Fenglian; LIN, Aiwen; WANG, Huanhuan; PENG, Yuling; HONG, Song. Global research trends of geographical information system from 1961 to 2010: a bibliometric analysis. Scientometrics, v. 106, p. 751-768, 2016. DOI: https://doi.org/10.1007/s11192-015-1789-x.
MATHERON, Georges. Principles of geostatistics. Economic Geology, v. 58, n. 8, p. 1246-1266, 1963. DOI: https://doi.org/10.2113/gsecongeo.58.8.1246.
MOYEED, Rana A.; PAPRITZ, Andreas. An empirical comparison of kriging methods for nonlinear spatial point prediction. Mathematical Geology, v. 34, n. 4, p. 365-386, 2002. DOI: https://doi.org/10.1023/A:1015085810154.
PEBESMA, Edzer J. Multivariable geostatistics in S: the gstat package. Computers & Geosciences, v. 30, n. 7, p. 683-691, 2004. DOI: https://doi.org/10.1016/j.cageo.2004.03.012.
ROUHANI, Shahrokh; MYERS, Donald E. Problems in Space-Time Kriging of Geohydrological Data. Mathematical Geology, v. 22, n. 5, p. 611-623, 1990. DOI: https://doi.org/10.1007/BF00890508.
SAHU, Sujit K.; MARDIA, Kanti V. A Bayesian kriged Kalman model for short‐term forecasting of air pollution levels. Journal of the Royal Statistical Society Series C, v. 54, n. 1, p. 223-244, 2005. DOI: https://doi.org/10.1111/j.1467-9876.2005.00480.x.
SAMPSON, Paul D.; SZPIRO, Adam A.; SHEPPARD, Lianne; LINDSTRÖM, Johan; KAUFMAN, Joel D. Pragmatic estimation of a spatio-temporal air quality model with irregular monitoring data. Atmospheric Environment, v. 45, n. 36, p. 6593-6606, 2011. DOI: https://doi.org/10.1016/j.atmosenv.2011.04.073.
SHTILIYANOVA, Anastasiya; BELLOCCHI, Gianni; BORRAS, David; EZA, Ulrich; MARTIN, Raphaël; CARRÈRE, Pascal. Kriging-based approach to predict missing air temperature data. Computers and Electronics in Agriculture, v. 142, p. 440-449, 2017. DOI: https://doi.org/10.1016/j.compag.2017.09.033.
SIDERIS, Ioannis V.; GABELLA, Marco; ERDIN, Raimund; GERMANN, Urs. Real-time radar–rain-gauge merging using spatio-temporal co-kriging with external drift in the alpine terrain of Switzerland. Quarterly Journal of the Royal Meteorological Society, v. 140, 1097-1111, 2014. DOI: https://doi.org/10.1002/qj.2188.
SINGH, Vivek Kumar; SINGH, Prashasti; KARMAKAR, Mousumi; LETA, Jacqueline; MAYR, Philipp. The journal coverage of Web of Science, Scopus and Dimensions: a comparative analysis. Scientometrics, v. 126, n. 6, p. 5113-5142, 2021. DOI: https://doi.org/10.1007/s11192-021-03948-5.
SNEPVANGERS, Judith J. J. C.; HEUVELINK, Gerard B. M.; HUISMAN, Johan A. Soil water content interpolation using spatio-temporal kriging with external drift. Geoderma, v. 112, n. 3-4, p. 253-271, 2003. DOI: https://doi.org/10.1016/S0016-7061(02)00310-5.
SPADAVECCHIA, Luke; WILLIAMS, Mathew. Can spatio-temporal geostatistical methods improve high resolution regionalisation of meteorological variables? Agricultural and Forest Meteorology, v. 149, n. 6-7, p. 1105-1117, 2009. DOI: https://doi.org/10.1016/j.agrformet.2009.01.008.
WACKERNAGEL, Hans. Multivariate geostatistics. Berlin: Springer, 1998.
WALTMAN, Ludo; VAN ECK, Nees Jan; NOYONS, Ed C. M. A unified approach to mapping and clustering of bibliometric networks. Journal of Informetrics, v. 4, n. 4, p. 629-635, 2010. DOI: https://doi.org/10.1016/j.joi.2010.07.002.
WALTMAN, Ludo; NOYONS, Ed. Bibliometrics for research management and research evaluation: a brief introduction. Leiden: CWTS, Leiden University, 2018. Disponível em: https://www.cwts.nl/pdf/CWTS_bibliometrics.pdf.
WIKLE, Christopher K., CRESSIE, Noel A. C. A dimension-reduced approach to space-time Kalman filtering. Biometrika, v. 86, n. 4, p. 815–829, 1999. DOI: https://doi.org/10.1093/biomet/86.4.815.
XIAO, Haiping; ZHANG, Zhenchao; CHEN, Lanlan; HE, Qimin. An improved spatio-temporal kriging interpolation algorithm and its application in slope. IEEE Access, v. 8, p. 90718-90729, 2020. DOI: https://doi.org/10.1109/ACCESS.2020.2994050.
YU, Hwa-Lung; CHEN, Jiu-Chiuan; CHRISTAKOS, George; JERRETT, Michael. BME Estimation of Residential Exposure to Ambient PM10 and Ozone at Multiple Time Scales. Environmental Health Perspectives, v. 117, p. 537-544, 2009. DOI: https://doi.org/10.1289/ehp.0800089.
ZIMMERMAN, Dale; PAVLIK, Claire; RUGGLES, Amy; ARMSTRONG, Marc P. An experimental comparison of ordinary and universal kriging and inverse distance weighting. Mathematical Geology, v. 31, n. 4, p. 375-390, 1999. DOI: https://doi.org/10.1023/A:1007586507433.
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