Skip to main content

Advertisement

SpringerLink
Log in

Development and applications of GIS-based spatial analysis in environmental geochemistry in the big data era

  • Review Paper
  • Published:
Environmental Geochemistry and Health Aims and scope Submit manuscript

Abstract

The research of environmental geochemistry entered the big data era. Environmental big data is a kind of new method and thought, which brings both opportunities and challenges to GIS-based spatial analysis in geochemical studies. However, big data research in environmental geochemistry is still in its preliminary stage, and what practical problems can be solved still remain unclear. This short review paper briefly discusses the main problems and solutions of spatial analysis related to the big data in environmental geochemistry, with a focus on the development and applications of conventional GIS-based approaches as well as advanced spatial machine learning techniques. The topics discussed include probability distribution and data transformation, spatial structures and patterns, correlation and spatial relationships, data visualisation, spatial prediction, background and threshold, hot spots and spatial outliers as well as distinction of natural and anthropogenic factors. It is highlighted that the integration of spatial analysis on the GIS platform provides effective solutions to revealing the hidden spatial patterns and spatially varying relationships in environmental geochemistry, demonstrated by an example of cadmium concentrations in the topsoil of Northern Ireland through hot spot analysis. In the big data era, further studies should be more inclined to the integration and application of spatial machine learning techniques, as well as investigation on the temporal trends of environmental geochemical features.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

Availability of data and material

There are no issues with availability of data and materials. The data used are properly referenced.

References

  • Ahrens, L. H. (1954). The lognormal distribution of the elements (a fundamental law of geochemistry and its subsidiary). Geochimica Et Cosmochimica Acta, 5, 49–73.

    Article  CAS  Google Scholar 

  • Aitchison, J. (1986). The statistical analysis of compositional data (p. 416). Chapman and Hall.

  • Albanese, S., De Vivo, B., Lima, A., & Cicchella, D. (2007). Geochemical background and baseline values of toxic elements in stream sediments of Campania region (Italy). Journal of Geochemical Exploration, 93, 21–34. https://doi.org/10.1016/j.gexplo.2006.07.006

    Article  CAS  Google Scholar 

  • Anselin, L. (1995). Local indicators of spatial association–LISA. Geographical Analysis, 27, 93–115.

    Article  Google Scholar 

  • Argyraki, A., & Kelepertzis, E. (2014). Urban soil geochemistry in Athens, Greece: The importance of local geology in controlling the distribution of potentially harmful trace elements. Science of the Total Environment, 482–483, 366–377. https://doi.org/10.1016/j.scitotenv.2014.02.133

    Article  CAS  Google Scholar 

  • Atkinson, P. M., German, S. E., Sear, D. A., & Clark, M. J. (2003). Exploring the relations between riverbank erosion and geomorphological controls using geographically weighted logistic regression. Geographical Analysis, 35(1), 58–82.

    Article  Google Scholar 

  • Bennett, L., 2018. Machine learning in ArcGIS. Available at: https://www.esri.com/about/newsroom/arcuser/machine-learning-in-arcgis/?rmedium=arcuser&rsource=https://www.esri.com/esri-news/arcuser/spring-2018/machine-learning-in-arcgis.

  • Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.

    Article  Google Scholar 

  • Brunsdon, C., Fotheringham, A. S., & Charlton, M. (1996). Geographically weighted regression: A method for exploring spatial non-stationarity. Geographical Analysis, 28, 281–298.

    Article  Google Scholar 

  • Burak, D. L., Fontes, M. P., Santos, N. T., Monteiro, L. V. S., de Sousa Martins, E., & Becquer, T. (2010). Geochemistry and spatial distribution of heavy metals in Oxisols in a mineralized region of the Brazilian Central Plateau. Geoderma, 160(2), 131–142.

    Article  CAS  Google Scholar 

  • Cheng, Q., Agterberg, F. P., & Ballantyne, S. B. (1994). The separation of geochemical anomalies from background by fractal methods. Journal of Geochemical Exploration, 51, 109–130.

    Article  CAS  Google Scholar 

  • Cheng, Q., Xu, Y., & Grunsky, E. (2000). Integrated spatial and spectrum method for geochemical anomaly separation. Natural Resources Research, 9, 43–52.

    Article  CAS  Google Scholar 

  • Cliff, A.D., Ord, J.K., 1981. Spatial processes, models and applications. Pion, London, 266 pp.

  • Coggins, A. M., Jennings, S. G., & Ebinghaus, R. (2006). Accumulation rates of the heavy metals lead, mercury and cadmium in ombrotrophic peatlands in the west of Ireland. Atmospheric Environment, 40(2), 260–278.

    Article  CAS  Google Scholar 

  • Filzmoser, P., Hron, K., & Reimann, C. (2009). Univariate statistical analysis of environmental (compositional) data: Problems and possibilities. Science of the Total Environment, 407, 6100–6108.

    Article  CAS  Google Scholar 

  • Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically weighted regression: The analysis of spatially varying relationships (p. 284). John Wiley & Sons Ltd.

  • Fotheringham, A. S., Yang, W., & Kang, W. (2017). Multiscale geographically weighted regression (MGWR). American Association of Geographers Annals, 107(6), 1247–1265.

    Article  Google Scholar 

  • Fotheringham, S., Rogerson, P. (Eds.)., 2013. Spatial analysis and GIS. CRC Press.

  • Franzese, M., & Iuliano, A. (2019). Correlation Analysis. Encyclopedia of Bioinformatics and Computational Biology, 1, 706–721.

    Article  Google Scholar 

  • Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3), 189–206.

    Article  Google Scholar 

  • Getis, A., 2010. Spatial autocorrelation. In Handbook of applied spatial analysis (pp. 255–278). Springer, Berlin, Heidelberg.

  • Ghezelbash, R., Maghsoudi, A., & Carranza, E. J. M. (2019). Mapping of single- and multi-element geochemical indicators based on catchment basin analysis: Application of fractal method and unsupervised clustering models. Journal of Geochemical Exploration, 199, 90–104.

    Article  CAS  Google Scholar 

  • Goodchild, M., Haining, R., & Wise, S. (1992). Integrating GIS and spatial data analysis: Problems and possibilities. International Journal of Geographical Information System, 6(5), 407–423.

    Article  Google Scholar 

  • Goovaerts, P. (1997). Geostatistics for natural resources evaluation. Oxford University Press.

  • Harris, P., Brunsdon, C., & Charlton, M. (2011). Geographically weighted principal components analysis. International Journal of Geographical Information Science, 25(10), 1717–1736.

    Article  Google Scholar 

  • Hawkes, H. E., & Bloom, H. (1955). Heavy metals in stream sediment used as exploration guides. Mining Engineering, 8, 1121–1126.

    Google Scholar 

  • Hawkes, H. E., Webb, J. S. (1962). Geochemistry in mineral exploration Harper. New York.

  • Hengl, T., Heuvelink, G. B. M., & Rossiter, D. G. (2007). About regression-kriging: From equations to case studies. Computer and Geoscience Spatial Analysis, 33, 1301–1315. https://doi.org/10.1016/j.cageo.2007.05.001

    Article  Google Scholar 

  • Hou, D., O’Connor, D., Nathanail, P., Tian, L., & Ma, Y. (2017). Integrated GIS and multivariate statistical analysis for regional scale assessment of heavy metal soil contamination: A critical review. Environmental Pollution, 231, 1188–1200.

    Article  CAS  Google Scholar 

  • Jordan, C., Zhang, C., & Higgins, A. (2007). Using GIS and statistics to study influences of geology on probability features of surface soil geochemistry in Northern Ireland. Journal of Geochemical Exploration, 93(3), 135–152.

    Article  CAS  Google Scholar 

  • Kalogirou, S., 2014. A spatially varying relationship between the proportion of foreign citizens and income at local authorities in Greece. Proceedings of the 10th International Congress of the Hellenic Geographical Society, 5, 1458–1466.

  • Kirkwood, C., Cave, M., Beamish, D., Grebby, S., & Ferreira, A. (2016). A machine learning approach to geochemical mapping. Journal of Geochemical Exploration, 167, 49–61. https://doi.org/10.1016/j.gexplo.2016.05.003

    Article  CAS  Google Scholar 

  • Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015). Machine learning applications in cancer prognosis and prediction. Computational and Structural Biotechnology Journal, 13, 8–17.

    Article  CAS  Google Scholar 

  • Kravchenko, A., & Bullock, D. G. (1999). A comparative study of interpolation methods for mapping soil properties. Agronomy Journal, 91, 393–400.

    Article  Google Scholar 

  • Krivoruchko, K., 2012. (a) Empirical Bayesian kriging and (b) Modeling contamination using empirical Bayesian kriging. Available at: https://www.esri.com/news/arcuser/1012/fall2012.html.

  • Kumar, S., Lal, R., & Liu, D. (2012). A geographically weighted regression kriging approach for mapping soil organic carbon stock. Geoderma, 189, 627–634.

    Article  Google Scholar 

  • Lalor, G., & Zhang, C. S. (2001). Multivariate outlier detection and remediation in geochemical databases. Science of the Total Environment, 281, 99–109.

    Article  CAS  Google Scholar 

  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.

  • Leenaers, H., Okx, J., & Burrough, P. (1990). Comparison of spatial prediction methods for mapping floodplain soil pollution. CATENA, 17, 535–550.

    Article  Google Scholar 

  • Liang, J., Feng, C., Zeng, G., Gao, X., Zhong, M., Li, X., He, X. Y., & Fang, Y. (2017). Spatial distribution and source identification of heavy metals in surface soils in a typical coal mine city, Lianyuan China. Environmental Pollution, 225, 681–690.

    Article  CAS  Google Scholar 

  • Limpert, E., Stahel, W. A., & Abbt, M. (2001). Log-normal distributions across the sciences: Keys and clues: On the charms of statistics, and how mechanical models resembling gambling machines offer a link to a handy way to characterize log-normal distributions, which can provide deeper insight into variability and probability–normal or log-normal: That is the question. BioScience, 51(5), 341–352.

    Article  Google Scholar 

  • Matheron, G., 1971. The theory of regionalized variables and its applications. Les Cahiers du Centre de Morphologie Mathématique, Fascicule 5. Ecole Nationale Superieure des Mines de Paris, Fontainebleau, p 211.

  • Matheron, G., 2012. Estimating and choosing: an essay on probability in practice. Springer Science & Business Media.

  • Matschullat, J., Ottenstein, R., & Reimann, C. (2000). Geochemical background–can we calculate it? Environmental Geology, 39(9), 990–1000.

    Article  CAS  Google Scholar 

  • Mayer-Schonberger, V., & Cukier, K. (2013). Big data: A revolution that will transform how we live, work and think. Houghton Mifflin Harcourt Publishing Company.

  • McGrath, D., & Zhang, C. (2003). Spatial distribution of soil organic carbon concentrations in grassland of Ireland. Applied Geochemistry, 18(10), 1629–1639.

    Article  CAS  Google Scholar 

  • McGrath, D., Zhang, C., & Carton, O. T. (2004). Geostatistical analyses and hazard assessment on soil lead in Silvermines area Ireland. Environmental Pollution, 127(2), 239–248.

    Article  CAS  Google Scholar 

  • Meng, Y. T., Cave, M., & Zhang, C. (2019). Comparison of methods for addressing the point-to-area data transformation to make data suitable for environmental, health and socio-economic studies. Science of the Total Environment, 689, 797–807.

    Article  CAS  Google Scholar 

  • Miller, H. J., & Goodchild, M. F. (2015). Data-driven geography. GeoJournal, 80, 449–461. https://doi.org/10.1007/s10708-014-9602-6

    Article  Google Scholar 

  • Odeh, I. O. A., McBratney, A. B., & Chittleborough, D. J. (1995). Further results on prediction of soil properties from terrain attributes: Hetero-topic cokriging and regression-kriging. Geoderma, 67(3–4), 215–226.

    Article  Google Scholar 

  • Oliver, M. A., & Webster, R. (1990). Kriging: A method of interpolation for geographical information systems. International Journal of Geographical Information Systems, 4, 313–332.

    Article  Google Scholar 

  • Oliver, M. A., & Webster, R. (2015). Basic steps in geostatistics: The variogram and kriging. Springer International Publishing.

  • Ord, J. K., & Getis, A. (1995). Local spatial autocorrelation statistics: Distributional issues and an application. Geographical Analysis, 27(4), 286–306.

    Article  Google Scholar 

  • Overpeck, J. T., Meehl, G. A., Bony, S., & Easterling, D. R. (2011). Climate data challenges in the 21st century. Science, 331(6018), 700–702.

    Article  CAS  Google Scholar 

  • Pan, H., Lu, X., & Lei, K. (2017). A comprehensive analysis of heavy metals in urban road dust of Xi’an, China: Contamination, source apportionment and spatial distribution. Science of the Total Environment, 609, 1361–1369.

    Article  CAS  Google Scholar 

  • Porteous, A. (1996). Dictionary of environmental science and technology (2nd Ed). Chichester, NY: Wiley.

  • Povak, N. A., Hessurg, P. H., McDonnell, T. C., Reynolds, K. M., Sullivn, T. J., Salter, R. B., & Crosby, B. J. (2014). Machine learning and linear regression models to predict catchment-level base cation weathering rates across the southern Appalachian Mountain region, USA. Water Resources Research, 50, 2798–2814.

    Article  CAS  Google Scholar 

  • Reichman, O. J., Jones, M. B., & Schildhauer, M. P. (2011). Challenges and opportunities of open data in ecology. Science, 331(6018), 703–705.

  • Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., Carvalhais, N., & Prabhat, K. (2019). Deep learning and process understanding for data-driven Earth system science. Nature, 566, 195–204. https://doi.org/10.1038/s41586-019-0912-1

    Article  CAS  Google Scholar 

  • Reimann, C., & de Caritat, P. (2005). Distinguishing between natural and anthropogenic sources for elements in the environment: Regional geochemical surveys versus enrichment factors. Science of the Total Environment, 337(1–3), 91–107.

    Article  CAS  Google Scholar 

  • Reimann, C., & Filzmoser, P. (2000). Normal and lognormal data distribution in geochemistry: Death of a myth. Consequences for the statistical treatment of geochemical and environmental data. Environmental Geology, 39(9), 1001–1014.

  • Reimann, C., & Garrett, R. G. (2005). Geochemical background–concept and reality. Science of the Total Environment, 350(1–3), 12–27.

    Article  CAS  Google Scholar 

  • Reimann, C., Filzmoser, P., Garrett, R., & Dutter, R. (2011). Statistical data analysis explained: Applied environmental statistics with R. John Wiley & Sons.

  • Reimann, C., Fabian, K., Birke, M., Filzmoser, P., Demetriades, A., Négrel, P., Oorts, K., & Matschullat, J. (2018). GEMAS: Establishing geochemical background and threshold for 53 chemical elements in European agricultural soil. Applied Geochemistry, 88, 302–318.

    Article  CAS  Google Scholar 

  • Reimann, C., Filzmoser, P., Garrett, R. G., & Dutter, R. (2008). Statistical data analysis explained (p. 343). Wiley, Chichester, UK: Applied environmental statistics with R.

    Book  Google Scholar 

  • Reimann, C., Birke, M., Demetriades, A., Filzmoser, P., O' Connor, P., 2014. Chemistry of Europe's agricultural soils, part A: Methodology and interpretation of the GEMAS data set. Geologisches Jahrbuch (Reihe B102), Schweizerbarth, Hannover.

  • Reyes, A., Thiombane, M., Panico, A., Daniele, L., Lima, A., Di Bonito, M., & De Vivo, B. (2020). Source patterns of potentially toxic elements (PTEs) and mining activity contamination level in soils of Taltal city (northern Chile). Environmental Geochemistry and Health, 42(8), 2573–2594.

    Article  CAS  Google Scholar 

  • Robinson, T. P., & Metternicht, G. (2006). Testing the performance of spatial interpolation techniques for mapping soil properties. Computers and Electronics in Agriculture, 50(2), 97–108.

    Article  Google Scholar 

  • Rose, A. W., Hawkes, H. E., & Webb, J. S. (1979). Geochemistry in mineral exploration (2nd ed., p. 657). Academic Press.

  • Shepard, D., 1964. A two dimensional interpolation function for irregularly data spaced. ACM National Conference, 517–524.

  • Shotyk, W., Krachler, M., Martinez-Cortizas, A., Cheburkin, A. K., & Emons, H. (2002). A peat bog record of natural, pre-anthropogenic enrichments of trace elements in atmospheric aerosols since 12 370 14C yr BP, and their variation with Holocene climate change. Earth and Planetary Science Letters, 199(1–2), 21–37.

    Article  CAS  Google Scholar 

  • Sokal, R. R., & Oden, N. L. (1978). Spatial autocorrelation in biology: 1. Methodology. Biological Journal of the Linnean Society, 10(2), 199–228.

    Article  Google Scholar 

  • Tarasov, D. A., Buevich, A. G., Sergeev, A. P., & Shichkin, A. V. (2018). High variation topsoil pollution forecasting in the Russian subarctic: Using artificial neural networks combined with residual kriging. Applied Geochemistry, 88, 188–197.

    Article  CAS  Google Scholar 

  • Tepanosyan, G., Lilit, S., Nairuhi, M., & Armen, S. (2020). Combination of compositional data analysis and machine learning approaches to identify sources and geochemical associations of potentially toxic elements in soil and assess the associated human health risk in a mining city. Environmental Pollution, 261, 114210.

    Article  CAS  Google Scholar 

  • Thornton, I., & Webb, J. S. (1979). Geochemistry and health in the United Kingdom. Philosophical Transactions of the Royal Society, B288, 151–168.

    Google Scholar 

  • Tobler, W. R. (1970). A computer movie simulating urban growth in the detroit region. Economic Geography, 46234–10.2307/143141.

  • Vitolo, C., Elkhatib, Y., Reusser, D., Macleod, C. J. A., & Buytaert, W. (2015). Web technologies for environmental big data. Environmental Modelling and Software, 63, 185–198.

    Article  Google Scholar 

  • Wackernagel, H. (1998). Multivariate geostatistics: An introduction with applications. Springer-Verlag.

  • Wang, J., & Zuo, R. (2020). Assessing geochemical anomalies using geographically weighted lasso. Appliled Geochemistry, 119, 104668.

    Article  CAS  Google Scholar 

  • Webster, R., & Oliver, M. (2001). Geostatistics for environmental scientists. John Wiley & Sons.

  • Wheeler, D. C. (2009). Simultaneous coefficient penalization and model selection in geographically weighted regression: The geographically weighted lasso. Environment and Planning A, 41(3), 722–742.

    Article  Google Scholar 

  • Worsham, L., Markewitz, D., & Nibbelink, N. (2010). Incorporating spatial dependence into estimates of soil carbon contents under different land covers. Soil Science Society of America Journal, 74, 635–646.

    Article  CAS  Google Scholar 

  • Wu, S., Zhou, S., Bao, H., Chen, D., Wang, C., Li, B., Tong, G. J., Yuan, Y. J., & Xu, B. (2019). Improving risk management by using the spatial interaction relationship of heavy metals and PAHs in urban soil. Journal of Hazardous Materials, 364, 108–116.

    Article  CAS  Google Scholar 

  • Xu, H. F., Demetriades, A., ReimannJiménez, C. J. J., Filser, J., & Zhang, C. S. (2019). Identification of the co-existence of low total organic carbon contents and low pH values in agricultural soil in north-central Europe using hot spot analysis based on GEMAS project data. Science of the Total Environment, 678, 94–104.

    Article  CAS  Google Scholar 

  • Xu, H. F., & Zhang, C. S. (2021). Investigating spatially varying relationships between total organic carbon contents and pH values in European agricultural soil using geographically weighted regression. Science of the Total Environment, 752, 141977.

    Article  CAS  Google Scholar 

  • Xu, H. F., Croot, P., & Zhang, C. S. (2021). Discovering hidden spatial patterns and their associations with controlling factors for potentially toxic elements in topsoil using hot spot analysis and K-means clustering analysis. Environment International, 151, 106456.

    Article  CAS  Google Scholar 

  • Yadav, I. C., Devi, N. L., Singh, V. K., Li, J., & Zhang, G. (2019). Spatial distribution, source analysis, and health risk assessment of heavy metals contamination in house dust and surface soil from four major cities of Nepal. Chemosphere, 218, 1100–1113.

    Article  CAS  Google Scholar 

  • Young, M. E., & Donald, A. W. (Eds.). (2013). A guide to the Tellus data. Geological Survey of Northern Ireland.

  • Yuan, Y. M., Cave, M., Xu, H. F., & Zhang, C. S. (2020). Exploration of spatially varying relationships between Pb and Al in urban soils of London at the regional scale using geographically weighted regression (GWR). Journal of Hazardous Materials, 393, 122377.

    Article  CAS  Google Scholar 

  • Zhang, C., & Selinus, O. (1998). Statistics and GIS in environmental geochemistry–some problems and solutions. Journal of Geochemical Exploration, 64(1–3), 339–354.

    Article  CAS  Google Scholar 

  • Zhang, C., Manheim, F. T., Hinde, J., & Grossman, J. N. (2005). Statistical characterization of a large geochemical database and effect of sample size. Applied Geochemistry, 20(10), 1857–1874.

  • Zhang, C. S., Luo, L., Xu, W., & Ledwith, V. (2008). Use of local Moran’s I and GIS to identify pollution hotspots of Pb in urban soils of Galway Ireland. Science Total Environment, 398(1–3), 212–221.

    Article  CAS  Google Scholar 

  • Zhang, C., Tang, Y., Xu, X., & Kiely, G. (2011). Towards spatial geochemical modelling: Use of geographically weighted regression for mapping soil organic carbon contents in Ireland. Applied Geochemistry, 26, 1239–1248.

    Article  CAS  Google Scholar 

  • Zhang C., Zuo R., Xiong Y., Shi X., Donnelly C., 2021. GIS, Geostatistics, and Machine Learning in Medical Geology. In: Siegel M., Selinus O., Finkelman R. (eds.) Practical applications of medical geology. Springer, Cham. https://doi.org/10.1007/978-3-030-53893-4_7.

  • Zuo, R. (2017). Machine learning of mineralization-related geochemical anomalies: A review of potential methods. Natural Resources Research, 26, 457–464.

    Article  CAS  Google Scholar 

  • Zuo, R., Xia, Q., & Zhang, D. (2013). A comparison study of the C-A and S–A models with singularity analysis to identify geochemical anomalies in covered areas. Applied Geochemistry, 33, 165–172.

    Article  CAS  Google Scholar 

  • Zuo, R., Carranza, E. J. M., & Wang, J. (2016). Spatial analysis and visualization of exploration geochemical data. Earth-Science Reviews, 158, 9–18. https://doi.org/10.1016/j.earscirev.2016.04.006

    Article  CAS  Google Scholar 

  • Zuo, R., Wang, J., Xiong, Y., & Wang, Z. (2021). The processing techniques of geochemical exploration data: Past, present, and future. Applied Geochemistry, 132, 105072.

    Article  CAS  Google Scholar 

Download references

Funding

No specific funding for this short review paper.

Author information

Authors and Affiliations

  1. School of Environmental and Chemical Engineering, Foshan University, Foshan, 528000, Guangdong, China

    Haofan Xu

  2. International Network for Environment and Health (INEH), School of Geography and Archaeology & Ryan Institute, National University of Ireland, Galway, Ireland

    Haofan Xu & Chaosheng Zhang

Authors
  1. Haofan Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chaosheng Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Mr. Haofan Xu is a PhD student who makes the main contribution in writing this paper. Prof. Chaosheng Zhang is the supervisor who provides the main research ideas, main outline of the paper and makes contribution to writing and revision of the paper.

Corresponding author

Correspondence to Chaosheng Zhang.

Ethics declarations

Conflicts of interest

There are no conflicts of interest/completing interests.

Consent to participate

All co-authors have agreed.

Consent for publication

All co-authors have agreed.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xu, H., Zhang, C. Development and applications of GIS-based spatial analysis in environmental geochemistry in the big data era. Environ Geochem Health 45, 1079–1090 (2023). https://doi.org/10.1007/s10653-021-01183-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10653-021-01183-8

Keywords

Search

Navigation