SPATIAL CLUSTER AND OUTLIER IDENTIFICATION OF GEOCHEMICAL ASSOCIATION OF ELEMENTS: A CASE STUDY IN JUIRUI COPPER MINING AREA

Spatial clusters and spatial outliers play an important role in the study of the spatial distribution patterns of geochemical data. They characterize the fundamental properties of mineralization processes, the spatial distribution of mineral deposits, and ore element concentrations in mineral districts. In this study, a new method for the study of spatial distribution patterns of multivariate data is proposed based on a combination of robust Mahalanobis distance and local Moran’s Ii. In order to construct the spatial matrix, the Moran's I spatial correlogram was first used to determine the range. The robust Mahalanobis distances were then computed for an association of elements. Finally, local Moran’s Ii statistics was used to measure the degree of spatial association and discover the spatial distribution patterns of associations of Cu, Au, Mo, Ag, Pb, Zn, As, and Sb elements including spatial clusters and spatial outliers. Spatial patterns were analyzed at six different spatial scales (2km, 4 km, 6 km, 8 km, 10 km and 12 km) for both the raw data and Box-Cox transformed data. The results show that identified spatial cluster and spatial outlier areas using local Moran’s Ii and the robust Mahalanobis accord the objective reality and have a good conformity with known deposits in the study area.

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  • Anselin, L. 1995. Local indicators of spatial association-LISA. Geographical Analysis, 27, 2, 93-115.
  • Bagchi, R., Henrys, P.A., Brown, P.E., Burslem, D.F.R.P., Diggle, P.J., Gunatilleke, C.V.S., Gunatilleke, I.A.U.N., Kassim, A.R., Law, R., Noor, S., Valencia, R.L. 2011. Spatial patterns reveal negative density dependence and habitat associations in tropical trees. Ecology, 92, 9, 1723-1729.
  • Brody, S.D., Highfield W.E., Thornton S. 2006. Planning at the urban fringe: an examination of the factors influencing nonconforming development patterns in southern Florida. Environment and Planning B: Planning and Design, 33, 1, 75-96.
  • Cliff, A.D., Ord, J.K. 1973. Spatial Autocorrelation. London - Pion.
  • Cliff, A.D., Ord, J.K. 1981. Spatial Processes, Models and Applications. London - Pion.
  • Filzmoser, P., Garrett, R.G., Reimann, C. 2005. Multivariate outlier detection in exploration geochemistry. Computers and Geosciences, 31,5, 579-587.
  • Filzmoser, P. and Hron, K. 2013. Robustness for compositional data. In C. Becker, R. Fried and S. Kuhnt, editors, Robustness and Complex Data Structures, Festschrift in Honour of Ursula Gather, Springer Verlag, Heidelberg, 117-131.
  • Fuentes, M., Song, H.R., Ghosh, S.K., Holland, D.M., Davis, J.M. 2006. Spatial Association Between Speciated Fine Particles and Mortality. Biometrics, 62, 855-863.
  • Garrett, R.G. 1989. The chi-square plot: A tool for multivariate outlier recognition. Journal of Geochemical Exploration, 32, 1-3, 319-341.
  • Getis, A., Ord, J.K. 1992. The analysis of spatial association by use of distance statistics. Geographical Analysis, 24, 3, 189-206.
  • Getis, A., Ord, J.K. 1996. Local spatial statistics: an overview. In: Longley P, Batty M, editors. Spatial Analysis: Modelling in a GIS Environment. Cambridge: GeoInformation International, 239-251.
  • Geary, R.C. 1954. The Contiguity Ratio and Statistical Mapping. The Incorporated Statistician, 5, 3, 115-145.
  • Gervini, D. 2003. A robust and efficient adaptive reweighted estimator of multivariate location and scatter. Journal of Multivariate Analysis, 84,1, 116-144.
  • Goovaerts, P., Jacquez, G.M. 2004. Accounting for regional background and population size in the detection of spatial clusters and outliers using geostatistical filtering and spatial neutral models: the case of lung cancer in Long Island, New York. International Journal of Health Geographics, 3, 14-46.
  • Hawkins, D.M., 1980. Identification of Outliers. Chapman and Hall - London.
  • Irl, S., Harter, D., Steinbauer, M., Puyol, D., Fernández-Palacios, J., Jentsch, A., Beierkuhnlein, K. 2015. Climate vs. topography - spatial patterns of plant species diversity and endemism on a high-elevation island. Journal of Ecology, 103, 1621-1633.
  • Ishioka, F., Kurihara, K., Suito, H., Horikawa, Y., ONO, Y. 2007. Detection of hotspots for three-dimensional spatial data and its application to environmental pollution data. Journal of Environmental Science for Sustainable Society, 1, 15-24.
  • James, W.L., Cossman, R.E., Cossman, J.S., Campbell, C., Blanchard, T. 2004. A brief visual primer for the mapping of mortality trend data. International Journal of Health Geographics, 3, 1, 1-7.
  • Legendre, P., Fortin, M.J. 1989. Spatial pattern and ecological analysis. Plant Ecology, 80, 2, 107-138.
  • Lalor, G. C., Zhang, C. S. 2001. Multivariate outlier detection and remediation in geochemical databases. Sci. Total Environ, 281, 99-109.
  • McGrath, D., Zhang, C.S. 2003. Spatial distribution of soil organic carbon concentrations in grassland of Ireland. Applied Geochemistry, 18, 10, 1629-1639.
  • McLaughlin, C.C., Boscoe, F.P. 2007. Effects of randomization methods on statistical inference in disease cluster detection. Health Place, 13, 1, 152-63.
  • Monastiriotis, V. 2009. Examining the consistency of spatial association patterns across socio-economic indicators: an application to the Greek regions. Empirical Economics, 37, 1, 25-49.
  • Moran, P. A. P. 1948. Biometrika, 35, 255-60.
  • Moran, P. 1950. Notes on continuous stochastic phenomena. Biometrika, 37, 17-23.
  • Nguyen, T., Liu, X., Ren, Z. 2014. A Study Of Geochemical Exploration Spatial Cluster Identificaton Based On Local Spatial Autocorrelation. Geophysical and Geochemical Exploration, 38, 2, 370-376.
  • Reimann, C., Filzmoser, P., Garrett, R.G. 2005. Background and threshold: critical comparison of methods of determination. Science of the Total Environment, 346, 1-3, 1-16
  • Rose, A.W., Hawkes, H.E., Webb, J.S. 1979. Geochemistry in Mineral Exploration. Academic Press - London.
  • Rocke, D.M., Woodruff, D.L. 1996. Identification of Outliers in Multivariate Data. Journal of the American Statistical Association, 91, 435, 1047-1061.
  • Rousseeuw, P. J., Leroy, A. M. 1987. Robust regression and outlier detection. John Wiley and Sons - New York.
  • Rousseeuw, P.J., Van Zomeren, B.C. 1990. Unmasking multivariate outliers and leverage points. Journal of the American Statistical Association, 85, 411, 633-651.
  • Ruiz, M.O., Tedesco, C., McTighe, T.J., Austin, C., Kitron, U. 2004. Environmental and social determinants of human risk during a West Nile virus outbreak in the greater Chicago area, 2002. International Journal of Health Geographics, 3, 8, 2-11.
  • Tango, T. 1995. A class of test for detecting ‘general’ and ‘focused’ clustering of rare diseases. Statistics in Medicine, 14, 21-22, 2323-2334.
  • Tyler, D.E. 1991. Some issues in the robust estimation of multivariate location and scatter [C]. In W. Stahel and S. Weisberg, editors, Directions in Robust Statistics and Diagnostics 2. Springer - New York, 327-336.
  • Waller, L., Gotway, C.A. 2004. Applied Spatial Statistics for Public Health Data. John Wiley and Sons - New Jersey.
  • Zhang, C.S., McGrath, D. 2004. Geostatistical and GIS analyses on soil organic carbon concentrations in grassland of southeastern Ireland from two different periods. Geoderma, 119, 3-4, 261-275.
  • Zhang, C.S., Luo, L., Xu, W.L., Ledwith, V. 2008. Use of local Moran's I and GIS to identify pollution hotspots of Pb in urban soils of Galway, Ireland. Science of the total environment, 398, 1-3, 212-221.