Cokriging is the common method of spatial interpolation (best linear unbiased prediction) in multivariate geostatistics. While best prediction has been well understood univariate statistics, literature for case elusive so far. The new challenges provided by modern datasets, being typically multivariate, call a deeper study cokriging. In particular, we deal with problem misspecified cokriging wi...

The concentrations of As, Hg, Co, Cr and Cd were tested for each soil sample, and their spatial patterns were analyzed by the semivariogram approach of geostatistics and geographical information system technology. Multivariate statistic approaches (principal component analysis and cluster analysis) were used to identify heavy metal sources and their spatial pattern. Principal component analysis...

Since gstat package version 1.0-0, a dependency of gstat on the R package spacetime was introduced, allowing the code in gstat to exploit spatio-temporal data structures from that package. This vignette describes the possibilities and limitations of the package for spatio-temporal geostatistics. To understand some of the possibilities and limitations, some knowledge of the history of the softwa...

the importance of groundwater as a source of water supply in arid, semi –arid areas in recent years due to the uncontrolled exploitation of groundwater have been doubled. uncontrolled exploitation has been caused a drop in the water table in many areas of the country, including this region. with replace methods of modern irrigation instead of traditional irrigation methods can prevent drop the ...

This paper presents an overview of the most recent developments in the field of geostatistics and describes their application to soil science. Geostatistics provides descriptive tools such as semivariograms to characterize the spatial pattern of continuous and categorical soil attributes. Ž . Various interpolation kriging techniques capitalize on the spatial correlation between observations to ...

With the increase in size and complexity of spatiotemporal data, traditional methods for performing statistical analysis are insufficient for meeting real-time requirements for mining information from Big Data, due to both dataand computing-intensive factors. To solve the Big Data challenges in geostatistics and to support decision-making, a high performance, spatiotemporal statistical analysis...

In geostatistics, spatial data will be analysed that often come from irregularly distributed sampling locations. Interest is in modelling the data, i.e. estimating distributional parameters, and then to predict the phenomenon under study at unobserved sites within the corresponding sampling domain. The method of universal kriging for spatial prediction was introduced to cover the problem of spa...