Abstract
Protecting groundwater from lead contamination is an important public-health concern and a major national environmental issue worldwide. This article addresses myGeoffice Web Internet service for geographers, in general, and geo-statistics researchers, in particular, with the famous water contamination case at Jura lake, Switzerland (a typical rural-urban region). Based on 189 samples of lead (Pb), five key investigation steps for a scientific perspective of any pollution incident are presented: Descriptive analysis (including nearest neighborhood, G(h) and Kernel techniques), spatial autocorrelation (Moran location scatterplot and Moran I) and Ordinary Kriging (OK) interpolation. The uncertainty and cost assessments issues are exceptionally tackled with Indicator Kriging (including the conditional cumulative distribution function, Shannon local entropy, probabilistic intervals and E-type estimation) and Gaussian geo-simulation. The total lead pollution exhibited patterns of high and low levels of concentrations along the all lake, leading to the conclusion that water is unsuitable for human consumption, in general, and unsuitable for any living organism, in particular sub-areas. It is also hoped that future GIS readers will follow this approach for their spatial cases with myGeoffice.
Highlights
The problem of statistical spatial analysis covers an escalating range of methods that address different spatial problems, from pattern recognition to spatial interpolation and economic trend modeling
The uncertainty and cost assessments issues are exceptionally tackled with Indicator Kriging and Gaussian geo-simulation
Regarding the drawbacks of this non-parametric method, five issues should be stressed: 1) Loss of information because it does not distinguish among observations if they are both below or above the threshold; 2) Setup of as many variograms as the levels to be considered; 3) Possibility of obtaining estimates greater than 1 and below 0; 4) Regarding extreme values, the variogram may correspond to a pure nugget-effect; 5) Concerning a series of several cutoffs, order relation correction should be taken into account with a posteriori correction procedure of the conditional cumulative distribution function
Summary
This writing tries to review a set of methods and procedures that are truly spatial (and special) such as Moran scatterplot, Indicator Kriging, stochastic simulation for spatial processes, error and uncertainty measurement In this particular case, the spatial pollution case of lead (Pb) at Jura Lake, Switzerland, will take place. The spatial pollution case of lead (Pb) at Jura Lake, Switzerland, will take place As expected, these fields hold a common process for any space study: Collection of data points (Section 2); Descriptive analysis (Section 3); Modeling of spatial variability for description of spatial patterns (Section 4); Spatial prediction at non-sampled locations (Section 5); Modeling of uncertainty such as what is the probability to exceed a critical concentration at any non-sampled location or which sub-areas should be considered for cleaning (Section 6); Geo-simulation (Section 7); Conclusions (Section 8). It is hoped that the present reader is able to fully understand this Web spatial analysis software for his/her future geographical studies
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