Document Detail

AUTO-IK: a 2D indicator kriging program for the automated non-parametric modeling of local uncertainty in earth sciences.
MedLine Citation:
PMID:  20161335     Owner:  NLM     Status:  Publisher    
Indicator kriging provides a flexible interpolation approach that is well suited for datasets where: 1) many observations are below the detection limit, 2) the histogram is strongly skewed, or 3) specific classes of attribute values are better connected in space than others (e.g. low pollutant concentrations). To apply indicator kriging at its full potential requires, however, the tedious inference and modeling of multiple indicator semivariograms, as well as the post-processing of the results to retrieve attribute estimates and associated measures of uncertainty. This paper presents a computer code that performs automatically the following tasks: selection of thresholds for binary coding of continuous data, computation and modeling of indicator semivariograms, modeling of probability distributions at unmonitored locations (regular or irregular grids), and estimation of the mean and variance of these distributions. The program also offers tools for quantifying the goodness of the model of uncertainty within a cross-validation and jack-knife frameworks. The different functionalities are illustrated using heavy metal concentrations from the well-known soil Jura dataset. A sensitivity analysis demonstrates the benefit of using more thresholds when indicator kriging is implemented with a linear interpolation model, in particular for variables with positively skewed histograms.
P Goovaerts
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Publication Detail:
Journal Detail:
Title:  Computers & geosciences     Volume:  35     ISSN:  0098-3004     ISO Abbreviation:  -     Publication Date:  2009 Jun 
Date Detail:
Created Date:  2010-7-13     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101092352     Medline TA:  Comput Geosci     Country:  -    
Other Details:
Languages:  ENG     Pagination:  1255-1270     Citation Subset:  -    
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MeSH Terms
Grant Support
R43 CA132347-01//NCI NIH HHS

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