One of the most common problems we have when attempting to interpolate data using kriging is the presence of outliers in the data. An outlier is a data value that is either very large or very small compared to the rest of the data. Outliers often result from malfunctions in the monitoring equipment or typos during data entry, such as accidentally removing a decimal. These erroneous data points should be manually corrected or removed before attempting to interpolate. However, not all outliers are the result of machine or human error. Some outliers are valid values, and this blog will demonstrate how to deal with this kind of outlier.
Description of the data
The data used in this blog comes from measurements of heavy metal concentrations of mosses in Austria in 1995. Various heavy metals were measured in milligrams of heavy metal per kilogram of moss, and here we will focus on molybdenum. As the following graphic shows, lower molybdenum concentrations were found in the north and higher concentrations in the south. However, note that two locations had molybdenum concentrations that were much higher than the rest of the data (7.66 and 1.81 mg/kg).......
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