The log transformation is often used where the data has a positively skewed distribution (shown below) and there are a few very large values. The log transformation is actually a special case of the Box-Cox transformation when λ = 0 the transformation is as follows: Y( s) = ln( Z( s)),įor Z( s) > 0, and ln is the natural logarithm. The square-root transformation is a special case of the Box-Cox transformation when λ = ½. In this case, the square-root transformation may help to make the variances more constant throughout the study area and often makes the data appear normally distributed as well. That is, if you have small counts in part of your study area, the variability in that local region will be smaller than the variability in another region where the counts are larger. For these types of data, the variance is often related to the mean. The Box-Cox transformation is Y( s) = ( Z( s) λ - 1)/λ,įor example, suppose that your data is composed of counts of some phenomenon. Learn more about Box–Cox, arcsine, and log transformations Box-Cox transformation Learn more about transformations and trends What often happens is that the transformation also yields data that has constant variance through the study area. Usually, you want to find the transformation so that Y( s) is normally distributed. Suppose you observe data Z( s), and apply some transformation Y( s) = t( Z( s)). Geostatistical Analyst allows the use of several transformations including Box-Cox (also known as power transformations), arcsine, and logarithmic. If the interpolation model you build uses one of the kriging methods, and you choose to transform the data as one of the steps, the predictions will be transformed back to the original scale in the interpolated surface. The histogram allows you to explore the effects of different transformations on the distribution of the dataset. When the data is skewed (the distribution is lopsided), you might want to transform the data to make it normal. Some methods in Geostatistical Analyst require that the data be normally distributed.
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