The appropriate standardization method depends on your data set and the conventions of your particular field of study. Examples of papers that discuss standardization include Gower (1985), Johnson and Wichern (1992), Everitt (1993), and van Tongeren (1995). In addition, Milligan and Cooper (1988) present an in-depth examination of standardization of variables when using Euclidean Distance as the dissimilarity metric.
Remember, if you choose to use the Steinhaus Coefficient of Similarity (recommended for count data, such as the number of trees of different species at sampled locations), this measure is self-normalizing and data should not be standardized.
0-1 scaling: each variable in the data set is recalculated as (V - min V)/(max V - min V), where V represents the value of the variable in the original data set. This method allows variables to have differing means and standard deviations but equal ranges. In this case, there is at least one observed value at the 0 and 1 endpoints.
Dividing each value by the range: recalculates each variable as V /(max V - min V). In this case, the means, variances, and ranges of the variables are still different, but at least the ranges are likely to be more similar.
Z-score scaling: variables recalculated as (V - mean of V)/s, where "s" is the standard deviation. As a result, all variables in the data set have equal means (0) and standard deviations (1) but different ranges.
Dividing each value by the standard deviation. This method produces a set of transformed variables with variances of 1, but different means and ranges.
Please note: when you standardize your data and save the data over the original data set, BoundarySeer will not update the maps, charts and tables referencing the data set in your project. Thus, if you query a map, it will show the pre-standardized information, which may be misleading. To view an updated map, chart, or table, delete the old one and create a new one using the standardized data set.
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