The MINMWMAX and MEDMW algorithms available here are mean-centered; they generate class boundaries on both sides of the arithmetic mean (of the similarity distribution to be visualized) using very simple statistical methods. In **MINMWMAX**, the ranges between the arithmetic mean and the minimum at the one hand, and between the maximum and the arithmetic mean at the other hand are divided by *n/2* (*n* = number of selected colors/intervals). In **MEDMW**, intervals or classes are formed on both sides of the arithmetic mean which are as large as possible equal with regard to their elements (polygons or inquiry points). MINMWMAX usually produces very balanced choropleth profiles and can therefore be recommended as a standard algorithm. MEDMW acts as a profile amplifier and produces more sharp-cut choropleth profiles, which however can be useful in certain cases of pattern recognition.